diff --git a/Rmd/BreastMammaryTissueJunctionAnalysis.Rmd b/Rmd/BreastMammaryTissueJunctionAnalysis.Rmd new file mode 100644 index 0000000..67d7a18 --- /dev/null +++ b/Rmd/BreastMammaryTissueJunctionAnalysis.Rmd @@ -0,0 +1,654 @@ +--- +jupyter: + jupytext: + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.4.1 + kernelspec: + display_name: R + language: R + name: ir +--- + +# BreastMammaryTissueJunctionAnalysis as a Notebook + +rMATS 3.2.5 was run on controlled access RNASeq files retrieved experiments stored in the Sequence Read Archive with controlled access managed by dbGaP. The data were generated under the Gene Tissue Expression. + +## rMATS RNASeq-MATS.py produces 10 different output types which get assembled into as type junction ID by sample ID matrices + +### Alternative Splice Site Types are: (se, a3ss, a5ss, mxe, ri) + + This is input as ARGV1 into variable 'astype' + + * Skipped Exon events (se), + * Alternative 3' splice site (a3ss), + * Alternative 5' splice site (a5ss), + * Mutually exclusive exon (mxe), + * and retention intron (ri) + +### There are two different kinds of junction counts + + * jc = junction counts - reads that cross the junction + * jcec = junction counts plus reads on the target (such as included exon + +### And the count type -- there are 5 types + + * inclusion levels (percent spliced in) + * included junction counts (ijc) + * skipped junction counts (sjc) + * inclusion length (inclen) + * skipped length (skiplen) + +### function: fit_iso_tissue + +fit_iso_tissue expects the following input: + + * the tissue of interest (SMSTD) + * an ordered_merged_rmats -- which will be ordered to fit the count matrix + * count matrix (inc or ijc & sjc merged) + * splice type (a3ss, a5ss, mxe, ri or se) + * junction_count type (jc or jcec) + * count type (inc or the merged ijc,sjc) + +### reordering to match annotations between count matrix and annotation matrix + +Common problem is to match specifically the rows of an annotation matrix with the columns of a count matrix +`match` is the function that gives the re-ordering index required to accomplish this + + + +## **NOTE**: + +We assume that you have cloned the analysis repository and have `cd` into the parent directory. Before starting with the analysis make sure you have first completed the dependencies set up by following the instructions described in the **`dependencies/README.md`** document. All paths defined in this Notebook are relative to the parent directory (repository). Please close this Notebook and start again by following the above guidelines if you have not completed the aforementioned steps. + +## rMATS-final-merged +the rmats-nf NextFlow was executed and the results released here: + + +## Loading dependencies + +```{r} +library(limma) +library(piggyback) +library(multtest) +library(Biobase) +library(edgeR) +library(tibble) +library(R.utils) +library(statmod) +``` + + +## Modeling + +This analysis uses edgeR. From the documentation, it is important to note that normalization takes the form of correction factors that enter into the statistical model. Such correction factors are usually computed internally by edgeR functions, but it is also possible for a user to supply them. The correction factors may take the form of scaling factors for the library sizes, such as computed by calcNormFactors, which are then used to compute the effective library sizes. + +Alternatively, gene-specific correction factors can be entered into the glm functions of edgeR as offsets. In the latter case, the offset matrix will be assumed to account for all normalization issues, including sequencing depth and RNA composition. + +Note that normalization in edgeR is model-based, and the original read counts are not themselves transformed. This means that users should not transform the read counts in any way before inputing them to edgeR. For example, users should not enter RPKM or FPKM val- ues to edgeR in place of read counts. Such quantities will prevent edgeR from correctly estimating the mean-variance relationship in the data, which is a crucial to the statistical strategies underlying edgeR. Similarly, users should not add artificial values to the counts before inputing them to edgeR. + +edgeR is not designed to work with estimated expression levels, for example as might be output by Cufflinks. +edgeR can work with expected counts as output by RSEM, but raw counts are still preferred. + +As instructed by the software, we are using the raw counts as provided by rMATS. The raw counts we are using in the model are `ijc` and `sjc`, the sample specific raw read counts as they align to the junctions of the `included exon (ijc)` and the junctions of the `excluded or skipped exon (sjc)` respectively. + + +Be sure to set your GITHUB_TOKEN, prior to downloading files + +One suggestion is change it to your token and then run it then immediately change it back to this: + +Sys.setenv(GITHUB_TOKEN = "your-very-own-github-token") + + +### Did you remember? +Did you remember to delete your private github token? Now is a good time to do so, before you save your work and checkit in inadvertantly.... + +```{r} +piggyback::pb_download( + repo = "TheJacksonLaboratory/sbas", + file = "SraRunTable.noCram.noExome.noWGS.totalRNA.txt.gz", + tag = "GTExV8.v1.0", + dest = "../data/") + +piggyback::pb_download( + repo = "adeslatt/sbas_test", + file = "rmats_final.se.jc.ijc.txt.gz", + tag = "rMATS.3.2.5.GTEx.V8.final_matrices", + dest = "../data/") + +piggyback::pb_download( + repo = "adeslatt/sbas_test", + file = "rmats_final.se.jc.sjc.txt.gz", + tag = "rMATS.3.2.5.GTEx.V8.final_matrices", + dest = "../data/") + +ijc.iso.counts.mem <- data.table::fread("../data/rmats_final.se.jc.ijc.txt.gz") +sjc.iso.counts.mem <- data.table::fread("../data/rmats_final.se.jc.sjc.txt.gz") +meta.data <- data.table::fread("../data/SraRunTable.noCram.noExome.noWGS.totalRNA.txt.gz") + +head(ijc.iso.counts.mem) +head(sjc.iso.counts.mem) +head(meta.data) + +#dimensions before we make the changes. +dim(ijc.iso.counts.mem) +dim(sjc.iso.counts.mem) +dim(meta.data) +``` + +## Synchronize metadata samples with ijc sjc samples + +Keep only the runs that are in the ijc count list (assuming ijc and sjc are the same). As well, name the rows with the junction id column and then make the matrix just about the counts. + +```{r} +# the sample names are in the columns of both the ijc and the sjc matrices, these matrices have the identical column order) +keep.meta.data <- meta.data$Run %in% colnames(ijc.iso.counts.mem) +table(keep.meta.data) +reduced.meta.data <- meta.data[keep.meta.data==TRUE,] + +# preserve junction id as rowname +rownames(ijc.iso.counts.mem) <- ijc.iso.counts.mem$ID +rownames(sjc.iso.counts.mem) <- sjc.iso.counts.mem$ID + +# and remove the id to have a data matrix +ijc.iso.counts.mem <- ijc.iso.counts.mem[,-1] +sjc.iso.counts.mem <- sjc.iso.counts.mem[,-1] + +meta.data.run.names <- reduced.meta.data$Run +ijc.iso.counts.mem2 <- as_tibble(ijc.iso.counts.mem) +sjc.iso.counts.mem2 <- as_tibble(sjc.iso.counts.mem) + +ijc.iso.counts.mem2 <- ijc.iso.counts.mem2[,c(meta.data.run.names)] +sjc.iso.counts.mem2 <- sjc.iso.counts.mem2[,c(meta.data.run.names)] + +dim(ijc.iso.counts.mem) +dim(sjc.iso.counts.mem) +dim(reduced.meta.data) +``` + +## Order ijc and sjc columns in the same order as the metadata Run order + +Using tibble library, we can rearrange the columns as the column name. + +```{r} +meta.data.run.names <- as.character(reduced.meta.data$Run) +ijc.iso.counts.mem2 <- as_tibble(ijc.iso.counts.mem) +sjc.iso.counts.mem2 <- as_tibble(sjc.iso.counts.mem) + +ijc.iso.counts.mem2 <- ijc.iso.counts.mem2[,c(meta.data.run.names)] +sjc.iso.counts.mem2 <- sjc.iso.counts.mem2[,c(meta.data.run.names)] + +dim(ijc.iso.counts.mem2) +dim(sjc.iso.counts.mem2) +dim(reduced.meta.data) +``` + +Remove samples that match '11IL0' from the ijc, sjc and metadata files using the logical grep, grepl + +```{r} +keep.meta.data <- (!grepl('11ILO',reduced.meta.data$"Sample Name")) +table(keep.meta.data) +ijc.iso.counts.mem2 <-ijc.iso.counts.mem2 [ ,keep.meta.data==TRUE] +sjc.iso.counts.mem2 <-sjc.iso.counts.mem2 [ ,keep.meta.data==TRUE] + +reduced.meta.data <-reduced.meta.data [keep.meta.data==TRUE, ] +dim(ijc.iso.counts.mem2) +dim(sjc.iso.counts.mem2) +``` + +### and focus on a single tissue + +this will become a function so we can proceed on all the tissues + +```{r} +tissue <- reduced.meta.data$body_site %in% 'Breast - Mammary Tissue' +table(tissue) + +ijc.iso.counts.mem2 <-ijc.iso.counts.mem2 [ ,tissue==TRUE] +sjc.iso.counts.mem2 <-sjc.iso.counts.mem2 [ ,tissue==TRUE] + +reduced.meta.data <-reduced.meta.data [tissue==TRUE, ] +dim(ijc.iso.counts.mem2) +dim(sjc.iso.counts.mem2) +``` + +### exploration of the details + +For each sample, we have ijc and sjc count data and demographics of gender. +Our question is regarding the sex biased differences. +For each junction we have 8,000 samples with these count data. The way to think about the model is that we have in fact for all of these junctions, these are our co-variates in this global transcriptomic model. +For exon skipping events (SE), we have 42,611 non-zero junction IDs the (first dimension of the ijc and sjc cout table) for the skipped exon event for breast-Mammary Tissue, 191 individuals. These are healthy individuals, and we are studying the impact of sex on the occurrence or non-occurance of specific alternative splicing events. We explore the information we ahve about these junctions and create a construct, as_event, which accounts for the junction under exploration. + +```{r} +ijc <- as.data.frame(ijc.iso.counts.mem2) +sjc <- as.data.frame(sjc.iso.counts.mem2) +ijc <- data.matrix(ijc) +sjc <- data.matrix(sjc) +``` + +## Exploring the ijc and sjc Count data + +We have two counts that are in many ways two sides of the same coin. Both our the observational output and we wish to see how robust each are in their ability to separate out the samples to provide for us differentially expressed isoform events as measured by their counts. Each junction is in a manner a specific marker to specific isoform events that may or may not be shared between the genders. If there is significant results, then this is indicative of the separation achieved by isoform specific differentiation. In our model we will use these in combination, it is important to see if they will yield the results we are looking for. + +### IJC + +Exon included junction counts -- duplicate correlation is actually 2 (there are 2 exons for each included exon) the results provide robust separation up until around 550. + +```{r} +sex <- factor(reduced.meta.data$sex,levels=c('male','female')) + +design <- model.matrix ( ~ sex) +y <- DGEList(counts=ijc, group = sex) +y <- calcNormFactors(y, method="upperquartile") +y_voom <- voom (y, design=design) +Gender <- substring(sex,1,1) +plotMDS(y, labels=Gender, top=500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_voom, labels=Gender, top=500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y, labels=Gender, top=1500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_voom, labels=Gender, top=1500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +``` + +### SJC +sjc counts are skipped exon junction counts -- ijc counts hold together for the top 1000, the skipped exon junction counts fall appart +at a lower number -- around 200! separation fails between the genders at 250 + +```{r} +sex <- factor(reduced.meta.data$sex,levels=c('male','female')) +design <- model.matrix(~ sex) +y <- DGEList(counts=sjc, group = sex) +y <- calcNormFactors(y, method="upperquartile") +y_voom <- voom (y, design=design) + +Gender <- substring(sex,1,1) +plotMDS(y, labels=Gender, top=100, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_voom, labels=Gender, top=100, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y, labels=Gender, top=700, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_voom, labels=Gender, top=700, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +``` + +## Differential analysis as_event:ijc + +Differential Analysis (DE) was performed using voom (Law et.al., 2014) to transform junction counts (reads that were aligned to junctions when an exon is included - ijc, and reads that were aligned to junctions when the exon is excluded - sjc) with associated precision weights, followed by linear modeling and empirical Bayes procedure using limma. In each tissue, the following linear regression model was used to detec secually dimorphic alternative splicing event expression: + + y = B0 + B1 sex + epsilon (error) + + +where y is the included exon junction count expression; sex denotes the reported sex of the subject. + +```{r} +sex <- reduced.meta.data$sex + +design <- model.matrix( ~ sex ) + +colnames(design) <- c("intercept","sex") + +dim(ijc) +table(sex) +head(design) + +y_ijc <- DGEList(counts=ijc, group = sex) +y_ijc <- calcNormFactors(y_ijc, method="upperquartile") + +y_ijc_voom <- voom (y_ijc, design=design) + +Gender <- substring(sex,1,1) + +plotMDS(y_ijc, labels=Gender, top=1000, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") + +plotMDS(y_ijc_voom, labels=Gender, top=1000, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") + +fit_ijc <- lmFit(y_ijc_voom, design) +fit_ijc <- eBayes(fit_ijc) + +ijc_sex_results <- topTable(fit_ijc, coef='sex', number=nrow(y_voom)) +ijc_sex_results_refined <- ijc_sex_results$adj.P.Val < 0.05 & abs(ijc_sex_results$logFC) > 1.5 + +table(ijc_sex_results_refined) +``` + +## Differential analysis as_event:sjc + +Differential Analysis (DE) was performed using voom (Law et.al., 2014) to transform junction counts (reads that were aligned to junctions when an exon is included - ijc, and reads that were aligned to junctions when the exon is excluded - sjc) with associated precision weights, followed by linear modeling and empirical Bayes procedure using limma. In each tissue, the following linear regression model was used to detec secually dimorphic alternative splicing event expression: + + y = B0 + B1 sex + epsilon (error) + + +where y is the excluded exon junction count (sjc) expression; sex denotes the reported sex of the subject. + +```{r} +sex <- reduced.meta.data$sex + +design <- model.matrix( ~ sex ) + +colnames(design) <- c("intercept","sex") + +dim(sjc) +table(sex) +head(design) + +y_sjc <- DGEList(counts=sjc, group = sex) +y_sjc <- calcNormFactors(y_sjc, method="upperquartile") + +y_sjc_voom <- voom (y_sjc, design=design) + +Gender <- substring(sex,1,1) + +plotMDS(y_sjc, labels=Gender, top=500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") + +plotMDS(y_sjc_voom, labels=Gender, top=500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") + +fit_sjc <- lmFit(y_sjc_voom, design) +fit_sjc <- eBayes(fit_sjc) + +sjc_sex_results <- topTable(fit_sjc, coef='sex', number=nrow(y_voom)) +sjc_sex_results_refined <- sjc_sex_results$adj.P.Val < 0.05 & abs(sjc_sex_results$logFC) > 1.5 + +table(sjc_sex_results_refined) +``` + +## Differential analysis as_event (combined ijc and sjc) + +Differential Analysis (DE) was performed using voom (Law et.al., 2014) to transform junction counts (reads that were aligned to junctions when an exon is included - ijc, and reads that were aligned to junctions when the exon is excluded - sjc) with associated precision weights, followed by linear modeling and empirical Bayes procedure using limma. In each tissue, the following linear regression model was used to detec secually dimorphic alternative splicing event expression: + + y = B0 + B1 sex + B2 as_event + B3 sex*as_event + epsilon (error) + + +where y is the alternative splicing event expression; sex denotes the reported sex of the subject, as_event represents the specific alternative splicing event - either included exon junction counts or skipped exon junction counts and their interaction terms. Donor is added to our model as a blocking variable used in both the calculation of duplicate correlation as well as in the linear fit. + +```{r} +ijc_names <- as.character(colnames(ijc)) +sjc_names <- as.character(colnames(sjc)) +sample_names <- as.character(colnames(ijc)) + +ijc_names <- paste0(ijc_names,"-ijc") +sjc_names <- paste0(sjc_names,"-sjc") +colnames(ijc) <- ijc_names +colnames(sjc) <- sjc_names + +as_matrix <- cbind(ijc,sjc) +sex <- c(rep(reduced.meta.data$sex,2)) +sex <- factor(sex, levels=c('male','female')) +as_event <- c(rep("ijc",dim(ijc)[2]), rep("sjc", dim(sjc)[2])) +as_event <- factor(as_event, levels=c("ijc", "sjc")) + +# we will add donor as a blocking parameter +donor <- rep(sample_names, 2) + +design <- model.matrix( ~ sex + as_event + sex*as_event ) + +colnames(design) <- c("intercept","sex","as_event","sex*as_event") + +dim(as_matrix) +table(sex) +table(as_event) +head(design) +``` + +### Voom, limma's lmFit and eBayes + +Using sample as a blocking variable, we are able to model the effects of the donor on the results, which improves the power. This topic is discussed in biostars https://www.biostars.org/p/54565/. And Gordon Smyth answers the question here https://mailman.stat.ethz.ch/pipermail/bioconductor/2014-February/057887.html. The method of modeling is a random effects approach in which the intra-donor correlation is incorporated into the covariance matrix instead of the linear predictor. And though as Gordon Smyth states both are good method and the twoway anova approach makes fewer assumptions, the random effects approach is statistically more powerful. + +We have a balanced design in which all donors receive all stimuli (which is really in healthy human donors, life and all of its factors!) Our measurement has so many points -- we are measuring in the skipped exon approach, 42,611 junctions! It is not possible to encorporate those measurements into the linear predictor. A two-way ANOVA approach is virtually as powerful as the random effects approach +and hence is preferable as it makes fewer assumptions. + +For an unbalanced design in which each donor receives only a subset of the stimula, the random effects approach is more powerful. + +Random effects approach is equivalent to The first method is twoway anova, a generalization of a paired analysis. + + +```{r} +# we will model as random effects, represented as a block +donor <- rep(sample_names, 2) +length(donor) +``` + +```{r} +y <- DGEList(counts=as_matrix, group = sex) +y <- calcNormFactors(y, method="upperquartile") +y_voom <- voom (y, design=design) + +dup_cor <- duplicateCorrelation(y_voom$E, design=design, ndups=2, block=donor, weights=y$samples$norm.factors) +dup_cor$consensus.correlation +``` + +```{r} +y_dup_voom <- voom (y, design=design, plot = TRUE, block = donor, correlation = dup_cor$consensus.correlation) +``` + +```{r} +Gender <- substring(sex[1:dim(ijc)[2]],1,1) + +plotMDS(y[,c(1:dim(ijc)[2])], labels=Gender, top=500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_voom[,c(1:dim(ijc)[2])], labels=Gender, top=500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_dup_voom[,c(1:dim(ijc)[2])], labels=Gender, top=500, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y[,c((dim(ijc)[2]+1)):(dim(ijc)[2]+dim(sjc)[2])], labels=Gender, top=250, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_voom[,c((dim(ijc)[2]+1)):(dim(ijc)[2]+dim(sjc)[2])], labels=Gender, top=250, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +plotMDS(y_dup_voom[,c((dim(ijc)[2]+1):(dim(ijc)[2]+dim(sjc)[2]))], labels=Gender, top=250, col=ifelse(Gender=="m","blue","red"), + gene.selection="common") +``` + +```{r} +fit <- lmFit(y_dup_voom, design=design, block=donor, correlation = dup_cor$consensus.correlation) +fit <- eBayes(fit, robust=TRUE) +``` + +```{r} +sex_as_events_results <- topTable(fit, coef="sex*as_event", number=nrow(y_voom)) +sex_as_events_results_refined <- sex_as_events_results$adj.P.Val < 0.05 & abs(sex_as_events_results$logFC) > 1.5 + +sex_results <- topTable(fit, coef="sex", number=nrow(y_voom)) +sex_results_refined <- sex_results$adj.P.Val < 0.05 & abs(sex_results$logFC) > 1.5 + +table(sex_as_events_results_refined) +table(sex_results_refined) +``` + +#### fromGTF + +rMATS analyzes the gtf file and determines the junctions, identifying 10 matrices in all. The fromGTF.SE.txt contains the annotation information and we can use this information to estimate the duplicate correlation effect from counting based upon the same gene with our ijc counts. we will use this information, to obtain the gene information. + + +```{r} +# fromGTF.tar.gz +if (! (file.exists("../data/fromGTF.tar.gz"))) { + system("mkdir -p ../data", intern = TRUE) + message("Fetching fromGTF.tar.gz from GitHub ..") + # Download archive from GitHub release with tag "dge" + piggyback::pb_download(file = "fromGTF.tar.gz", + dest = "../data", + repo = "adeslatt/sbas_gtf", + tag = "rMATS.3.2.5.gencode.v30", + show_progress = TRUE) + message("Done!\n") + message("Decompressing fromGTF.tar.gz into ../data") + system("mkdir -p ../data && tar xvfz ../data/fromGTF.tar.gz -C ../data", intern = TRUE) + message("Done!\n") + message("Decompressing fromGTF.*.txt.gz into ../data") + system("gunzip ../data/fromGTF*.txt.gz ", intern = TRUE) + message("Done!\n") +} +fromGTF.SE <- read.table("../data/fromGTF.SE.txt", header=TRUE) +head(fromGTF.SE) +genes <- factor(fromGTF.SE$geneSymbol) +length(levels(genes)) +``` + +```{r} +head(sex_as_events_results) +``` + +```{r} +# index to the annotations - these are the Junction IDs +sex_as_events_rnResults <- rownames(sex_as_events_results) +sex_rnResults <- rownames(sex_results) +ijc_sex_rnResults <- rownames(ijc_sex_results) +sjc_sex_rnResults <- rownames(sjc_sex_results) +head(sex_as_events_rnResults) +head(ijc_sex_rnResults) +head(sjc_sex_rnResults) +head(sex_rnResults) +head(fromGTF.SE[sex_as_events_rnResults,]) +``` + +```{r} +# use the junctionIDs to get the annotations +sex_as_events_resultsAnnotations <- fromGTF.SE[sex_as_events_rnResults,] +sex_resultsAnnotations <- fromGTF.SE[sex_rnResults,] +ijc_sex_resultsAnnotations <- fromGTF.SE[ijc_sex_rnResults,] +sjc_sex_resultsAnnotations <- fromGTF.SE[sjc_sex_rnResults,] +head(sex_as_events_resultsAnnotations) +head(sex_resultsAnnotations) +head(ijc_sex_resultsAnnotations) +head(sjc_sex_resultsAnnotations) +``` + +```{r} +sex_as_events_results_refinedAnnotations<- sex_as_events_resultsAnnotations[sex_as_events_results_refined==TRUE,] +sex_results_refinedAnnotations <- sex_resultsAnnotations [sex_results_refined ==TRUE,] +ijc_sex_results_refinedAnnotations <- ijc_sex_resultsAnnotations [ijc_sex_results_refined ==TRUE,] +sjc_sex_results_refinedAnnotations <- sjc_sex_resultsAnnotations [sjc_sex_results_refined ==TRUE,] +head(sex_as_events_results_refinedAnnotations) +head(sex_results_refinedAnnotations) +head(ijc_sex_results_refinedAnnotations) +head(sjc_sex_results_refinedAnnotations) +``` + +```{r} + +# geneSymbols are in the annotations +sex_as_events_geneSymbols <- sex_as_events_resultsAnnotations$geneSymbol +sex_geneSymbols <- sex_resultsAnnotations$geneSymbol +ijc_sex_geneSymbols <- ijc_sex_resultsAnnotations$geneSymbol +sjc_sex_geneSymbols <- sjc_sex_resultsAnnotations$geneSymbol + +sex_as_events_refined_geneSymbols <- sex_as_events_results_refinedAnnotations$geneSymbol +sex_refined_geneSymbols <- sex_results_refinedAnnotations$geneSymbol +ijc_sex_refined_geneSymbols <- ijc_sex_results_refinedAnnotations$geneSymbol +sjc_sex_refined_geneSymbols <- sjc_sex_results_refinedAnnotations$geneSymbol + +# adjust the rownames to be the geneSymbols rather than junction IDs +sex_as_events_results_rn <- paste(sex_as_events_geneSymbols, sex_as_events_rnResults, sep="-") +sex_results_rn <- paste(sex_geneSymbols, sex_rnResults, sep="-") +ijc_sex_results_rn <- paste(ijc_sex_geneSymbols, ijc_sex_rnResults, sep="-") +sjc_sex_results_rn <- paste(sjc_sex_geneSymbols, sjc_sex_rnResults, sep="-") + +head(sex_as_events_results_rn) +head(sex_results_rn) +head(ijc_sex_results_rn) +head(sjc_sex_results_rn) + +``` + +```{r} +rownames(sex_as_events_results) <- sex_as_events_results_rn +rownames(sex_results) <- sex_results_rn +rownames(ijc_sex_results) <- ijc_sex_results_rn +rownames(sjc_sex_results) <- sjc_sex_results_rn +``` + +```{r} +sex_as_events_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_sex_as_events.csv',sep='') +sex_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_sex.csv',sep='') +ijc_sex_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_ijc_sex.csv',sep='') +sjc_sex_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_sjc_sex.csv',sep='') + +sex_as_events_refined_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_sex_as_events_refined.csv',sep='') +sex_refined_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_sex_refined.csv',sep='') +ijc_sex_refined_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_ijc_sex_refined.csv',sep='') +sjc_sex_refined_filename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_DGE_sjc_sex_refined.csv',sep='') + +sex_as_events_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_sex_as_events_universe.txt',sep='') +sex_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_sex_universe.txt',sep='') +ijc_sex_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_ijc_sex_universe.txt',sep='') +sjc_sex_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_sjc_sex_universe.txt',sep='') + +sex_as_events_refined_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_sex_as_events_gene_set.txt',sep='') +sex_refined_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_sex_gene_set.txt',sep='') +ijc_sex_refined_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_ijc_sex_gene_set.txt',sep='') +sjc_sex_refined_genesFilename = paste(paste('../data/BreastMammaryTissue',collapse='.'),'_sjc_sex_gene_set.txt',sep='') + +``` + +```{r} +write.table(sex_as_events_results, file = sex_as_events_filename, row.names = T, col.names = T, quote = F, sep = ",") +write.table(sex_results, file = sex_filename , row.names = T, col.names = T, quote = F, sep = ",") +write.table(ijc_sex_results, file = ijc_sex_filename , row.names = T, col.names = T, quote = F, sep = ",") +write.table(sjc_sex_results, file = sjc_sex_filename , row.names = T, col.names = T, quote = F, sep = ",") + +write.table(sex_as_events_results[sex_as_events_results_refined,], file = sex_as_events_refined_filename, row.names = T, col.names = T, quote = F, sep = ",") +write.table(sex_results [sex_results_refined ,], file = sex_refined_filename , row.names = T, col.names = T, quote = F, sep = ",") +write.table(ijc_sex_results [ijc_sex_results_refined ,], file = ijc_sex_refined_filename , row.names = T, col.names = T, quote = F, sep = ",") +write.table(sjc_sex_results [sjc_sex_results_refined ,], file = sjc_sex_refined_filename , row.names = T, col.names = T, quote = F, sep = ",") + +write.table(sex_as_events_geneSymbols, file = sex_as_events_genesFilename, row.names = F, col.names = F, quote = F, sep = ",") +write.table(sex_geneSymbols, file = sex_genesFilename , row.names = F, col.names = F, quote = F, sep = ",") +write.table(ijc_sex_geneSymbols, file = ijc_sex_genesFilename , row.names = F, col.names = F, quote = F, sep = ",") +write.table(sjc_sex_geneSymbols, file = sjc_sex_genesFilename , row.names = F, col.names = F, quote = F, sep = ",") + +write.table(sex_as_events_refined_geneSymbols,file = sex_as_events_refined_genesFilename, row.names = F, col.names = F, quote = F, sep = ",") +write.table(sex_refined_geneSymbols, file = sex_refined_genesFilename , row.names = F, col.names = F, quote = F, sep = ",") +write.table(ijc_sex_refined_geneSymbols, file = ijc_sex_refined_genesFilename , row.names = F, col.names = F, quote = F, sep = ",") +write.table(sjc_sex_refined_geneSymbols, file = sjc_sex_refined_genesFilename , row.names = F, col.names = F, quote = F, sep = ",") + +``` + +## Metadata + +For replicability and reproducibility purposes, we also print the following metadata: + +1. Checksums of **'artefacts'**, files generated during the analysis and stored in the folder directory **`data`** +2. List of environment metadata, dependencies, versions of libraries using `utils::sessionInfo()` and [`devtools::session_info()`](https://devtools.r-lib.org/reference/session_info.html) + + +### 1. Checksums with the sha256 algorithm + +```{r} +notebookid = "BreastMammaryTissueJunctionAnalysis" + +message("Generating sha256 checksums of the artefacts in the `..data/` directory .. ") +system(paste0("cd ../data && find . -type f -exec sha256sum {} \\; > ../metadata/", notebookid, "_sha256sums.txt"), intern = TRUE) +message("Done!\n") + +data.table::fread(paste0("../metadata/", notebookid, "_sha256sums.txt"), header = FALSE, col.names = c("sha256sum", "file")) +``` + +### 2. Libraries metadata + +```{r} +dev_session_info <- devtools::session_info() +utils_session_info <- utils::sessionInfo() + +message("Saving `devtools::session_info()` objects in ../metadata/devtools_session_info.rds ..") +saveRDS(dev_session_info, file = paste0("../metadata/", notebookid, "_devtools_session_info.rds")) +message("Done!\n") + +message("Saving `utils::sessionInfo()` objects in ../metadata/utils_session_info.rds ..") +saveRDS(utils_session_info, file = paste0("../metadata/", notebookid ,"_utils_info.rds")) +message("Done!\n") + +dev_session_info$platform +dev_session_info$packages[dev_session_info$packages$attached==TRUE, ] +``` + +```{r} + +``` diff --git a/jupyter/BreastMammaryTissueJunctionAnalysis.ipynb b/jupyter/BreastMammaryTissueJunctionAnalysis.ipynb index 5b3a62e..1379d0f 100644 --- a/jupyter/BreastMammaryTissueJunctionAnalysis.ipynb +++ b/jupyter/BreastMammaryTissueJunctionAnalysis.ipynb @@ -71,93 +71,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading required package: BiocGenerics\n", - "Loading required package: parallel\n", - "\n", - "Attaching package: ‘BiocGenerics’\n", - "\n", - "The following objects are masked from ‘package:parallel’:\n", - "\n", - " clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n", - " clusterExport, clusterMap, parApply, parCapply, parLapply,\n", - " parLapplyLB, parRapply, parSapply, parSapplyLB\n", - "\n", - "The following object is masked from ‘package:limma’:\n", - "\n", - " plotMA\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " IQR, mad, sd, var, xtabs\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n", - " dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n", - " grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n", - " order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n", - " rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n", - " union, unique, unsplit, which, which.max, which.min\n", - "\n", - "Loading required package: Biobase\n", - "Welcome to Bioconductor\n", - "\n", - " Vignettes contain introductory material; view with\n", - " 'browseVignettes()'. To cite Bioconductor, see\n", - " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", - "\n", - "Updating HTML index of packages in '.Library'\n", - "Making 'packages.html' ... done\n", - "Loading required package: R.oo\n", - "Loading required package: R.methodsS3\n", - "R.methodsS3 v1.8.0 (2020-02-14 07:10:20 UTC) successfully loaded. See ?R.methodsS3 for help.\n", - "R.oo v1.23.0 successfully loaded. See ?R.oo for help.\n", - "\n", - "Attaching package: ‘R.oo’\n", - "\n", - "The following object is masked from ‘package:R.methodsS3’:\n", - "\n", - " throw\n", - "\n", - "The following objects are masked from ‘package:methods’:\n", - "\n", - " getClasses, getMethods\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " attach, detach, load, save\n", - "\n", - "R.utils v2.9.2 successfully loaded. See ?R.utils for help.\n", - "\n", - "Attaching package: ‘R.utils’\n", - "\n", - "The following object is masked from ‘package:utils’:\n", - "\n", - " timestamp\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,\n", - " warnings\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "library(limma)\n", + "library(piggyback)\n", "library(multtest)\n", "library(Biobase)\n", "library(edgeR)\n", "library(tibble)\n", - "install.packages('R.utils')\n", - "library(R.utils)" + "library(R.utils)\n", + "library(statmod)" ] }, { @@ -176,14 +101,73 @@ "edgeR can work with expected counts as output by RSEM, but raw counts are still preferred. \n", "\n", "As instructed by the software, we are using the raw counts as provided by rMATS. The raw counts we are using in the model are `ijc` and `sjc`, the sample specific raw read counts as they align to the junctions of the `included exon (ijc)` and the junctions of the `excluded or skipped exon (sjc)` respectively.\n", - "\n" + "\n", + "\n", + "Be sure to set your GITHUB_TOKEN, prior to downloading files\n", + "\n", + "One suggestion is change it to your token and then run it then immediately change it back to this:\n", + "\n", + "Sys.setenv(GITHUB_TOKEN = \"your-very-own-github-token\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Did you remember?\n", + "Did you remember to delete your private github token? Now is a good time to do so, before you save your work and checkit in inadvertantly...." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "downloading SraRunTable.noCram.noExome.noWGS.totalRNA.txt.gz ...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " |======================================================================| 100%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "downloading rmats_final.se.jc.ijc.txt.gz ...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " |======================================================================| 100%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "downloading rmats_final.se.jc.sjc.txt.gz ...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " |======================================================================| 100%\n" + ] + }, { "data": { "text/html": [ @@ -336,125 +320,51 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A data.table: 6 × 8674A data.table: 6 × 79
IDSRR1068788SRR1068808SRR1068832SRR1068855SRR1068880SRR1068929SRR1068953SRR1068977SRR1068999⋯SRR821573SRR821581SRR821602SRR821626SRR821653SRR821690SRR821715SRR823967SRR823991SRR824015
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Runanalyte_typeAssay TypeAvgSpotLenBasesBioProjectBioSamplebiospecimen_repositorybiospecimen_repository_sample_idbody_site⋯data_type (run)product_part_number (exp)product_part_number (run)sample_barcode (exp)sample_barcode (run)is_technical_controltarget_set (exp)primary_disease (exp)secondary_accessions (run)Alignment_Provider (run)
<chr><chr><chr><int><int64><chr><chr><chr><chr><chr>⋯<chr><chr><chr><dbl><dbl><chr><lgl><chr><chr><lgl>
10.00000.00000.0000.00000.07690.00000.000000.0000⋯0.00000.00000.00.000000.0000000
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SRR2911715RNARNA-Seq1503852895500PRJNA244100SAMN04216864Cloud TestingHG00103Lymphoblastoid cell line⋯NANANANA
SRR2911716RNARNA-Seq1504885577400PRJNA244100SAMN04216866Cloud TestingHG00154Lymphoblastoid cell line⋯NANANANA
SRR2911718RNARNA-Seq1502690545500PRJNA244100SAMN04216863Cloud TestingNA18910Lymphoblastoid cell line⋯NANANANA
SRR2911719RNARNA-Seq1502699599350PRJNA244100SAMN04216865Cloud TestingNA19200Lymphoblastoid cell line⋯NANANANA
SRR2911720RNARNA-Seq1524300467752PRJNA244100SAMN04216865Cloud TestingNA19200Lymphoblastoid cell line⋯NANANANA
SRR2911717RNARNA-Seq1502666546700PRJNA244100SAMN04216863Cloud TestingNA18910Lymphoblastoid cell line⋯NANANANA
\n" ], "text/latex": [ - "A data.table: 6 × 8674\n", + "A data.table: 6 × 79\n", "\\begin{tabular}{lllllllllllllllllllll}\n", - " ID & SRR1068788 & SRR1068808 & SRR1068832 & SRR1068855 & SRR1068880 & SRR1068929 & SRR1068953 & SRR1068977 & SRR1068999 & ⋯ & SRR821573 & SRR821581 & SRR821602 & SRR821626 & SRR821653 & SRR821690 & SRR821715 & SRR823967 & SRR823991 & SRR824015\\\\\n", - " & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", + " Run & analyte\\_type & Assay Type & AvgSpotLen & Bases & BioProject & BioSample & biospecimen\\_repository & biospecimen\\_repository\\_sample\\_id & body\\_site & ⋯ & data\\_type (run) & product\\_part\\_number (exp) & product\\_part\\_number (run) & sample\\_barcode (exp) & sample\\_barcode (run) & is\\_technical\\_control & target\\_set (exp) & primary\\_disease (exp) & secondary\\_accessions (run) & Alignment\\_Provider (run)\\\\\n", + " & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", "\\hline\n", - "\t 1 & 0.0000 & 0.0000 & 0.000 & 0.0000 & 0.0769 & 0.0000 & 0.0000 & 0 & 0.0000 & ⋯ & 0.0000 & 0.000 & 0 & 0.0 & 0.0000 & 0 & 0.0000 & 0 & 0 & 0\\\\\n", - "\t 2 & 1.0000 & 1.0000 & 1.000 & 0.9972 & 1.0000 & 1.0000 & 0.9885 & 1 & 1.0000 & ⋯ & 1.0000 & 1.000 & 1 & 1.0 & 1.0000 & 1 & 1.0000 & 1 & 1 & 1\\\\\n", - "\t 3 & 1.0000 & 0.0000 & 1.000 & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 1 & 1.0000 & ⋯ & 1.0000 & 1.000 & 0 & 1.0 & 1.0000 & 0 & 1.0000 & 0 & 0 & 0\\\\\n", - "\t 4 & 0.0000 & 1.0000 & 1.000 & 0.5000 & 0.0000 & 0.0000 & 1.0000 & 0 & 0.5000 & ⋯ & 0.0000 & 0.000 & 0 & 0.0 & 0.2000 & 0 & 0.0000 & 0 & 0 & 0\\\\\n", - "\t 5 & 1.0000 & 0.0000 & 0.250 & 0.1578 & 0.4285 & 1.0000 & 0.1428 & 1 & 0.4545 & ⋯ & 0.1428 & 0.250 & 0 & 0.2 & 0.5000 & 0 & 0.5000 & 0 & 0 & 0\\\\\n", - "\t 6 & 0.0833 & 0.0041 & 0.027 & 0.0087 & 0.0142 & 0.0082 & 0.0078 & 0 & 0.0084 & ⋯ & 0.0095 & 0.037 & 0 & 0.0 & 0.0058 & 0 & 0.0078 & 0 & 0 & 0\\\\\n", + "\t SRR2911715 & RNA & RNA-Seq & 150 & 3852895500 & PRJNA244100 & SAMN04216864 & Cloud Testing & HG00103 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", + "\t SRR2911716 & RNA & RNA-Seq & 150 & 4885577400 & PRJNA244100 & SAMN04216866 & Cloud Testing & HG00154 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", + "\t SRR2911718 & RNA & RNA-Seq & 150 & 2690545500 & PRJNA244100 & SAMN04216863 & Cloud Testing & NA18910 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", + "\t SRR2911719 & RNA & RNA-Seq & 150 & 2699599350 & PRJNA244100 & SAMN04216865 & Cloud Testing & NA19200 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", + "\t SRR2911720 & RNA & RNA-Seq & 152 & 4300467752 & PRJNA244100 & SAMN04216865 & Cloud Testing & NA19200 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", + "\t SRR2911717 & RNA & RNA-Seq & 150 & 2666546700 & PRJNA244100 & SAMN04216863 & Cloud Testing & NA18910 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A data.table: 6 × 8674\n", + "A data.table: 6 × 79\n", "\n", - "| ID <int> | SRR1068788 <dbl> | SRR1068808 <dbl> | SRR1068832 <dbl> | SRR1068855 <dbl> | SRR1068880 <dbl> | SRR1068929 <dbl> | SRR1068953 <dbl> | SRR1068977 <dbl> | SRR1068999 <dbl> | ⋯ ⋯ | SRR821573 <dbl> | SRR821581 <dbl> | SRR821602 <dbl> | SRR821626 <dbl> | SRR821653 <dbl> | SRR821690 <dbl> | SRR821715 <dbl> | SRR823967 <dbl> | SRR823991 <dbl> | SRR824015 <dbl> |\n", + "| Run <chr> | analyte_type <chr> | Assay Type <chr> | AvgSpotLen <int> | Bases <int64> | BioProject <chr> | BioSample <chr> | biospecimen_repository <chr> | biospecimen_repository_sample_id <chr> | body_site <chr> | ⋯ ⋯ | data_type (run) <chr> | product_part_number (exp) <chr> | product_part_number (run) <chr> | sample_barcode (exp) <dbl> | sample_barcode (run) <dbl> | is_technical_control <chr> | target_set (exp) <lgl> | primary_disease (exp) <chr> | secondary_accessions (run) <chr> | Alignment_Provider (run) <lgl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | 0.0000 | 0.0000 | 0.000 | 0.0000 | 0.0769 | 0.0000 | 0.0000 | 0 | 0.0000 | ⋯ | 0.0000 | 0.000 | 0 | 0.0 | 0.0000 | 0 | 0.0000 | 0 | 0 | 0 |\n", - "| 2 | 1.0000 | 1.0000 | 1.000 | 0.9972 | 1.0000 | 1.0000 | 0.9885 | 1 | 1.0000 | ⋯ | 1.0000 | 1.000 | 1 | 1.0 | 1.0000 | 1 | 1.0000 | 1 | 1 | 1 |\n", - "| 3 | 1.0000 | 0.0000 | 1.000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 1 | 1.0000 | ⋯ | 1.0000 | 1.000 | 0 | 1.0 | 1.0000 | 0 | 1.0000 | 0 | 0 | 0 |\n", - "| 4 | 0.0000 | 1.0000 | 1.000 | 0.5000 | 0.0000 | 0.0000 | 1.0000 | 0 | 0.5000 | ⋯ | 0.0000 | 0.000 | 0 | 0.0 | 0.2000 | 0 | 0.0000 | 0 | 0 | 0 |\n", - "| 5 | 1.0000 | 0.0000 | 0.250 | 0.1578 | 0.4285 | 1.0000 | 0.1428 | 1 | 0.4545 | ⋯ | 0.1428 | 0.250 | 0 | 0.2 | 0.5000 | 0 | 0.5000 | 0 | 0 | 0 |\n", - "| 6 | 0.0833 | 0.0041 | 0.027 | 0.0087 | 0.0142 | 0.0082 | 0.0078 | 0 | 0.0084 | ⋯ | 0.0095 | 0.037 | 0 | 0.0 | 0.0058 | 0 | 0.0078 | 0 | 0 | 0 |\n", - "\n" - ], - "text/plain": [ - " ID SRR1068788 SRR1068808 SRR1068832 SRR1068855 SRR1068880 SRR1068929\n", - 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A data.frame: 6 × 79
Runanalyte_typeAssay.TypeAvgSpotLenBasesBioProjectBioSamplebiospecimen_repositorybiospecimen_repository_sample_idbody_site⋯data_type..run.product_part_number..exp.product_part_number..run.sample_barcode..exp.sample_barcode..run.is_technical_controltarget_set..exp.primary_disease..exp.secondary_accessions..run.Alignment_Provider..run.
<chr><chr><chr><int><dbl><chr><chr><chr><chr><chr>⋯<chr><chr><chr><dbl><dbl><chr><lgl><chr><chr><lgl>
1SRR2911715RNARNA-Seq1503852895500PRJNA244100SAMN04216864Cloud TestingHG00103Lymphoblastoid cell line⋯NANANANA
2SRR2911716RNARNA-Seq1504885577400PRJNA244100SAMN04216866Cloud TestingHG00154Lymphoblastoid cell line⋯NANANANA
3SRR2911718RNARNA-Seq1502690545500PRJNA244100SAMN04216863Cloud TestingNA18910Lymphoblastoid cell line⋯NANANANA
4SRR2911719RNARNA-Seq1502699599350PRJNA244100SAMN04216865Cloud TestingNA19200Lymphoblastoid cell line⋯NANANANA
5SRR2911720RNARNA-Seq1524300467752PRJNA244100SAMN04216865Cloud TestingNA19200Lymphoblastoid cell line⋯NANANANA
6SRR2911717RNARNA-Seq1502666546700PRJNA244100SAMN04216863Cloud TestingNA18910Lymphoblastoid cell line⋯NANANANA
\n" - ], - "text/latex": [ - "A data.frame: 6 × 79\n", - "\\begin{tabular}{r|lllllllllllllllllllll}\n", - " & Run & analyte\\_type & Assay.Type & AvgSpotLen & Bases & BioProject & BioSample & biospecimen\\_repository & biospecimen\\_repository\\_sample\\_id & body\\_site & ⋯ & data\\_type..run. & product\\_part\\_number..exp. & product\\_part\\_number..run. & sample\\_barcode..exp. & sample\\_barcode..run. & is\\_technical\\_control & target\\_set..exp. & primary\\_disease..exp. & secondary\\_accessions..run. & Alignment\\_Provider..run.\\\\\n", - " & & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & SRR2911715 & RNA & RNA-Seq & 150 & 3852895500 & PRJNA244100 & SAMN04216864 & Cloud Testing & HG00103 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", - "\t2 & SRR2911716 & RNA & RNA-Seq & 150 & 4885577400 & PRJNA244100 & SAMN04216866 & Cloud Testing & HG00154 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", - "\t3 & SRR2911718 & RNA & RNA-Seq & 150 & 2690545500 & PRJNA244100 & SAMN04216863 & Cloud Testing & NA18910 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", - "\t4 & SRR2911719 & RNA & RNA-Seq & 150 & 2699599350 & PRJNA244100 & SAMN04216865 & Cloud Testing & NA19200 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", - "\t5 & SRR2911720 & RNA & RNA-Seq & 152 & 4300467752 & PRJNA244100 & SAMN04216865 & Cloud Testing & NA19200 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", - "\t6 & SRR2911717 & RNA & RNA-Seq & 150 & 2666546700 & PRJNA244100 & SAMN04216863 & Cloud Testing & NA18910 & Lymphoblastoid cell line & ⋯ & & & & NA & NA & & NA & & & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 79\n", - "\n", - "| | Run <chr> | analyte_type <chr> | Assay.Type <chr> | AvgSpotLen <int> | Bases <dbl> | BioProject <chr> | BioSample <chr> | biospecimen_repository <chr> | biospecimen_repository_sample_id <chr> | body_site <chr> | ⋯ ⋯ | data_type..run. <chr> | product_part_number..exp. <chr> | product_part_number..run. <chr> | sample_barcode..exp. <dbl> | sample_barcode..run. <dbl> | is_technical_control <chr> | target_set..exp. <lgl> | primary_disease..exp. <chr> | secondary_accessions..run. <chr> | Alignment_Provider..run. <lgl> |\n", - "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 1 | SRR2911715 | RNA | RNA-Seq | 150 | 3852895500 | PRJNA244100 | SAMN04216864 | Cloud Testing | HG00103 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", - "| 2 | SRR2911716 | RNA | RNA-Seq | 150 | 4885577400 | PRJNA244100 | SAMN04216866 | Cloud Testing | HG00154 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", - "| 3 | SRR2911718 | RNA | RNA-Seq | 150 | 2690545500 | PRJNA244100 | SAMN04216863 | Cloud Testing | NA18910 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", - "| 4 | SRR2911719 | RNA | RNA-Seq | 150 | 2699599350 | PRJNA244100 | SAMN04216865 | Cloud Testing | NA19200 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", - "| 5 | SRR2911720 | RNA | RNA-Seq | 152 | 4300467752 | PRJNA244100 | SAMN04216865 | Cloud Testing | NA19200 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", - "| 6 | SRR2911717 | RNA | RNA-Seq | 150 | 2666546700 | PRJNA244100 | SAMN04216863 | Cloud Testing | NA18910 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", + "| SRR2911715 | RNA | RNA-Seq | 150 | 3852895500 | PRJNA244100 | SAMN04216864 | Cloud Testing | HG00103 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", + "| SRR2911716 | RNA | RNA-Seq | 150 | 4885577400 | PRJNA244100 | SAMN04216866 | Cloud Testing | HG00154 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", + "| SRR2911718 | RNA | RNA-Seq | 150 | 2690545500 | PRJNA244100 | SAMN04216863 | Cloud Testing | NA18910 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", + "| SRR2911719 | RNA | RNA-Seq | 150 | 2699599350 | PRJNA244100 | SAMN04216865 | Cloud Testing | NA19200 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", + "| SRR2911720 | RNA | RNA-Seq | 152 | 4300467752 | PRJNA244100 | SAMN04216865 | Cloud Testing | NA19200 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", + "| SRR2911717 | RNA | RNA-Seq | 150 | 2666546700 | PRJNA244100 | SAMN04216863 | Cloud Testing | NA18910 | Lymphoblastoid cell line | ⋯ | | | | NA | NA | | NA | | | NA |\n", "\n" ], "text/plain": [ - " Run analyte_type Assay.Type AvgSpotLen Bases BioProject \n", + " Run analyte_type Assay Type AvgSpotLen Bases BioProject \n", "1 SRR2911715 RNA RNA-Seq 150 3852895500 PRJNA244100\n", "2 SRR2911716 RNA RNA-Seq 150 4885577400 PRJNA244100\n", "3 SRR2911718 RNA RNA-Seq 150 2690545500 PRJNA244100\n", @@ -468,28 +378,28 @@ "4 SAMN04216865 Cloud Testing NA19200 \n", "5 SAMN04216865 Cloud Testing NA19200 \n", "6 SAMN04216863 Cloud Testing NA18910 \n", - " body_site ⋯ data_type..run. product_part_number..exp.\n", + " body_site ⋯ data_type (run) product_part_number (exp)\n", "1 Lymphoblastoid cell line ⋯ \n", "2 Lymphoblastoid cell line ⋯ \n", "3 Lymphoblastoid cell line ⋯ \n", "4 Lymphoblastoid cell line ⋯ \n", "5 Lymphoblastoid cell line ⋯ \n", "6 Lymphoblastoid cell line ⋯ \n", - " product_part_number..run. sample_barcode..exp. sample_barcode..run.\n", + " product_part_number (run) sample_barcode (exp) sample_barcode (run)\n", "1 NA NA \n", "2 NA NA \n", "3 NA NA \n", "4 NA NA \n", "5 NA NA \n", "6 NA NA \n", - " is_technical_control target_set..exp. primary_disease..exp.\n", + " is_technical_control target_set (exp) primary_disease (exp)\n", "1 NA \n", "2 NA \n", "3 NA \n", "4 NA \n", "5 NA \n", "6 NA \n", - " secondary_accessions..run. Alignment_Provider..run.\n", + " secondary_accessions (run) Alignment_Provider (run)\n", "1 NA \n", "2 NA \n", "3 NA \n", @@ -500,62 +410,6 @@ }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "ijc.iso.counts.mem <- data.table::fread(\"../data/rmats_final.se.jc.ijc.txt.gz\") \n", - "sjc.iso.counts.mem <- data.table::fread(\"../data/rmats_final.se.jc.sjc.txt.gz\") \n", - "inc.iso.counts.mem <- data.table::fread(\"../data/rmats_final.se.jc.inc.txt.gz\")\n", - "\n", - "meta.data<-read.csv('../data/SraRunTable.noCram.noExome.noWGS.totalRNA.txt',header=TRUE, stringsAsFactors=FALSE)\n", - "head(ijc.iso.counts.mem)\n", - "head(sjc.iso.counts.mem)\n", - "head(inc.iso.counts.mem)\n", - "head(meta.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Synchronize metadata samples with ijc, sjc and inc samples\n", - "\n", - "Keep only the runs that are in the ijc count list (assuming ijc and sjc are the same). As well, name the rows with the junction id column and then make the matrix just about the counts." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 42611\n", - "\\item 8661\n", + "\\item 8673\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 42611\n", - "2. 8661\n", + "2. 8673\n", "\n", "\n" ], "text/plain": [ - "[1] 42611 8661" + "[1] 42611 8673" ] }, "metadata": {}, @@ -929,22 +751,22 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", - "\\item 42611\n", - "\\item 8661\n", + "\\item 8673\n", + "\\item 79\n", "\\end{enumerate*}\n" ], "text/markdown": [ - "1. 42611\n", - "2. 8661\n", + "1. 8673\n", + "2. 79\n", "\n", "\n" ], "text/plain": [ - "[1] 42611 8661" + "[1] 8673 79" ] }, "metadata": {}, @@ -952,37 +774,36 @@ } ], "source": [ - "keep.meta.data <- (!grepl('11ILO',reduced.meta.data$Sample.Name))\n", - "table(keep.meta.data)\n", - "ijc.iso.counts.mem2 <-ijc.iso.counts.mem2 [ ,keep.meta.data==TRUE]\n", - "sjc.iso.counts.mem2 <-sjc.iso.counts.mem2 [ ,keep.meta.data==TRUE]\n", - "inc.iso.counts.mem2 <-inc.iso.counts.mem2 [ ,keep.meta.data==TRUE]\n", - "reduced.meta.data <-reduced.meta.data [keep.meta.data==TRUE, ]\n", + "meta.data.run.names <- as.character(reduced.meta.data$Run)\n", + "ijc.iso.counts.mem2 <- as_tibble(ijc.iso.counts.mem)\n", + "sjc.iso.counts.mem2 <- as_tibble(sjc.iso.counts.mem)\n", + "\n", + "ijc.iso.counts.mem2 <- ijc.iso.counts.mem2[,c(meta.data.run.names)]\n", + "sjc.iso.counts.mem2 <- sjc.iso.counts.mem2[,c(meta.data.run.names)]\n", + "\n", "dim(ijc.iso.counts.mem2)\n", "dim(sjc.iso.counts.mem2)\n", - "dim(inc.iso.counts.mem2)" + "dim(reduced.meta.data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### and focus on a single tissue\n", - "\n", - "this will become a function so we can proceed on all the tissues" + "Remove samples that match '11IL0' from the ijc, sjc and metadata files using the logical grep, grepl" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tissue\n", + "keep.meta.data\n", "FALSE TRUE \n", - " 8470 191 " + " 12 8661 " ] }, "metadata": {}, @@ -996,22 +817,88 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
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\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 42611\n", + "\\item 8661\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 42611\n", + "2. 8661\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] 42611 8661" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "keep.meta.data <- (!grepl('11ILO',reduced.meta.data$\"Sample Name\"))\n", + "table(keep.meta.data)\n", + "ijc.iso.counts.mem2 <-ijc.iso.counts.mem2 [ ,keep.meta.data==TRUE]\n", + "sjc.iso.counts.mem2 <-sjc.iso.counts.mem2 [ ,keep.meta.data==TRUE]\n", + "\n", + "reduced.meta.data <-reduced.meta.data [keep.meta.data==TRUE, ]\n", + "dim(ijc.iso.counts.mem2)\n", + "dim(sjc.iso.counts.mem2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### and focus on a single tissue\n", + "\n", + "this will become a function so we can proceed on all the tissues" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tissue\n", + "FALSE TRUE \n", + " 8470 191 " ] }, "metadata": {}, @@ -1082,11 +969,10 @@ "\n", "ijc.iso.counts.mem2 <-ijc.iso.counts.mem2 [ ,tissue==TRUE]\n", "sjc.iso.counts.mem2 <-sjc.iso.counts.mem2 [ ,tissue==TRUE]\n", - "inc.iso.counts.mem2 <-inc.iso.counts.mem2 [ ,tissue==TRUE]\n", + "\n", "reduced.meta.data <-reduced.meta.data [tissue==TRUE, ]\n", "dim(ijc.iso.counts.mem2)\n", - "dim(sjc.iso.counts.mem2)\n", - "dim(inc.iso.counts.mem2)" + "dim(sjc.iso.counts.mem2)" ] }, { @@ -1103,14 +989,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "ijc <- as.data.frame(ijc.iso.counts.mem2)\n", "sjc <- as.data.frame(sjc.iso.counts.mem2)\n", "ijc <- data.matrix(ijc)\n", - "sjc <- data.matrix(sjc)\n" + "sjc <- data.matrix(sjc)" ] }, { @@ -1128,19 +1014,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [ - { - "data": { - "image/png": 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//kJz+pqamprq7u0KHDVVddtWTJks6d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nhS9p9y/C9L137JI4lKJjmsoLrr02vr+lyiGQp35TA7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itW7L1pHAFmOuWqoKO3KQsIgaEIQyJVjY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c+ePX5+fpY3w6WkpAghmpqa3nrrLauNL126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\ngg==", - "text/plain": [ - "Plot with title “voom: Mean-variance trend”" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "image/png": 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OdzPDnhEVBtnm595w2on2RVkHQBwCsI\ndj7HkxMe7LpURU5DYb16pUNhgP8kSboA4BUEOx/FhAf8Gt8ZAMArCHY+igkPAABQXQQ7H8WE\nBwAAqC6CHQAAgEUQ7AAAACyCYBegPL/rklUPJGD/KgCA7yDYwUM4kCBAkHQBwIsIdvAQDiQA\nAMDdCHY+x9oTHuzPBwCA+xDs4FHszwcAgPsQ7OBR7M8HwO9YtfcLlkSwAwCgMvR+wY8Q7AAA\nqAy9X/AjBDsAAJyj9wt+gWAHK2NlDABXofcLfoFgBytjZQwAV6H3C36BYIfKuHDGyyv787Ey\nBgAQUAh2qIw1ZrxYGQPAHaZNc3z1/fOfJWnyZBZ7wMsIdqiMNWa8WBkDwB3Mdw/zq++YMZK0\ne7f/ffWFxRDs4Jy/z3ixMgaAOwQHS+e/+rZpI0lxcf731RcWQ7CDc8x4AUBFSn71bdlS8rev\nvrAYgh2cY8YLQCCrqPcrPFw6/0XX7P2KiXGMmPjqCw8j2AEAUHN89YVPIdgBAABYBMEOsALO\n2AAAiGAHa/PKrsheYY0dBwEAtUSwA6zAGjsOAgBqiWCHygTOjJc1+PuOgwCAWiLYAdbBjoOA\nJ/HVFz6IYAdYB9suAECAI9gB3kEfKwDA5Qh2gHfQxwoAcDmCHeAd9LECAFyOYAd4E32sAAAX\nItgB3kQfKwDAhQh2gDe5qo+VbRcAP0UfFVyLYIdAVPk7acOGCgpSfLz69eN9FoB70UcF1yLY\nIRBV/k56222StGWL/vd/tWoV77MA3Ig+KrgWwQ6BqPJ30vBwSercWYWFOnOG91kAbkcfFVyF\nYIfAVfk76S23SOffSXmfBeBW9FHBVQh2CFyVv5Neeql0/p2U91kAbsV5gHAVgh0CV+XvpMHB\nzh9fG/SxApZXlY7XDz6QpLFjHY984QVJSk93PHLcOEmaMoWeLThHsINvofMfgGVUpeP12DHp\n/GUB85H33SdJP/3keOTo0ZL0yy/0bME5gh18C53/ACyjKh2v5hLe48cdj2zRQpK6d3c8sm1b\nSbr5Znq24BzBDr6Fzn8AFlN5n1bTppJ0+nTpR7ZqVfqRLVtK9GzBGYIdfBGd/wAso/I+rTp1\nJOmPP0qPm8t8yz6XixWoHMEOvojOfwCWUXmfVmqqJDVo4Bg3+6jatHGMmH1U11/v/lrh/wh2\n8EXu7vyvvCPVvHfiREdHKv2qAAC/QLADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAA3KLq\nJweuW8cZg3ANgh18CyeoAgBQYwQ7AABqgrOt4YMIdgAA1ARnW8MHEewAAKgJzraGDyLYAU5w\ntQVAJTjbGj6FYAc4wdUWAJXgbGv4FIId4ARXWwBUwt1nWwPVQrADqoSrLQAA30ewA6qEqy0A\nAN9HsAOqhKstAADfR7ADAACwCIIdAACARRDsAACoCc62hg8i2AEA4CFseA53I9gBAOAhbHgO\ndyPYwZpc+LWYqy0AXIUNz+FuBDtYE1+LAfgsNjyH+xDsYE18LQbgs9jwHO5DsIOV8bUYgA9i\nw3O4D8EOfq/c5XSHDknSm286ltOZI3wtBgBYWF1vFwDUln05XWKisrK0bZtGjLBd3WjQQCtX\nKi9PaWnatk3iazEAwNIIdvB7JZfTSYqN1erVWrpUksaNU4cOiotTdrZmzfJmkQAAeACXYmER\nZZfTlWQup/NHbGcKAKg6/w52xcXFubm569ev37Vrl7drgZeV7TIryX+vwLJvC+DL+OoFX+M3\nwe7pp59ev359yZG5c+e2aNEiPj7+1ltvbd++/XXXXffNN994qzx4nf9Gt8qxbwvgy6r71YsN\nz+FufhPsJk+e/NFHH9n/74cffjhixIgTJ07ce++96enp3bp127x58y233LJz504vFgm4Cfu2\nAL6Jr17wNf7aPDF27NimTZt+8cUXnTp1Mkfee++9/v37P/PMM2+88YZ3a4OPyM6+4GuxpMWL\nS38tTkvzcFE1xHamgC/jqxd8h9/M2JV06NChHTt2jBo1yp7qJCUnJ/fp0+d///d/vVgYLMZ3\nVs+wnSngy/jqBd/hl8Hu1KlTkkqmOlN0dPRvv/3mjYpgTTQuAKgKvnrBd/hlsIuIiGjatOm+\nfftKjf/666+NGzf2SkmwJFbPAAD8iz8Fuz179mzatOmnn37Kz88fOXLk66+/fuLECfu9//73\nv//5z39269bNixXCK9zdZcbqGQCAv/Cn5onFixcvXry45Mi//vWvfv36SVq0aNEjjzxy8uTJ\nyZMne6k6WBarZwAA/sJvgt0//vGPYyUcP3782LFjF110kXnvsWPHmjVrtmTJkuuvv967dfq4\nIUO0eLHy8zV+vN5/XwUFionR7NmKjtbEiVq2TMePKyZGL72kLl28XavPYPUMAMBf+E2we+CB\nByq59/777x8xYkSdOv50Zdkr7N0AiYnKytK2bRoxQikpionRVVdp5Url5SktTUlJ2ruX+OIT\nMjOVmXnBSEaGMjIuGPGjfVsAAG5lkSQUFhZGqqsKugEAwIU4SQK+hjAUiOgGAADAkvzmUqxT\nO3fuTE9Pl7RmzZqqP6uwsPD5558/ffp0JY+x3hG0dAMAAGBJ1gl2BQUFa9eure6zioqKNm3a\nVFxcXMljfvnlF0mGYdS8OB9DNwAAAJZknWB35ZVXfvvtt9V9Vnh4+Icfflj5Y+bOnTtixIig\noKCalgZ/ReMCAE9i4wLUnnWCXWhoaHR0tLerAACghti4ALXnf8HOMIxdu3b9/PPPBQUFkpo2\nbRoVFdW6dWtv1wUAQK2Yi57XrNGyZbZJuzNntGePOnTQlCm2SbuiIh07prfe0oMPertc+CR/\n6orNz89/4oknWrRoccUVV9x+++3JycnJycm33XZbmzZt2rZt+9RTT508edLbNQIAUFspKYqM\nVFaWkpIkaetWDRyo0FCtXClzU9c//5kWN5TPb2bs9u/f361bt127dkVFRSUlJbVt27ZRo0aS\nfv/99507d3766adTpkx59913169fbz+OAgAAf2TuNiqpSxd98IGOHLHtNiopPl7z5unYMeXm\nKiHBu2XCF/lNsJs8efK+ffuWLl2akpJS9t5z587NnTt39OjR06ZNmzlzpufL8xd0AwCA7yu5\n26ip5G6jJnYbRbn85lLshx9+OGzYsHJTnaTg4OCRI0cOGDDgvffe83BhAAC4Vsm9RSsa4VIs\nylWrYJefn5+Xl+eiSpw4cuTIFVdcUfljOnXqdPDgQc/UAwCAm7DbKGqssmC3bdu2u+666/LL\nL09MTHz55ZfPnTtX6gEzZsxo166dO8tziIiI2Lp1a+WP2bJlS0REhGfqAQAA8DUVBruNGzfG\nx8evXr360KFDX3311ahRo2677bb8/HxPFldS3759ly1b9sILL5R7/FdRUdHUqVNXrFgxcOBA\nz9cGAHZDhigoSMeOKT1d4eFq2FBduyonRydOaMwYRUYqLEwJCfr6a28XCsCKKmyeeO655/74\n44/ly5f36dOnuLj45ZdfHj9+fM+ePdevX2+2o3pYRkZGdnb2uHHjnnzyyfj4+NatW4eFhRmG\nUVhYuHv37pycnBMnTiQmJk6aNMnztQGAHXvMAvAmowKtW7e+7777So6sXbs2JCQkKSnp7Nmz\n5sj48eMreQWXO3369H//939fe+21wcHBJf8K9erV69q162uvvWYvzLVeffVVSQUFBe54cQAW\n89BDhmQ8+qhjZMAAQzL693eMPP64IRkbN3q+Ovg68/dnxw7HyNSphmRkZztG5s0zJGPxYsMw\njMGDDcnIzzceecRo3txo0MC44Qbjq6+MoiLj8ceNiAijUSPjxhuNzZs9/RexNvPi4Uaf/G+4\nwhm7AwcOtG/fvuTIrbfempmZef/99//5z3+eNWuWW2JmpUJCQsaOHTt27NhTp07t3bvXPHmi\nSZMmbdq0CTG/IwOAbyi5XUVUlHThdhUdO0psVwFXYIYYpVQY7MLDw7/55ptSg8OGDdu+fftz\nzz3XqlWrcePGubm2CoWGhkaZ75QA4JNKbk5hnhNVcsT8fGW7CpRV3d1Gzd8u+4bGsbFavVpL\nlyo+3rahcVycsrM1axYbGgeKCpsnkpOTP/jgg5deeunMhe89zzzzTGpq6l/+8pexY8eeOHHC\n/RUCgP9huwrUTM2ab5ghhl2FwW7KlCmtW7d+7LHHksyT6s4LCgr6xz/+8ac//WnmzJkvvvii\n+ysEAFgQ7cPlsl9aNc+KfeUVbd2qlBTHWbELFmj7diUlXTDjywwx7CoMdpdccsnmzZtHjhwZ\nHR1d6q6goKBZs2a9++67TncMBgCgXDVLMJZX8tJqbKxSU9W7t/bssZ0VGxenfv2UmqqDB5Wb\n63gWM8Swq2yD4ksvvXTOnDn/8z//U+69ycnJP/30k2EY7ikMAGBlNUswvsZN845cWkWN+c1Z\nsQAA6/H3BOOmeUcuraLGCHYAAK/x9wRT+3nHUnN+8+dL0vbtjjm/Z5+VpF27PPQ3gr8j2AGA\nK2VmyjDUoYNjJCNDhqHu3R0jaWkyDA0a5PnqfI41FofVZt6x1Jxft26S9Nhjjjm/e++VpHHj\nfDrgwncQ7AAAqJXazDuWmvMzc+Gvvyo0VHv26Lrr1K6dJB09qsaNbQv4ioslaeRIhYXpgw8q\nrMo+F7hwoSSlptJ3HBAIdgC8r9wV6D17KihIjz6qRo1Up46Cg3X11XwswRfVft6x5JyfqU8f\n22TesmW2kYcfti3gmztXksaN04IFKi5WeLjatnU80T5DbJ8LvO02ff21XnuNvuOAQLAD4H3l\nrkDfuFGSsrI0bJj+9jeFhur779W/Px9LsKCSM3z2EXMy7+KLbSPdutkW8JmpsV07Jwv4rNF3\njOpyHuw+++yzo0ePlntXTk7Ou+++6+qSAASccj+Biook6c479eqreuIJPfywDEN79/KxBAuq\nZM6vUyfHoHmh9tprHSNOG4f9ve8Y1eU82CUmJm7YsKHcu7Kzsx9++GFXlwQgQJX9BCo5aH4C\nqdYfSxx4AF+WmampUy8YmTBB8+bZbptfgcwO2V69lJ6u8eMl6a9/Led3+MgRyf/7jlFdFQa7\nn376KSsrKysrS9KWLVuyyli+fPnSpUtPnz7twWoBX1fL0FCVp192mWVzSdlPoJKD9gmMWn4s\nceCBj6B9uIrKTubVry+d/x0eM0aSdu8u53f4o4/Kf7o/9h2j6upWdMc777wzYcIE8/aTTz5Z\n0cP69+/v+qIAv2UPDYmJysrStm0aMUIpKYqJ0VVXaeVK5eUpLU1JSdq7t5y316o8fcgQSerf\nXzfdVJM/wpeVW63LP5ZKXvaVFBur1au1dKni4zV9uiTFxSk7W7NmKTdXCQm1+rMAdwgOls7/\nDmdmSlJcnL78spzfYQSgCoPdX//619TU1Nzc3D59+gwbNqxz586lHhAcHNy+ffvevXu7uULA\nn9QyNFTl6VFR+v57NWlCLqkVFh7BJTIzbdHKLiNDGRkXjKSlKS3N9X90yd/hli2l8n6HEYAq\nDHaSWrZs2bt377vuumvkyJFdu3b1WE2Av6tlaKj86U2bStLVV9fqjwALj+DvSv7G1qlTesS/\nZuvhQs6bJ1atWkWqA6qllqGh8qeb7+BNmtTqjwALj+AjarzWkN9hlKuyGTuTYRjvvPPOm2++\nuW/fvjPlfVB89913bigM8GO1fMOtytPNRTY1/iMAAJbkfMbu73//+4ABA1atWvXjjz/uK48H\nqgQAINBUNJkXHu4YMSfzoqPLf4XFi+k7DjjOg92sWbN69uy5c+fOoqKiY+XxQJUArK2iD7CS\ng+YnEB9LAFAJ58Hu4MGD06ZNa9++vQeqARBQ2CsYAFzLebALDw83DMMDpQAINOwVDACu5TzY\nDR48eOHChR4oBfAW5o28xSuHlHPgAfwdv8OohPNgN2XKlJ07dw4dOvSjjz7avn37T2V4oErA\nrVw4b1TLN9yqPD07W4ZhO0eoBn+ED2KvYMBb+FprPc6DXePGjT/66KNFixb16tWrc+fOUWV4\noErArbwybwQ79gquOj6GUS1Of2GWLZOkXr1YDmEdzvexGzx4cEhISN26zh8J+DXmjbyFfVar\nrpaHEcOPDBmixYuVn6/x4/X++yooUEyMZs9WdLQmTtSyZTp+XDExeukldelS4XOkrvIAACAA\nSURBVIs4/YUZP15r12rbNmVnq149jii0AudxbdGiRR6oA/A65o3g+2p5GDH8iEtCvNNfmMsv\nl6STJx2/MHyt9XfOL8XaFRQUfP/992xc56MWLVKrVqpbV+PGebsUf8W8EfwFs8t+qlpX0l24\nRKTyXxiT/ReGr7X+rkrB7tNPP73uuuuaNGkSHR395ZdfmoO9e/deu3atO2uzLpeHsOPHlZam\nwkI99ZR69nTNawLwVcwu+6ka9Gm5JMRX/gtjKvULw9da/+U82OXk5Nxxxx0//vhjzxKJ4dCh\nQ7m5uUlJSZs3b3ZneVbkjhC2Y4dOntTQoZowQT16uOY1AfgqZpf9VA0m4VwS4vmFCSjOg92T\nTz7ZokWLH374Yf78+fbByy67bOvWrS1atHjqqafcWJ0luSOEnTolSY0bu+bVAABuU61JODIZ\nqst5sPvyyy8fffTRVq1alRpv3rz5iBEjNmzY4J7CrMvlIaxXLyUmStKMGQoK0ogRLntlwM3Y\nZxUBiCvpcCvnwe748eOtW7cu966WLVsWFha6uiRLc0cImzpVzz4rScnJWr5cjz7qgtcEALgH\nk3BwK+fBrkWLFtu3by/3rg0bNkRERLi6JEtzRwi78UZbWIyKUt++uuYaF7xmgPHZeSN2owUA\nVIvzYJeUlPTyyy9/feFHR35+/n/913/94x//uOuuu9xWmxURwlAdLjzrDADKyszUvHkXjPjI\n11rUmPNgN23atLCwsBtuuMHMcBMmTIiNjW3ZsuWzzz7bpk2bKeamhwDcgLPOUIrPzi7DN/EL\nE4CqdCl206ZNDz/88O7duyV9880333zzTePGjR999NHc3Nzw8HD3FwkENHajBQKQOzIZqzsC\nQZU2KG7evPnLL7986NChAwcO7Nix48CBA4cOHXr55ZebN2/u7voA0ENnJXyywotY3REIqnGk\nWFBQUHh4eIcOHZilAzyJHjor4ZM1kHn9wiirOwJBXaePMAzjnXfeefPNN/ft23emvHea7777\nzg2FAYAFOT2UPS5O2dmaNctxKDvgWqzusDbnwe7vf//7uHHjJDVs2LAeEwUAUGt8ssKLWN1h\nbc6D3axZs3r27Pnyyy+3b9/eAwUBgOXxyQovYnWHtTkPdgcPHnznnXdIdT6te3cZhreLAFBV\nfLICcBPnwS48PNwgNLgQIQwAALiH867YwYMHL1y40AOlACjF6z10AAD/4nzGbsqUKf379x86\ndOj999/fpk2bsv0THUp+7AAAAMBLnAe7xo0bmzcWLVpU7gO4UAsAAOALnAe7wYMHh4SE1K3r\n/JEAAMBnZWYqM/OCkYwMZWRcMJKWprQ0D9YEVwtivs2puXPnjhgxoqCgICwszNu1AABQ2pAh\nWrxY+fkaP17vv6+CAsXEaPZsRUdr4kQtW6bjxxUTo5deUpcu/vEH+bji4uL69etv3Lgxwfe2\nEa/GPNzhw4d37NhRVFTUuHHjjh07NmvWzH1lAQCAKrIfVZeYqKwsbdumESOUkqKYGF11lVau\nVF6e0tKUlKS9e2u1t47H/iDUWJXOiv3ss8+6du162WWXJSQk3H777V27dr344ot79OjBYWIA\nABcaMkRBQTp2TOnpCg9Xw4bq2lU5OTpxQmPGKDJSYWFKSNDXX3u7UB9T8qi655/XAw/ozju1\nZ48+/lizZ2vUKLVuraFDdfCgIiJq9TPktFnf53zGLicnp0ePHmfPnu3evXvHjh0bNGhQVFT0\nww8/rFu3rlu3bjk5OR3N428AAKgdJoRqwzyqzvwZfvONJE2erFatbD/Dpk0l6S9/Ufv2tf0Z\nciaeL3Me7J5++unLLrvs448/vvLKK0uOb9mypVevXtOmTauoWxYAgGopOSEkKTZWq1dr6VLF\nx2v6dEmKi1N2tmbNUm6ufG91k5eZB9OZP8NLLtGuXUpI0H/8h+1nGBcnSa1bq1+/2v4MORPP\nlzm/FPv555+PHDmyVKqTFBsbO3LkyHXr1rmnMABAgGJCqGZKTr916uQYMX+G117ruLeWP0PO\nxPNlzoPd8ePHW7VqVe5dl19++dGjR11dEgAgoDEhVHtNmjhumz9Ds+Nx8GAdO6YlSyRpyBCW\nMFqQ82DXvHnz7du3l3vXDz/80Lx5c1eXBI9YtEitWqluXY0b5+1SAOACf/mLo4XihRckafRo\nR/4w37SmTCF/VKZOmY93+3a0KSm2kPfQQ9q6VSkpGjhQoaFauVILFmj7diUlOcnNY8eW/gdK\nT+cfyFc4D3Z33HHHiy++uGLFipI73hmGsXz58jlz5tx5553uLA/ucfy40tJUWKinnlLPnt6u\nBgAuYG+hiIzUffdJ0k8/OfLH6NGS9MsvzvMHyhUVpXvukaSbbqphTyv/QL7MebCbOnVqw4YN\n+/btGxERcdttt/Xu3fu2226LiIhITk5u0qTJ1KlTPVAlXGzHDp08qaFDNWGCevTwdjUAcIHg\nYOl8C0WLFpLUvbsjf7RtK0k332zZPTXcvedL7ZcwBvg/kI9zHuwuv/zyTZs2paamnjx5ct26\ndR988MG6deuKi4vT0tI2b95c0fI7+LRTpyTp/CnAAOCDSuYP86OmZP5o2VKyaAtFyfmwrCy9\n8koNL5hWxFVLGAP2H8jHVWmD4tatW8+fPz8/P//XX3/dsWPH/v37jxw5Mm/evJbmvxv8S69e\nSkyUpBkzFBSkESO8XRAAlKNk2jCniMomEkte6avBJsCZmTIMdejgeJE//UmGoe7dHSN3362H\nHpJq19Nq/kFmZ4b5z5GRIcNQu3aOkbQ0GYZuvFGy6D+Qj6tSsDMdOHDgwIEDe/fu/e233w4d\nOuS+muBeU6fq2WclKTlZy5fr0Ue9XRAAL/PN8x4CfE8N39/zJcD/gXxWlYLdvHnz2rVrFxER\n0aVLl1tvvfWaa65p3rx5p06dlpgN0/AvN95om7GLilLfvrrmGm8XBMDL3H3tr+pKTgiZzAmh\nNm0cI+aE0PXXu7cSr5s505G2Z86UpGefdaTtiRMlacKEctJ22dk782dYcvbO/BkOGuTuvwS8\nwHmwe+WVVx555JH9+/f36NEjNTV15MiRQ4cOjY+P/7//+7/Bgwe/+eabHqgSAOA+HADqg+rX\nly7sPN2502Wdp/Y52hdflKTbblN4uC6+WA0b6j//U5IeeEAREWrenJ3t/I/zYDdz5syePXse\nPHjw448/nj9//pw5c956662vvvpq586dHTp0mDFjhgeqBAC4m+9f+wsobu08tc/RmjciI3X0\nqOrXV8eOKix0jJw7pzfe8NBkLVzFebDLy8ubPHlyU/P04BLatWs3duzYnTt3uqcwAIBHcd6D\nD3JT56l9jtZ8hWuuUXKyDhxQhw7q0kWSLrtMI0bo6FG1aqXwcB08qPXrHUswV66UpJMnHUsw\nX3+9hn9BuJzzYNe0adNg84tDGcHBwZdeeqmrSwIAeAFr4X2Qa1uDSy2/S062xbhRoxxztOam\n9aNHO+ZoL7pIksaNcyzBNA8TffxxxxLMw4cl6ezZGv0l4VLOg90999zzwQcflHvXqlWrUlJS\nXF0SACCgsfzfzq1p254RIyMdc7T2G/Y5WnMkPNyxBLNfP0m65BLHEsz0dOn8FV5TgPwD+aC6\nTh/x9NNP9+3bNy8vb9CgQVFRUQ0bNiwqKvrhhx/eeOON4uLiUaNG7du3z/5g9isGAMAv2DNi\nJTfsSrYhswTTlzmfsYuIiMjJyVm0aFHv3r07derUtm3bzp079+/ff/Xq1WvWrImKimpdggcq\nBgDAwnxzz5eLL3bcrs0STN/cNNFKnM/Y9e3bt77ZdQ0AAAJS2cX25V4UHjJEixcrP1/jx+v9\n91VQoJgYzZ6t6GhNnKhly/Tbb5LUq5eSkpSVpW3bNGKEUlIUE6OrrtLKlcrLU1qakpK0dy+r\nPGvCebBbvny5B+qAR3XvLsPwdhEAAKuxb6SSmFh+bhs/XmvXats2ZWerXj3Fxmr1ai1dqvh4\nTZ8uSXFxys7WrFnKzVVCgnf/Nn6pGkeKAQAsiWYFuIrTza4vv1ySTp50bL/Hij3Xcj5jJ+nc\nuXNfffXV/v37z5R3/XwQ/6EDAOBSmZnKzLxgJCNDGRkXjKSlKS3NgzVVWeWbXZvsuY1NE13L\nebDbvHlz//798/LyKnoAwQ4AAD9iT432G/bU2L2744aZGtes0Wef6e67q/H6lW92bSqV21hO\n5yrOg93o0aOPHTv2+OOPd+zYsR4/eJ+yaJH+8hcdOKCxY/X8896uBgDglyrveFi8WJIGDFBm\npm1DY6fY7NqLnAe7b7/99q233urbt68HqkE1HD+utDSFhOippzza8g6fQrgHUGuVdzxs2FC6\nU7WSi8Jr1ni+fFzAebALCwtrU3LzHPiIHTt08qQefFATJni7FHgJ4R6AK5TseJDoVPVvzrti\nBwwY8M4773igFFTPqVOS1Lixt+uA95jhfuhQTZigHj28XQ0A/1Z5xwOdqv7C+Yzd9OnTBw0a\nNGDAgD59+kRERJRdZte9ZEM8PKNXL330kSTNmKEZM5Serldf9XZN8DjCPQDXqbzjgU5Vf+E8\n2H333XfffPPN3r17ly1bVu4DDLa69bypU3XzzZo4UcnJGjZM7dp5uyB4HOEegEt5puMhM1Nd\nu+rhhx0jfrSNi19wHuwee+yxQ4cODRgwICoqqm7dKu17B7e78UadOydJUVGiryUwEe4B+B6/\n3n7PGpwHtW3bts2bN+++++7zQDUAqopwDwAow3nzRKNGjaKjoz1QCgAA8ClDhigoSMeOaeFC\nSUpNVdeuysnRiRMaM0aRkQoLU0KCvv7a24XiPOfB7t577121apUHSgEAAD7FvsVds2aS9NBD\n2rpVKSkaOFChoVq5UgsWaPt2JSWV7quwJ8L0dIWHq2FDEqGHOL8U+/zzz6ekpOzfv//ee++N\njIws2xXboeTB0QAAwCrsW9x16aKVK3XTTTpypEpb3FW+6fHKlaU3PYarOA92F110kaQ1a9a8\n/PLL5T6ArlgAAPxXJR0P8+dLUnKyevSwdTx8951UhS3u2PTYW5wHu8GDB4eEhNAPCwBAYKrx\nFndseux5zuPaokWLPFBH1RmGsWvXrp9//rmgoEBS06ZNo6KiWrdu7e26AACwprKXSp99Vrfe\nqvx8jR+vxYslafJktW+v6GhNnKhly/Tbb5J0/LjjKWx67BnVmIc7fPjwjh07ioqKGjdu3LFj\nx2bmQkoPys/Pf+aZZxYuXPib+ftSQps2bdLS0p544okGDRp4uCoAAAKNmcnMJXRjxuipp7R7\n9wVL6MaP19q1GjFCvXtfkAtZTuduVQp2n3322RNPPPHVV1/ZR4KCgm699daZM2d6bCeU/fv3\nd+vWbdeuXVFRUUlJSW3btm3UqJGk33//fefOnZ9++umUKVPefffd9evXm4sCra97d7G6EQDg\nDcHB0vkldOb6vLg4ffmlYwnd5ZdL0uHDLKHzNOfBLicnp0ePHmfPnu3evXvHjh0bNGhQVFT0\nww8/rFu3rlu3bjk5OR3Ni+RuNnny5H379i1dujQlJaXsvefOnZs7d+7o0aOnTZs2c+ZMD9QD\neB/hHoBXlVxC17KlJP3yi4KClJ+vjRtt4w88oLfeUnS0srIk6fbbFRurl15Sly4eLzcwOA92\nTz/99GWXXfbxxx9feeWVJce3bNnSq1evadOmeWYR3ocffjhs2LByU52k4ODgkSNHbtiw4b33\n3iPYAQBQLUOGaPFi25q5999XQYFiYjR7ttatk6SEBOXnKyhIdero0ksl2Q6+GTpURUVq0UKS\n6tSRpCZNJCklRQ0b2l45L892idZcYzdpkl54wbbLCdzB+QbFn3/++ciRI0ulOkmxsbEjR45c\nZ/6bu9+RI0euuOKKyh/TqVOngwcPeqYeAAAsw77tXGSksrJ0zTX66iv162eLX0eOqGlThYSo\nfn1bE6s5/TZ+vBYssI388Yd0fgldVJRiY22vHBenPXsUGqoePSTp5puVmqqDB5Wb69G/YOBw\nHuyOHz/eqlWrcu+6/PLLjx496uqSyhcREbF169bKH7Nly5aIiAjP1AMAgGWU3HYuNta2Ecm+\nfQoLk6RbblFRkerU0e+/y9yF4rLLJOm559S7tx5+WJJKrm8ve4mWXU48xnmwa968+fbt28u9\n64cffmjevLmrSypf3759ly1b9sILL5w+fbrsvUVFRVOnTl2xYsXAgQM9Uw8AABZjD2T2vWuv\nuUaSJkxQ794qKJCk666TpE6dpPO9EWZQKyx0vE5kpDIzNW+edP4SbWSkMjJkGOre3bHLSVqa\nDEODBrn7rxVYnK+xu+OOO1588cUbbrihd+/eQUFB5qBhGO+///6cOXMGDx7s5gptMjIysrOz\nx40b9+STT8bHx7du3TosLMwwjMLCwt27d+fk5Jw4cSIxMXHSpEmeqQcAAIspucmcqVEj27i5\nvXCpcUn799uCmrnqzlR2TxN2OfEY58Fu6tSpq1ev7tu3b4sWLTp37tyoUSOzK/bAgQMtW7ac\nOnWqB6qU1KxZsy+++GLOnDlvvvnmJ598cq7Eb1C9evXi4uKGDx8+fPjwYLMDGwAAVNwVYe4k\n/NprOnlS11+vQ4ckqUsXXXONZs/W2bO2p3/0kST9+KNKnT9V5/wFP7YX9jXOL8VefvnlmzZt\nSk1NPXny5Lp16z744IN169YVFxenpaVt3ry5ouV37hASEjJ27NgtW7YUFhb++OOPmzdv3rx5\n844dOwoLC7/44ouHH36YVAcAQEmluiJeeUVbtyolRQMHKjRUt90mSVu26JdfJGnaNNu969fb\nnn7ttZKUlmbrjYDvcx7sJLVu3Xr+/Pn5+fm//vrrjh079u/ff+TIkXnz5rU0l0R6XGhoaFRU\nVJcuXbp06dKhQ4cQ89cWAABcqFRXRGqqeve2talOn67wcEnq3Nk28ZacbLvXPk9ifs4fPmxL\nfnbjx9uW0Nn17y/DsL2gyVxC56lzDGDjJNj99ttvX3zxhXk7KCioZcuWHTp0aNGixZw5c44d\nO+b+8uB+ixapVSvVratx47xdCgDALUq2qZqr5Uq2qd5yS+l727Qp/QoleyPgyypbY7dhw4Y+\nffpcd911H3/8ccnxbdu2jR49+rnnntuwYUP79u3dXGFV7dy5Mz09XdKaNWuq/qy8vLwbb7yx\n3E5bO/New5Jb/B8/rrQ0hYToqad0/fXergYA4BYluyLMObySI+aewyXvtfdG2JXsjYAvqzDY\n7d+/v1+/foWFhbfeemupu66++urZs2ePGTOmV69e27ZtCw0NdXORVVJQULB27drqPqt169av\nvvpqcXFxJY/5+OOP582bZ+8ItpQdO3TypB58UBMmeLsUAIC7VN6mWnaBep0qLdQqR2am7ehY\nux9/lKToaKWnO7o3vvpK0dEaM0bLlun4ccXEcMiYy1QY7ObNm3f48OF58+alpaWVuisoKOix\nxx47d+7c2LFjFyxYYM6Ted2VV1757bffVvdZwcHBfUrOR5fn6NGj80otJbCMU6ckqXFjb9cB\nAPCyxYvVocMFIzt2qEMHZWbatiCWlJ2t7t1tt//9b9uNtDSVSQoO9u6NxERlZWnbNo0YYTtk\n7KqrtHKl8vKUlmY7ZIxdUWqvwky+YsWKK664Yvjw4RU9YPTo0a1atZo/f75b6qq+0NDQ6Ojo\naFZpVl2vXkpMlKQZMxQUpBEjvF0QAMAnjB8vw7gg55m9EfZUJ1V1e+HKuzfi4tSvH4eMuVKF\nM3Z79uy544476lQ8G1u3bt2uXbt+ZG5x40GGYezatevnn38uKCiQ1LRp06ioqNbmESeolqlT\ndfPNmjhRyckaNkzt2nm7IACANf373woKsm2n9+GHkrRli3JybNvpmXNEDz6oxYu5IFtbFQa7\n33///ZJLLqn8yZdccknlbQeulZ+f/8wzzyxcuPC3334rdVebNm3S0tKeeOKJBg0aeKwev3fj\njbbVsFFR6tvX29UAACyraVPp/AXZ++7T3Lnau9dxQfaxx/T009q3jwuyLlDhhNwll1yyZ8+e\nyp/8448/XmaeA+x++/fvj4uL+/vf/960adMHHnhg6tSpf/vb3/72t79NmjRp8ODBZ8+enTJl\nyo033pifn++ZeryDrUkAAL5qyBAFBenYMaWnKzxcDRuqa1fbmRbffy9Jn36qrCyZm0x07+64\nINu2rSTdfDMXZF3BqEDv3r3DwsIOHz5c0QN27NhRt27dfv36VfQA13rooYfq1au3dOnScu89\ne/bsnDlzgoKCHn/8cZf/0a+++qqkgoICl79y9Rw7ZjRoYDRtajz7rPHxx655zexsQzLGj3fN\nqwEALGTwYEMy8vONRx4xmjc3GjQwbrjB+Ooro6jIePxxIyLCaNTIuPFGY/Nm2+NTUw3J6NHD\nmDbN+PprY/58IzTUCAszJOPKKw3JmDLFaNbMaNTIkIz77zckY+FCwzCMefMMyRg+3JCMd97x\n4t+4qszLlRs3bvR2IeWocMZu2LBhhYWFDz/88Fn7iXEl/P7770OHDj179uwDDzzgtsx5gQ8/\n/HDYsGEpKSnl3hscHDxy5MgBAwa89957nqnHC8ytSYYO1YQJ6tHD29UAACyu8uPIVq7UggXa\nvl1JSbaDK8rtkzB3Nr76akkaNEipqSoqks7vsVJ2gz0On62lCoNdv379evTosXz58q5duy5f\nvtzsVJB06NCh119/PSYmJicn595777377rs9U+iRI0euuOKKyh/TqVOngwcPeqYeL/DM1iRc\n7QUASKppQ2vZUy7s6tVTx46lR+BaFQa7oKCgZcuW3XnnnZs3b05OTm7atOlFF13UpEmT5s2b\np6Wl7d69e+DAgW+//bbHCo2IiNi6dWvlj9myZUtERIRn6vE0z2xNYh5EUViop55Sz55u+SMA\nAH6l8uPIzKC2f79jpOwkXEkkOXerbG/pZs2arV69evXq1YMHD27Xrt2ZM2ckdezY8cEHH9yw\nYcOSJUs82YLat2/fZcuWvfDCC+X24RYVFU2dOnXFihUDBw70WEkeNXWqnn1WkpKTtXy5Hn20\nsgfXeNbNTVd7mQUEEAis9V43ZIhef12S5s1zdEKYV8Xef1+RkQoLU0KCLdKVvH5qRjezkcK8\n1CTJXCe1bZvsJz0tXCidP5cCLlTZWbGmO++888477/RAKZXLyMjIzs4eN27ck08+GR8f37p1\n67CwMMMwCgsLd+/enZOTc+LEicTExEmTJnm7Uveo+tYktTn+1R1XezmOFkAgsNx7nbnATlLL\nlo4TI8zTJswFduaJEWa7a0VPX7ZMkt54Q/Pna8MGPfaY7VzaZ5/VN99o6VKNG6dhw2xnV2Rm\n6rXX3P73sjznwc5HNGvW7IsvvpgzZ86bb775ySefnCtxHHG9evXi4uKGDx8+fPjw4LIn3gWa\nGh//2quXzO2mZ8zQjBlKT9err3qzHgDwI5Z7r7NfRR09Wh06KDZWq1dr6VJJGjFCcXGKi1N2\ntmbNquzpF1+sn39WVJSiorRhg379VS1bSlK7djIvvx09qtxcJSS4/a8TOPwm2EkKCQkZO3bs\n2LFjT506tXfvXrOfo0mTJm3atAmxf7NAtWbdune3bSgktx1E4UfH0S5apL/8RQcOaOxYPf+8\nt6sB4Ff86L2upkp1QkilOyHK6tRJmzZdMHLttdq8+YKRkuvzUHuVrbHzWaGhoVFRUV26dOnS\npUuHDh1IdQ616bG48Ubbc82rvddc4+V6PIzGEQA15kfvdbVQg06IJk1sNzIzNXWqJDVrZhvJ\nyNC8eVKJ9XlVPHwWlfOnGTs452vHv/paPZWw3GUUAJ7jR+91nlX2wPmy6RCu5ZczdqjQjTfa\nLgcsX66NG10z61bLelw+C+gmAXAZBYC7+NF7nTtlZsow1KGDY+RPf5JhqHt3x8jddzMt514E\nO2s5flwzZkjSTTf5+vVEn9oXIDAuowBAde3YcUFQk5Sd7Qhq5rXU11/X+vWOLVFycnTihL78\nUpJiYpSQoK+/9mzRga38KdF9+/ZV/SVatWrlomJQazt22BqNunb16WPHfG1fAC6joCyaaQBn\nzOuqM2eqf3/HligpKYqJsZ0Y9vzzmjRJSUnau9e7lQaQ8oNd69atq/4Shr2tEl5n3wvShdzx\n8eZrC9qqvk0gAoSvffcAfMyQIVq8WF27StL//Z+mT9fq1Zo9W3fdpXff1aFDtpaIVq2UmqpZ\nsy44cwxuVX6ws+z5DX6t5NYk5bJvRCdpxgwdO+aCjejc9PHGgjb4OF/77gH4GHM7CnO/4mHD\nlJhom6urX1+SJkzQp59q7VqlpdmaYdnTxGPKD3ZLliypypOLiorMzeTgE+zXEyUlJzs5dqyK\n3PHx5qadkAEX4rsHAl5mpjIzLxjJyFBGhu22eRG2ZUsdO6b//E916mTbvrhzZ0nq3l2TJ2vM\nGM2apV9+kaQzZy54usk8cAKuVavmiRUrVnTp0sVVpaBCVewzsLdlSYqKck1bljs+3qp17i3g\neTTTwCeZp68eO6b09NKdCmPGOA5v9WSnwuWXS+d3szO3Lza3LDZHzNvHj3uuHqiK+9gdPnx4\nyZIleXl5Z8+etQ+eOnVq1apVhYWFbqsNkjy71qfU1V43Ta2xoA0+jmYa+CTz6mdKihITS3cq\nXHWV4/BWs1PB6dbBLvGvf0nS5Mlat05Hj0pScbEkzZ6tgQN15IgkHTzoiUpg5zzY5eXlxcfH\nHzp0qJwn1607efJkN1SFEry41qe6H290EcIa+O4Bn2Re/YyK0pQpkhyHt8bHa/p0SY7DWz18\n+mp4uLKy9OSTev99ffKJJIWEaOVKvf66XnnFFv7gMc4vxU6aNOnUqVMvvfTS2rVrJWVmZmZl\nZf31r3+NjIxctWrVFPP3C+5Tg4uh48fb/iuvpV279D//I0n79jnfcrOiI7nMWUCX1AMAPstT\n73XJyY7b5tXPPn0cI+bVTw93KowerdhY20dEUZEkjRihuDiZa7VOnrQ956OsVQAAIABJREFU\nzAcvJVuS82CXnZ09atSoUaNGJSQkSLrqqqt69uz53HPPrVq1asiQIRs3bnR/kQHMi2t9zKB2\n4oSkKl2KMmcWhw7VhAk+vYUeAPizyEjHbXMOr+SIeQXWfvqq73jxRS1eLEnR0Xr7bR0/rogI\nbdmi/v0VHa3XX1d+viIj9d136tat/Pw3cKCCgtSyperVU0iIQkPJheVzHuz279/fvn17SXXq\n1JFUbF4/l6699tpRo0ZNNfuY4SZe7DMwg9odd0jn18dWji5CAHC/sovnPLOcrlqmTVNQkG1m\nwPTtt7YbZ8+qYUNJ2rVLdepo716FhOiTT7RwoX77TefO2T5MUlIUGamsLL3yirZuVUqKPv9c\nktq0Ub9+Cg1V/fq28YEDFRqqlSu1YIG2b1dSki/mWk9yHuwaN2588OBBSSEhIWFhYT///LP9\nrs6dO2/atMmN1cGL5w+a/22Z//05ZYEuQi4ZA0D1lXvmmDmJaG+3e/RRR8i75Rb96196+20F\nB9s+ZyZNUlyc+vVTaqrjYeZSwthYpaaqd2/t2WPLr3FxWrJEw4fr99+VkKA9exQaqunTHa9w\n8GCgb4bsPNglJia++uqrn3zyiaSrr756zpw59k7YdevW1Tf3IoTF2IPa229Lcux7XBF2MAGA\nknzqOGyPM68Rh4fb/u9NN6lNG9vtqVNtISw21jZiv5RsLhA0lV1KaL6COW4+8qKLJB9YYuhr\nnAe7iRMnHjly5IknnpD08MMPb9q0qXPnzsnJybGxsfPmzbv99tvdXyQ8zh7Ubr5ZkuO/v4p4\ncWYRAHxNRc1kFpKZqYceumAkI0OlFmf9139p3jzb7aZNbTfsF44vu8x248knFRSk5s01erTj\nuf/934qIUEiI6tfXjBmSZO63ZqbAklef/WKJoSc53+4kPj7+s88+y8nJkfTAAw/s2LFj5syZ\ny5cvDwoK6t2798yZM91fJKrM6bFjVWTf7qFVK0lq3twFrwkAAYIj6SRJkZHKy7PdDgoqfW+d\n8zNL5vK79u3VqpW2bLENZmcrNlY33KC1a1VcrLNnZa788oslht5VpZMn4uLiHn30UUlBQUHP\nPvvs0aNHd+3aVVRUtGLFiksvvdTNFcKKWNAGwMJoJpNU5cgVFiZJXbpo5EjHYGGhwsO1fLmG\nD9fp05Js/wunqnGk2P79+7ds2bJ+/foff/yxUaNGDRo0cF9ZAOBNfPdAjbmtmSwzU4ZxQadC\nRoYMQ927O0bS0mQYGjTIVX+mJ7RsKV24qM5kLp4rufAOVVGlYDdv3rx27dpFRER06dLl1ltv\nveaaa5o3b96pU6clS5a4uz54WatWfLwBQDUETDOZq4JmaKh04VI5U9nldKgK52vsXnnllZEj\nR9avX79Hjx6RkZGNGjU6fvz4jh07cnNzBw8eXFxcfP/993ugUPgu8ySxAG9DAgATR9JVk7n8\njsVzruI82M2cObNnz57//Oc/m9p7WiRJu3btuuOOO2bMmEGwC2hm81dIiNLS9Npr3q4GAOBl\nQ4bYDpmYPFnr1qmgQDExtg3q7rxTN9+s48cVE+PIbazVdy3nl2Lz8vImT55cKtVJateu3dix\nY3fu3OmewnCej6/1sZ8kNmyYt0sBAHhfSIjtRni44+iIAwck6eKLHUdEfPNN6Sempdk2THnw\nwdJ3PfKIJC1ebLvya17hfecdKywxdDnnwa5p06bBwcHl3hUcHExXbKDzu+avwN41FADczdyd\n+NFHNXOm4+iI48fVv7/eestxRMTvv+vuu71dqxU5D3b33HPPBx98UO5dq1atSklJcXVJ8B8l\nm78SE5We7rszi6YA2DUUAHxB2aMjyh4RcfKkZ2sKDM7X2D399NN9+/bNy8sbNGhQVFRUw4YN\ni4qKfvjhhzfeeKO4uHjUqFH79u2zP7iVuZ8tLKAqex1Pnaqbb9bEiUpO1rBhatfOI5XVAruG\nAoBHlOxyNefwyh4R8ccfnq0pMDgPdhEREZJycnIWLVpU9t4oM4efZ7jk2AP4C79r/vK7C8cA\n4J+q0uX6yCNat+6CkYwMZWRI0htv2EbS0pSWJklz52rIEA0erPx8jR+v99+3tWXMnq3oaE2c\nqGXLbG0ZL72kLl1c/hfyG86DXd++fevXr++BUgD36tVLH30kSTNmaMYMpafr1Ve9XRMAoKrM\ntoyUFCUmKitL27ZpxAilpCgmRlddpZUrlZentDQlJWnv3sDdLcV5sFu+fLkH6gDczu8uHAMA\nSjAv6UZFacoUSYqN1erVWrpU8fG2Bd5xccrO1qxZys1VQoI3S/Wi8oPdgQMH6tevf9FFF5m3\nK3+JFi1auL4uwOX87sIxAD9VlTXKqKmqtGUE8pb55Qe7li1b9uzZMysry7xd+Uuwrg4AAHhG\nVdoyzpzxbE2+pPxgN3DgwGuvvdZ+24P1AAAAVIjDxypXfrBbsmRJubcBAAAqkZmpzMwLRuy9\nrnb2Xle4nPMNik3ff//94cOHS/7fLVu2uKck+AP7+Q0vv+ztUgAAgI3zYHfmzJmHHnooOjr6\nu+++sw+uX7++S5cuDz744DlzNToCSsnzG+LjvV0NAACwcb7dyYsvvvjGG2/cddddbdu2tQ/e\nfvvtAwcOnD9//rXXXvv444+7s0L4nlLnN4wZ4+2CAACAVJUZu/nz5999992rVq1qV2Lfr44d\nOy5ZsiQpKemll15yZ3nwSZzfAACAT3Ie7H766af/+I//KPeuW265Zffu3a4uCb6tVy8lJkrS\njBkKCtKIEd4uCAAQEDIzZRjq0MExkpEhw1D37o6RtDQZhgYN8nx1vsL5pdgmTZrk5eWVe9f/\nb+/uA6Kq8z2Of0dgEAVBTVEEH0hSV6+WoknCYmXpailRJmnaxbhXcGuVvVKLt8SHNFjteXWz\ndr2lLWambF5ddWtXSU0vpq1raYX4hAnakpCIiOLcP45OyMMAwszvnDPv118zZw5nvjMDnM/8\nns7x48fbtWvXzBVB5wx9/QZWDQUAmFr9LXZjxoz54x//+Je//KXqxsuXL7/99ttvvfXW/fff\n77TaoEsREdda7LTrNwwYoLogAAB+MnGiWCxSXCzTpklgoLRqJUOHSk6OlJXJzJnSpYv4+spd\nd8n+/aoLdY76W+xeeOGFzZs3jxkzpmvXrr169fL29i4uLj506NAPP/zQuXPnF154wQVVAgAA\nNITVKiIyfrxERcmWLfLPf0pioowfL/37S9++smGDHD8uCQkyerTk55twceP6W+w6d+78xRdf\nJCYmXrhw4eOPP964cePOnTs9PDz+4z/+Y+/evV27dnVBlQAax77QYEqK6lIAwKW064yFhcmc\nOXLHHfLEEzJ2rJw8KS1bSnq6DBokDz8sTzwhZ87I3r2qa3WC+lvsRCQwMPD3v//9smXLCgoK\nLl682KlTp9atWzu7MgA3SVto0GqVBQtk8GDV1QCAArGxP90OCxMRGTfupy29eomIFBS4tiaX\naOiVJ0TEYrEEBQXdeuutpDpAd6o20WkLDU6aJKmpMmKE6soAQIEuXX66rbXhVd2i9cBevuza\nmlyi/hY7m8324Ycfrly58tSpU5drew+qXpECgALVmuhYaBCA26s5eM58w+lqVX+we+mll1JS\nUkSkVatWXm7yrgDGUvVaIKNGydatIiIZGZKRIdOmyZtvqq4PAOAi9Qe71157beTIkcuWLQsN\nDXVBQQAarWoTnaEXGgQANE39Y+zOnDkzb948Uh1chxmdjVLtWiDvvstCgwDgtupvsQsMDLSx\nWD+qcur1G5jR2Vg1m+jOn1ddEwBAjfqD3WOPPbZq1aqhQ4e6oBrghuFiaIiICKmsFLneRCci\nO3eqrQgAoEr9XbFz5szJy8ubNGnS1q1bDx8+fKQGF1QJA2tsvyozOgEATfCHP4jNJj17/rRl\n7lyx2SQy8qctCQlis0lcnOurc7r6W+z8rp9iMzMza92BjlrUqbH9qszoBACgCRrUFWu1Wj09\nG3SNCuAGje1XZUYnAABNUH9cq6uhDqhfY/tVaw4XAwAADVZ7sCssLPT29m7btq122/EhOnXq\n1Px1wQToVwUAwLVqD3adO3ceOXLkli1btNuOD8EYO9SOflUAAFyr9mA3YcKE22+/3X7bhfXA\nROhXVcWpCw0CAHSs9mD3/vvv13obAAAAulX/5IkNGzbceuutffv2dUE1AG4GTXQAABFpyALF\nEyZM2LhxowtKAQAAQFPUH+wiIyOzs7OvXr3qgmoAAABw0+rvin3vvfeSk5PHjBkzZcqU2267\nzd/fv9oOPatetgMAAACK1B/s7MvUaauf1MRyJ2hODBcDAOBm1R/sJkyYYLVavby8LBaLCwoC\nAADAzak/2LHcCXTq/vvl449FRDp1koIC1dUAAKBePZMnLl26lJOTs3379novLAbUQutXTU9v\n/iMfOnQt1YWFyQMPNP/xYQ6ZmRIcLJ6ekpKiuhQAcAVHwe7dd9/t1KnTnXfeeffddwcFBU2c\nOPH8+fMuqwxw5MMPRUTatZNvv5W331ZdjZsxSloqKZGEBCktlQULZORI1dUAgCvU2RX76aef\nxsfHe3h4jBw5sn379nv27Fm9evXFixezsrJcWR9Qu5ISEZGWLVXX4X60tGS1yoIFMniw6moc\nys2VixclPl5SU1WXAgAuUmewW7JkicVi+fvf/x4VFSUiFRUVcXFxWVlZX375Zb9+/VxYIVCD\nt7dUVIiInD4tFou0bSs//KC6JrdhoLRUXi4i4uenug4AcJ06u2L37Nlz//33a6lORKxW69y5\nc0Xk008/dU1lQJ1+/WsJCxMRadlSYmNl9mzVBbkTo6SlUaNE+/eVkSEWiyQmqi4IAFyhzmBX\nVFR02223Vd2i3S0qKnJ6UYBjL74oDz4oItKunaxbJ7NmqS7IbRgoLaWlyaJFIiKxsZKVJUlJ\nqgsCAFeoM9hdvXrVx8en6paWLVuKSGVlpdOLgosZZSw8lDNQWoqIuJZBw8IkJkYGDFBdEAC4\nQv3r2MHkDDQWHspFRIj21U5LSwAAnalnHTuYnzYWftIkSU2VESNUV1MDrYkAADSYoxa7nTt3\nahMmqtq+fXu1jTX3gZHoeSw8rYkAADSGo2C3a9euXbt2VduYnZ2dnZ1ddQvBzsBGjZKtW0VE\nMjIkI0OmTZM331RQRmamPPOMFBZKcrIsXvzTdgOtrAEAgA7UGexWrVrlyjqgRlqaREfL7NkS\nGyuTJ0uPHgpqcNAsp+fWRAAA9KfOYPf444+7sg6ooYex8HU1y+mkNREAAONg8gRUq6tZzkAr\nawAAoA8EOyjlYMFbx+uQvfSS2Gzy3XcurBUAAL1jHTsopYdBfjCryEix2VQXAQAuRbCDUnoY\n5IdGIS0BgI7RFQsAAGASxm6xq6ioOHDgQGlpaffu3XvQiwcAANybYVrsXnjhhW3btlXdsnz5\n8k6dOg0ZMuSee+4JDQ0NDw//xz/+oao8AAAA5QwT7J5//vmt2qpmIiKyadOmxMTEsrKyhx56\naNq0acOGDdu3b9/w4cPz8vIUFglz4nq1AACDMGpXbHJysr+//+7du/v06aNtWb9+/SOPPLJw\n4cIVK1aorc1gGAvvGNerBQAYhyGD3ffff5+bmzt79mx7qhOR2NjYcePG/fWvf1VYGEyI69UC\nAIzDkMGuvLxcRKqmOk2/fv02bdqkoiI4hx5aE7leLQDAOAwzxq6qoKAgf3//U6dOVdt++vRp\nP07AaEYOLowBAID+GCnYnTx58vPPPz9y5Mi5c+emT5/+xz/+sayszP7o119/vWbNmmHDhims\nEDdDa5ZLT1ddR224Xi0AwFCMFOxWr149ePDgsLCwDh06vPjii0eOHNm8ebP2UGZmZnh4+MWL\nF59//nm1RcJUHF+vFnrD/GUAbs8wY+z+53/+p7iKkpKS4uLitm3bao8WFxcHBAS8//77g5m3\nCLgn5i8DgIGC3b//+787eHTKlCmJiYktWhipARJAc2L+MgAYKNg55uvrq7oEAEoxfxkAjDXG\nDqgfo6zcE/OXAUBEzBTs8vLyRowYMWLECNWFQB1tlFVpqSxYICNHKiiAWKkK85cBQERM0xUr\nIufPn//b3/6mugoopXaUFYP3FYqIkMpKkevzlwHAXZkn2PXu3fvgwYOqq4BSakdZMXgfAKCa\neYJdy5Yt+/Xr19ifqqys3LRp06VLlxzss2/fvibUBVcZNUq2bhURyciQjAyZNk3efNOlBTB4\nHwCgmvGCnc1mO3bs2NGjR8+fPy8i/v7+YWFhISEhN3e0/Pz8//zP/6yoqHCwjxb7bMovWgrH\n0tIkOlpmz5bYWJk8WXr0aJ7DNvB6ta6MlZmZ8swzUlgoycmyeHFz7gwAMDgjBbtz584tXLhw\n1apVZ8+erfZQ165dExISZs2a5ePj06hjdu/evbCw0PE+y5cvT0xMtFgsjSsXLqZ2lJWTYmVN\njRrJx7A/AHAzhgl2BQUFw4YNO3bsWFhY2OjRo7t169a6dWsR+fHHH/Py8rKzs+fMmbNu3bpt\n27bZL0cBuI7LYmWjRvIx7A8A3Ixhgt3zzz9/6tSpDz74YPz48TUfraysXL58+VNPPTVv3rxX\nX33V9eUBLtKokXwM+wMAN2OYdew2bdo0efLkWlOdiHh4eEyfPv3RRx9dv369iwsDXKdRy/Cy\nZi8AuB/DBLuioqJbb73V8T59+vQ5c+aMa+pBdazN6wKNWobXrdbszcyU2FgRkW3bVJcCACoZ\npis2KCjowIEDjvf54osvgoKCXFMPbsAgfddo1Eg+91mz1/7rt2gRv34A3JxhWuxiYmLWrl27\nZMmSWtecu3DhQlpa2kcffTRhwgTX14Zrg/QnTZLUVOGqbnAxfv0A4DrDtNjNnTt3x44dKSkp\n8+fPHzJkSEhIiK+vr81mKy0tPXHiRE5OTllZWVRU1HPPPae6UrfEIH0oxK8fTIlFKHFTDBPs\nAgICdu/evXTp0pUrV27fvr1S62MSEREvL69BgwZNnTp16tSpHh4eCot0U8ov+QB3xq8fTInx\nLbhZhgl2ImK1WpOTk5OTk8vLy/Pz87UrT7Rp06Zr165Wq1V1dW7MZWvz1quBV4mAmejn1w9o\nRixCiZtlpGBn17Jly7CwMNVV4Dr3GaTvGLFSCX79YEoMMMDNMszkiVotWbIkMjJSdRUAADQf\nFqFEExg72B05cmTXrl2qqwAAoPm41SKUaG6G7IoFAMC0GGCAJiDYAYbSqJF8DPsDzI0lUVAD\nwQ4AAANiSRTUxthj7NLT0/Pz81VXAQCAy3HNFdTG2C12AQEBAQEBqqsAAMDlWBIFtTF2ix10\nITNTYmNFRLZtU12KUpmZEhwsnp6SkqK6FABmx5IoqAPBDk2jDfKoqJBFi2ThQtXVqKO9D6Wl\nsmCBjBzZzAcnMjqmzRFJT1ddB+BCLImCOhi7Kxbqqbrujd7mgjnvfWB8NICaWBIFdSDYoWmU\nDPLQYdZx3vvAJSMBAA1GVyyaQNUgD73NBXPq+8D4aMANMcAAN4tghyZQNchDb1nHee+DocdH\nMzQQAFyOYIcmiIi4Fju0QR4DBrjiSXWYdZz3Phh3fLRTZ5MAAOpAsIPR6CTruKY5Skl0bhZ6\n6y4HAPfA5AkYjR7mgulw9obe6K27HADcAy12QOPRHOWYDrvLAcA9EOyAxqM5yjGddJcDgPsh\n2AGNRHNUvYw7NBAwEJZEQW0IdkAj0RwFANArgh3QSM3SHMUabwDMhP9pusGsWMDlmFQLwEz4\nn6YnBDs0jTbIQ4lDhyQ4WAoLJTlZFi9WU4Ndo94HLv8KwEz4n6YndMXCsDZvNuqFDRo1qZbx\n0QCUc9zTykIBekKwgwFFRsrevXLliiFXkmNSLQBjcXyFQP6n6QxdsTAm435BTEuT6GiZPVti\nY2XyZOnRQ3VBAOCQ455W/qfpDC12MCBDf0FkjTcAxuL4izT/03SGYAcDYiU5/WNoIGAOhv4i\n7ZboioUBRURIZaXI9S+IAAAnoafVaAh2QOMpXOQFAFyJL9JGQ1csAACASRDsAAAATIJgBwAA\nYBIEOwAAAJMg2AEAAJgEs2IBl2NSLQAz4X+antBiBwAAYBK02MGY+IIIAEANtNgBANxMZqYE\nB4unp6SkqC7FCLhCoKEQ7AAj4DwENJeSEklIkNJSWbBARo5UXQ3QzAh2gO5xHgKaUW6uXLwo\nkyZJaqqMGKG6GuPja6fOMMYO0D3tPBQfL6mpqksBjK+8XETEz091Haagfe20WmXBAhk8WHU1\nEKHFDjAAzkNAcxk1SqKiREQyMsRikcRE1QUZHM2f+kOwA/SN8xDQjNLSZNEiEZHYWMnKkqQk\n1QUZHF879YdgB+gb5yGgGUVEXPumFBYmMTEyYIDqgoyMr526xBg7QN8iIqSyUuT6eQgAdCIt\nTaKjZfZsiY2VyZOlRw/VBUGEYAcAAG4GXzt1ia5YAAAAkyDYwVxYUQkA4MboioWJsKISAMC9\n0WIHE3HzFZVorQQAt0eLHUzEnVdUorUSAECLHczDzVdUcmprJW2BAGAQtNjBLNx8RSXntVbS\nFgiTiYwUm011EYCzEOxgFiZeUane89CoUbJ1q4hIRoZkZMi0afLmm8327FpbYHy8pKY22zEB\nAM5BsAOMz6mtle48chGAYzR/6g9j7ADjc97lL9185CIAGA3BDkDd0tJk0SIRkdhYycqSpCTV\nBQEAHCHYATUwCdTOeW2BAAAnYIwdcCMmgQIADIsWO+BGbn75CgBwATpGnIYWO+BGTAIFAKei\nY8SZaLEDqjDuJNCPPxYR+e1v+foLQO/oGHEmWuxgIk1fUcmgl68oKZGMDBGRn/9cRo5UXQ0A\nOETHiDPRYgdUoc9JoPUORsnNlUuXRESGDuXrrwEwugjuzLgdIwZBix2gbw0ZjKJ9/X32WUlP\nd2VpuBmMLoKbM2jHiHHQYgfoW72DUfj6ayyMLoKb02fHiInQYgfoW72DUZz99ZdrQTYvRhcB\ncCZa7AAda0hrHF9/b46SgW40rwJwMlrsAB1jMIqTqBroxgcKwMkIdoCORURIZaXI9da4m5aZ\nKc88I4WFkpwsixc3V3UGpg10i4+X1FSXPm9zfaAAUAeCHWB2TMOsiYFuAEyKMXaA2TENsxoG\nugH6wbKOzY0WO+BG5psESutUNQx0A3SC/gQnoMUOMDVap2piHjGgE/QnOAHBDnChZu900A4Y\nHV3nDmlpsmiRiEhsrGRlSVJS8zwvANw0rWMkPZ3+BGcg2AGuonU6lJbKggUycmRzHjAhoc59\naJ0CoE/0JzgHY+wAV2n2JTbsB1y6VJYvb55jAoBrMNrVOQh2gKs0e6cDvRgAjItlHZ2DrljA\nJW6608E+GKW5Dgi16vpAAaA5EOwAl2j2SQzMigAA1EBXLOASzd7pQC8GAKAGWuwAAABMgmAH\nwAl0fpkgBroBMCm6YgE0Ny4TBACK0GIHmEVdjWSub53iMkEAoIjxWuxsNtuxY8eOHj16/vx5\nEfH39w8LCwsJCVFdF6CUrhrJWGAPABQxUrA7d+7cwoULV61adfbs2WoPde3aNSEhYdasWT4+\nPkpqAxRr9sta3LRRo2TrVhGRjAzJyJBp0+TNNxWXBECftP4ENCvDBLuCgoJhw4YdO3YsLCxs\n9OjR3bp1a926tYj8+OOPeXl52dnZc+bMWbdu3bZt29q2bau6WMDlGttIlpkpzzwjhYWSnCyL\nFzdnJVwmCADUMUywe/7550+dOvXBBx+MHz++5qOVlZXLly9/6qmn5s2b9+qrr7q+PEClxjaS\nObXflgX2ms55sRuA2Rkm2G3atGny5Mm1pjoR8fDwmD59+qeffrp+/XqCHXSqWqdD00/e9gPu\n3t24RjL99NuiJl0NlwRgNIYJdkVFRbfeeqvjffr06ZOVleWaeoAmad6Td2MbyZjcoGfEbgBN\nYJjlToKCgg4cOOB4ny+++CIoKMg19QBNonBBkFGjJCpKRCQjQywWSUx06bOjXsRuAE1gmGAX\nExOzdu3aJUuWXLp0qeajFy5cSEtL++ijjyZMmOD62oBGU3jyTkuTRYtERGJjJStLkpIU1IC6\nELsBNI1humLnzp27Y8eOlJSU+fPnDxkyJCQkxNfX12azlZaWnjhxIicnp6ysLCoq6rnnnlNd\nKXCjmmPp1C4IwuQGPWNOMYCmMUywCwgI2L1799KlS1euXLl9+/ZK7cwkIiJeXl6DBg2aOnXq\n1KlTPTw8FBYJVFfrWDpO3qgLsRtA0xgm2ImI1WpNTk5OTk4uLy/Pz8/XrjzRpk2brl27Wq1W\n1dUBtal1IDwnbwCAcxgp2Nm1bNkyLCxMdRVAAzAQHgDgQoaZPAEYj9sOhNcW2EtPV10HALgd\n8wS7vLy8ESNGjHDxyhGAA8w/BQC4liG7Ymt1/vz5v/3tb6qrAKpw5Vg6rqUNADBTsOvdu/fB\ngwdVVwEAAKCMeYJdy5Yt+/Xrp7oKAAAAZcwT7ESkqKjo3LlzPXv2bPiPnDlz5sknn6z1ahZ2\n3333nYjY6OeCadBvCwAmZapgt3jx4oyMjEYlsNatWw8cOLCiosLBPu3btz98+LC3t3eTCwSA\n+hC7ATSBqYLdTfD19Z0/f77jfT777LM1a9a4ph64C07eAAAnMM9yJwAAAG7OMC124eHh9e6j\nDYYDAABwT4YJdl988YWIeHl5OdjnypUrrioHAABAdwzTFZuSktK6desvv/yyvG6zZs1SXSZw\nIy6uBQBwIcMEuwULFvTs2fOxxx67fPmy6loAAAD0yDDBzsvL609/+tNXX301e/Zs1bUAAADo\nkWHG2IlInz59CgsLHQyk+8UvfhEQEODKkgAAAPTDSMFORNq0aePg0ejo6OjoaJcVAwAAoCuG\n6YoF4AqZmRIcLJ6ekpKiuhQAQKMZO9gtWbIkMjJSdRWAWZSUSEKClJbKggUycqTqagAAjWaw\nrthqjhw5smvXLtVVAGaRmysXL0p8vKSmqi4FAHAzjN1iB6AZ2LvSnCM6AAAU4klEQVRfX35Z\nRMTPT3VBAICbRLAD3Ju9+zU0VFavFhHJyBCLRRITVVcGAGg0gh3g3rTu10mT5N13ZdEiEZHY\nWMnKkqQk1ZUBABrN2MEuPT09Pz9fdRWAkZWXi4j4+UlEhERFiYiEhUlMjAwYoLYuAMBNMHaw\nCwgICA4OVl0FYFijRl0Lc1r365IlqgsCADSJsYMdgCZJS7uh+zUmRnVBAIAmMfZyJwCaJCJC\nKitFrne/7typuiAAQJPQYgcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJsGsWADXRUaK\nzaa6CADAzaPFDgDUycyU4GDx9JSUFNWlADADWuwAQJGSEklIEKtVFiyQwYNVVwPADAh2gHuj\n+1Wh3Fy5eFHi4yU1VXUpAEyCrlgAUKS8XETEz091HQDMg2AHACqMGiVRUSIiGRlisUhiouqC\nAJgBwQ4AVEhLk0WLRERiYyUrS5KSVBcEwAwYYwcAKkRESGWliEhYmMTEqK4GgEnQYgcAAGAS\nBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AA3kJkpwcHi6SkpKapL\nAQA4EQsUA2ZXUiIJCWK1yoIFMniw6mpQRWSk2GyqiwBgKgQ7wOxyc+XiRYmPl9RU1aUAAJyL\nrljA7MrLRUT8/FTXAQBwOoIdYGqjRklUlIhIRoZYLJKYqLogAIATEewAU0tLk0WLRERiYyUr\nS5KSVBcEAHAixtgBphYRIZWVIiJhYRITo7oaAIBz0WIHAABgEgQ7AAAAkyDYAQAAmATBDgAA\nwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMggWKAbOLjBSbTXURAABXoMUOAADAJAh2AAAA\nJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGw\nAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASnqoLMACr1Soi3t7eqgsBAAB6ocUDvbHYbDbVNRjA\ngQMHrly5oroKJ6qoqLjrrrvS0tJ69uypuhZcc/ny5alTp/Kh6Ir2ocyZMycsLEx1LbiGD0WH\ntA9lxYoV/fv3V12Ls3h6eg4YMEB1FbUg2EFEpLy83MfHZ/fu3UOHDlVdC67hQ9GhS5cutWz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@@ -1148,7 +1024,16 @@ "plot without title" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" }, { @@ -1158,7 +1043,16 @@ "plot without title" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" }, { @@ -1168,7 +1062,16 @@ "plot without title" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" }, { @@ -1178,7 +1081,16 @@ "plot without title" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" } ], @@ -1188,7 +1100,7 @@ "design <- model.matrix ( ~ sex)\n", "y <- DGEList(counts=ijc, group = sex)\n", "y <- calcNormFactors(y, method=\"upperquartile\")\n", - "y_voom <- voom (y, design=design, plot = TRUE)\n", + "y_voom <- voom (y, design=design)\n", "Gender <- substring(sex,1,1)\n", "plotMDS(y, labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", " gene.selection=\"common\")\n", @@ -1211,66 +1123,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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69Uz69h1uh572jmxccNAHFDxw55IFZRid5hBDJKdXwcAKCjYwdkgDZSljx2NGOV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S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\naltUQ2nVGx3Z0T2KqzatQjhtpuTVRrUH0aZdKEeWrhWoF1HeJ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sn2ckwLoCbhItk24r+O42wGBgNH7N6NE098t0YN99RT3VmzYHhqlRCO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\nWIlEsmHDht6OifI+eRKJJCoqysrKisvlGhoaz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- "text/plain": [ - "Plot with title “voom: Mean-variance trend”" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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Zo1JZ0+ffrAgQMff/zxxIkT33777U2b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Au4QTJVFgynFtPyAsAECHZAcPFz0qrh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9msnRA0TAEA3/Hex2727NlDhw7Ny8srLCw8WREDqoQN2K4BG8ox5ewpGx1GB9hZ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\nOTwDlMNmT8ChWIBYlgV/GhbMmg7jPdjFxcU1aNDAgFIAg8TEaO1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2A0qpWlpa2ubNmydNmjR9+vS+ffu2b9++SZMmbre7oKDg22+/zcnJ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7tt27b9+OOPFT6VkZGxatUqX5cEBDqatHzO+EszuaYTgCm8B7vY2NgPP/ywwqe2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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sex <- factor(reduced.meta.data$sex,levels=c('male','female'))\n", "design <- model.matrix(~ sex)\n", "y <- DGEList(counts=sjc, group = sex)\n", "y <- calcNormFactors(y, method=\"upperquartile\")\n", - "y_voom <- voom (y, design=design, plot = TRUE)\n", + "y_voom <- voom (y, design=design)\n", "\n", "Gender <- substring(sex,1,1)\n", "plotMDS(y, labels=Gender, top=100, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", @@ -1299,321 +1160,61 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 42611
  2. 191
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 42611\n", - "\\item 191\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 42611\n", - "2. 191\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] 42611 191" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "sex\n", - "female male \n", - " 81 110 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 6 × 2 of type dbl
interceptsex
110
210
310
410
511
611
\n" - ], - "text/latex": [ - "A matrix: 6 × 2 of type dbl\n", - "\\begin{tabular}{r|ll}\n", - " & intercept & sex\\\\\n", - "\\hline\n", - "\t1 & 1 & 0\\\\\n", - "\t2 & 1 & 0\\\\\n", - "\t3 & 1 & 0\\\\\n", - "\t4 & 1 & 0\\\\\n", - "\t5 & 1 & 1\\\\\n", - "\t6 & 1 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 6 × 2 of type dbl\n", - "\n", - "| | intercept | sex |\n", - "|---|---|---|\n", - "| 1 | 1 | 0 |\n", - "| 2 | 1 | 0 |\n", - "| 3 | 1 | 0 |\n", - "| 4 | 1 | 0 |\n", - "| 5 | 1 | 1 |\n", - "| 6 | 1 | 1 |\n", - "\n" - ], - "text/plain": [ - " intercept sex\n", - "1 1 0 \n", - "2 1 0 \n", - "3 1 0 \n", - "4 1 0 \n", - "5 1 1 \n", - "6 1 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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VYN+gxDpAAQbAh28LWgbV/me/45RApY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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "ijc_sex_results_refined\n", - "FALSE TRUE \n", - "42208 403 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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DBh27ZthYWF33///datW7du3bpnz57CwsLNmzc/9NBDpDoAVcGRR1SdY8AylkRs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\n4Lb4+KM4dMWivMo9vAyVyNalGxcng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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sex <- reduced.meta.data$sex\n", "\n", - "design <- model.matrix( ~ sex )\n", - "\n", - "colnames(design) <- c(\"intercept\",\"sex\")\n", - "\n", - "dim(ijc)\n", - "table(sex)\n", - "head(design)\n", - "\n", - "y_ijc <- DGEList(counts=ijc, group = sex)\n", - "y_ijc <- calcNormFactors(y_ijc, method=\"upperquartile\")\n", - "\n", - "y_ijc_voom <- voom (y_ijc, design=design)\n", - "\n", - "Gender <- substring(sex,1,1)\n", - "\n", - "plotMDS(y_ijc, labels=Gender, top=1000, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", - " gene.selection=\"common\")\n", - "\n", - "plotMDS(y_ijc_voom, labels=Gender, top=1000, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", - " gene.selection=\"common\")\n", - "\n", - "fit_ijc <- lmFit(y_ijc_voom, design)\n", - "fit_ijc <- eBayes(fit_ijc)\n", - "\n", - "ijc_sex_results <- topTable(fit_ijc, coef='sex', number=nrow(y_voom))\n", - "ijc_sex_results_refined <- ijc_sex_results$adj.P.Val < 0.05 & abs(ijc_sex_results$logFC) > 1.5\n", - "\n", - "table(ijc_sex_results_refined)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Differential analysis as_event:sjc \n", - "\n", - "Differential Analysis (DE) was performed using voom (Law et.al., 2014) to transform junction counts (reads that were aligned to junctions when an exon is included - ijc, and reads that were aligned to junctions when the exon is excluded - sjc) with associated precision weights, followed by linear modeling and empirical Bayes procedure using limma. In each tissue, the following linear regression model was used to detec secually dimorphic alternative splicing event expression: \n", - "\n", - " y = B0 + B1 sex + epsilon (error)\n", - " \n", - "\n", - "where y is the excluded exon junction count (sjc) expression; sex denotes the reported sex of the subject." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 42611
  2. 191
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 42611\n", - "\\item 191\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 42611\n", - "2. 191\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] 42611 191" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "sex\n", - "female male \n", - " 81 110 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 6 × 2 of type dbl
interceptsex
110
210
310
410
511
611
\n" - ], - "text/latex": [ - "A matrix: 6 × 2 of type dbl\n", - "\\begin{tabular}{r|ll}\n", - " & intercept & sex\\\\\n", - "\\hline\n", - "\t1 & 1 & 0\\\\\n", - "\t2 & 1 & 0\\\\\n", - "\t3 & 1 & 0\\\\\n", - "\t4 & 1 & 0\\\\\n", - "\t5 & 1 & 1\\\\\n", - "\t6 & 1 & 1\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 6 × 2 of type dbl\n", - "\n", - "| | intercept | sex |\n", - "|---|---|---|\n", - "| 1 | 1 | 0 |\n", - "| 2 | 1 | 0 |\n", - "| 3 | 1 | 0 |\n", - "| 4 | 1 | 0 |\n", - "| 5 | 1 | 1 |\n", - "| 6 | 1 | 1 |\n", - "\n" - ], - "text/plain": [ - " intercept sex\n", - "1 1 0 \n", - "2 1 0 \n", - "3 1 0 \n", - "4 1 0 \n", - "5 1 1 \n", - "6 1 1 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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h1gJr78hMqnYQBm1rt34XtUWJhiYtSl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\nVfoLsVvCZ6pw9O7x48c/++yz9evXf/HFF6dOnfJeTbCJgQMLT33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3b9/+gQceiI2NjY2Nvf/++1u3bt2mTZv09PTz58/7rBgrMckRrj7j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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "sjc_sex_results_refined\n", - "FALSE TRUE \n", - "42444 167 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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L04YN2rdPqalKSlJUlG65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\nqM1Jl6x1RisgAEDbCHZwS2oNPmv0e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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "design <- model.matrix( ~ sex )\n", + "\n", + "colnames(design) <- c(\"intercept\",\"sex\")\n", + "\n", + "dim(ijc)\n", + "table(sex)\n", + "head(design)\n", + "\n", + "y_ijc <- DGEList(counts=ijc, group = sex)\n", + "y_ijc <- calcNormFactors(y_ijc, method=\"upperquartile\")\n", + "\n", + "y_ijc_voom <- voom (y_ijc, design=design)\n", + "\n", + "Gender <- substring(sex,1,1)\n", + "\n", + "plotMDS(y_ijc, labels=Gender, top=1000, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + " gene.selection=\"common\")\n", + "\n", + "plotMDS(y_ijc_voom, labels=Gender, top=1000, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + " gene.selection=\"common\")\n", + "\n", + "fit_ijc <- lmFit(y_ijc_voom, design)\n", + "fit_ijc <- eBayes(fit_ijc)\n", + "\n", + "ijc_sex_results <- topTable(fit_ijc, coef='sex', number=nrow(y_voom))\n", + "ijc_sex_results_refined <- ijc_sex_results$adj.P.Val < 0.05 & abs(ijc_sex_results$logFC) > 1.5\n", + "\n", + "table(ijc_sex_results_refined)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Differential analysis as_event:sjc \n", + "\n", + "Differential Analysis (DE) was performed using voom (Law et.al., 2014) to transform junction counts (reads that were aligned to junctions when an exon is included - ijc, and reads that were aligned to junctions when the exon is excluded - sjc) with associated precision weights, followed by linear modeling and empirical Bayes procedure using limma. In each tissue, the following linear regression model was used to detec secually dimorphic alternative splicing event expression: \n", + "\n", + " y = B0 + B1 sex + epsilon (error)\n", + " \n", + "\n", + "where y is the excluded exon junction count (sjc) expression; sex denotes the reported sex of the subject." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sex <- reduced.meta.data$sex\n", "\n", @@ -1658,127 +1259,19 @@ " y = B0 + B1 sex + B2 as_event + B3 sex*as_event + epsilon (error)\n", " \n", "\n", - "where y is the alternative splicing event expression; sex denotes the reported sex of the subject, as_event represents the specific alternative splicing event - either included exon junction counts or skipped exon junction counts and their interaction terms." + "where y is the alternative splicing event expression; sex denotes the reported sex of the subject, as_event represents the specific alternative splicing event - either included exon junction counts or skipped exon junction counts and their interaction terms. Donor is added to our model as a blocking variable used in both the calculation of duplicate correlation as well as in the linear fit." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 42611
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A matrix: 6 × 4 of type dbl
interceptsexas_eventsex*as_event
11100
21100
31100
41100
51000
61000
\n" - ], - "text/latex": [ - "A matrix: 6 × 4 of type dbl\n", - "\\begin{tabular}{r|llll}\n", - " & intercept & sex & as\\_event & sex*as\\_event\\\\\n", - "\\hline\n", - "\t1 & 1 & 1 & 0 & 0\\\\\n", - "\t2 & 1 & 1 & 0 & 0\\\\\n", - "\t3 & 1 & 1 & 0 & 0\\\\\n", - "\t4 & 1 & 1 & 0 & 0\\\\\n", - "\t5 & 1 & 0 & 0 & 0\\\\\n", - "\t6 & 1 & 0 & 0 & 0\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 6 × 4 of type dbl\n", - "\n", - "| | intercept | sex | as_event | sex*as_event |\n", - "|---|---|---|---|---|\n", - "| 1 | 1 | 1 | 0 | 0 |\n", - "| 2 | 1 | 1 | 0 | 0 |\n", - "| 3 | 1 | 1 | 0 | 0 |\n", - "| 4 | 1 | 1 | 0 | 0 |\n", - "| 5 | 1 | 0 | 0 | 0 |\n", - "| 6 | 1 | 0 | 0 | 0 |\n", - "\n" - ], - "text/plain": [ - " intercept sex as_event sex*as_event\n", - "1 1 1 0 0 \n", - "2 1 1 0 0 \n", - "3 1 1 0 0 \n", - "4 1 1 0 0 \n", - "5 1 0 0 0 \n", - "6 1 0 0 0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ijc_names <- as.character(colnames(ijc))\n", - "sjc_names <- as.character(colnames(sjc))\n", + "ijc_names <- as.character(colnames(ijc))\n", + "sjc_names <- as.character(colnames(sjc))\n", + "sample_names <- as.character(colnames(ijc))\n", + "\n", "ijc_names <- paste0(ijc_names,\"-ijc\")\n", "sjc_names <- paste0(sjc_names,\"-sjc\")\n", "colnames(ijc) <- ijc_names\n", @@ -1789,7 +1282,10 @@ "sex <- factor(sex, levels=c('male','female'))\n", "as_event <- c(rep(\"ijc\",dim(ijc)[2]), rep(\"sjc\", dim(sjc)[2]))\n", "as_event <- factor(as_event, levels=c(\"ijc\", \"sjc\"))\n", - " \n", + "\n", + "# we will add donor as a blocking parameter\n", + "donor <- rep(sample_names, 2)\n", + "\n", "design <- model.matrix( ~ sex + as_event + sex*as_event )\n", "\n", "colnames(design) <- c(\"intercept\",\"sex\",\"as_event\",\"sex*as_event\")\n", @@ -1806,27 +1302,45 @@ "source": [ "### Voom, limma's lmFit and eBayes\n", "\n", - "with the duplicate correlation due to the two different counts combined into a single matrix - we should add the duplicate correlcation consensus correlation to the modeling" + "Using sample as a blocking variable, we are able to model the effects of the donor on the results, which improves the power. This topic is discussed in biostars https://www.biostars.org/p/54565/. And Gordon Smyth answers the question here https://mailman.stat.ethz.ch/pipermail/bioconductor/2014-February/057887.html. The method of modeling is a random effects approach in which the intra-donor correlation is incorporated into the covariance matrix instead of the linear predictor. And though as Gordon Smyth states both are good method and the twoway anova approach makes fewer assumptions, the random effects approach is statistically more powerful. \n", + "\n", + "We have a balanced design in which all donors receive all stimuli (which is really in healthy human donors, life and all of its factors!) Our measurement has so many points -- we are measuring in the skipped exon approach, 42,611 junctions! It is not possible to encorporate those measurements into the linear predictor. A two-way ANOVA approach is virtually as powerful as the random effects approach \n", + "and hence is preferable as it makes fewer assumptions.\n", + "\n", + "For an unbalanced design in which each donor receives only a subset of the stimula, the random effects approach is more powerful.\n", + "\n", + "Random effects approach is equivalent to The first method is twoway anova, a generalization of a paired analysis.\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we will model as random effects, represented as a block\n", + "donor <- rep(sample_names, 2)\n", + "length(donor)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "0.926713176949206" + "0.185977569902876" ], "text/latex": [ - "0.926713176949206" + "0.185977569902876" ], "text/markdown": [ - "0.926713176949206" + "0.185977569902876" ], "text/plain": [ - "[1] 0.9267132" + "[1] 0.1859776" ] }, "metadata": {}, @@ -1834,38 +1348,46 @@ } ], "source": [ - "\n", "y <- DGEList(counts=as_matrix, group = sex)\n", "y <- calcNormFactors(y, method=\"upperquartile\")\n", "y_voom <- voom (y, design=design)\n", "\n", - "dup_cor <- duplicateCorrelation(y_voom$E, design=design, ndups=2, block=as_event, weights=y$samples$norm.factors)\n", + "dup_cor <- duplicateCorrelation(y_voom$E, design=design, ndups=2, block=donor, weights=y$samples$norm.factors)\n", "dup_cor$consensus.correlation" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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IZ8Hx/kDG9XoEwEO2Rbpns8MfTeOE0iQ/gKFqB8XGMHxKp00ionjPr96GNOk4bK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\n6TfbRVdMXnHQAJjwvnliyZIljqlOCPHEE0+ce+65YZeEDIv6unv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2yJAhvXr16tGjR3V1dUNDw9tvv7158+ZCoTBr1qwYqhRCvPzyy+PH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O5E+IHvF2yFClic6oK2lluMJriFV1Fosh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\nZBZXOTQmRoifdj0TuDEmShND10q113+G0jRUR00ruLVKXdUQ9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MLOIlJaWlq7du2ioqKBAwfGnQtP7jh2BQUFgUu6CKgPzAL+JFN1uYdq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\n4l//+teAAQPs7e0/++yzOnXqxMbGBgQE5OTkO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MYO8OEEo8jTp/2NuqFXzdWEkPBJALKNYIcGp7zPPSnx2jmLA0CDQrBDw1LMV4pF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yIpgOQasIFTBhQnug+bffonp1kzVp3hQ3zHnKeyl9OmsAACAASURBVMQRCOzQs41u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\nAKAQFDsAAACF+H+B2BOGefKyUQAAAABJRU5ErkJggg==", "text/plain": [ "Plot with title “voom: Mean-variance trend”" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" } ], "source": [ - "y_voom <- voom (y, design=design, plot = TRUE, correlation = dup_cor$consensus_correlation)\n" + "y_dup_voom <- voom (y, design=design, plot = TRUE, block = donor, correlation = dup_cor$consensus.correlation)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1875,7 +1397,16 @@ "plot without title" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" }, { @@ -1885,56 +1416,87 @@ "plot without title" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" }, { "data": { - "image/png": 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u3aab80vHr88ccre9idd97p/aLgt9LSdMstmj5dCQkaN06dOlldEAAAcKs02P3p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\nOHFivfI/0ahQgGz9o6laCQ6stlTVXaGquWI9KUkrV+rYMT36qNa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8XdusmST95jdlN0yMGXNJs7Lyhw+/+aYJ/8RAVGmwO3jw4IgRI06e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Q0AIEAQ7PwJK8YqhmU0AIAAQbDzJ6wY\nAwAAdhDs/A8rxgCgkjiPE2ZFsPM/rBgDAAClItj5H1aMAQCAUhHsAAAATIJgBzNjGQ0AIKAQ\n7AAAAEyCYAcAAGASBDsAAACTINj5E1aMAQAAOwh2AAAAJkGwQ4AaMkRBQTp7ViNHKiJCNWqo\nc2dlZ+v8eY0Zo6gohYWpSxdt3ertQgEAcBrBDgEqJESSkpIUFaU1azRvnrZvV1KSkpMVGqqV\nK/XOO9q1SwkJtPEAAPgNgh38UuXH24xubDExmjhRcXFKSVGfPjp0SKGhmjZN8fHq318pKTp+\nXDk5nq4NAICKIdjBL7lqvC0x0Xo7JkaSHnnEeqV1a0k6etQ7tQEAUF4EO/glV423RUUVf07b\nK0YT3vLGL/eNBQIAYB/BDn6s8uNtRnSzf8VbtQEAUF4EO/gxd4y3uYov1wYAMCuCHfyY+8bb\nKs+XawMAmBXBDgAAwCQIdgAAACZBsAMAmAcHSSLAEewQoLKyZLGoVSvrlcxMWSzq1s16JTVV\nFosGDZL4tAD8BAdJIsAR7ACnOP9p8fvv3q4VCGA0lUGA8+9gd/ny5ZycnHXr1u3fv9/btcCj\nyjveVnnOf1oYf64nawNQDE1lELD8JthNmTJl3bp1tlfmz5/fsGHDTp063XfffS1atOjQocMP\nP/zgrfIQIDh2GHAHlw+G0VQGActvgt2ECRM+/fTTov/85JNPRo0adf78+X79+o0cObJr165b\ntmy59957c3NzvVgkTI9jhwF3cPlgGE1lELCqeLuACkpPTw8PD//222/btm1rXPnwww8HDBjw\n0ksvvfXWW96tDSbGscOAO9gOhkmKi9Pq1Vq6VJ06ado0SYqP16ZNmjVLOTnq0sWbpfLtDj7O\nb0bsbJ04cWLPnj2jR48uSnWSEhMTH3nkkc8++8yLhQGAk1iJX5JfDIbx7Q4+zi+D3cWLFyXZ\npjpDu3btfv31V29UBADlw0r8khgMAyrPL4NdZGRkeHj4kSNHil3/5ZdfatWq5ZWSAKBcWIlf\nUoAPhjGIC5fwp2B36NChzZs3792798yZM2lpaW+++eb58+eL7v3Xv/71j3/8o2vXrl6sELCD\nd22U5BeTj/AMBnHhEv4U7BYvXtyxY8eYmJj69eu//PLLe/fu/ec//2nctWjRog4dOly4cGHC\nhAneLRIoC+/aKInJR5fz/CGXrsIgLlzCb3bFvv3222dtnDt37uzZs3Xr1jXuPXv2bJ06dZYs\nWdKxY0fv1gk7hgzR4sU6c0bPP6+PPlJenmJj9dpratdO48dr2TKdO6fYWM2erfbtvV1rCVlZ\nysq64UpmpjIzb7iSmqrU1DKfwY/2/cFjAnzy0ccVe8s6dUqSduxQZGThW9aJE5K0e/cNqbGS\nGMRFJflNsPvLX/5i595HH3101KhRN93kTwOQAahoyKp7d61Zox07NGqUkpIUG6vbb9fKlTpw\nQKmpSkjQ4cN++fFmP7kuXixJGzZo69bC5Mq7NuDLSn3LSk/X22/f8JY1fryGDSt8y7L/7c4Z\nDOKikkyShMLCwkh1vs/0Ew32J1t79ZKkw4etk628awO+zCtvWQziopIIQ/A0E0802P8YuOUW\nSUpMLP4xwLs24MsL40z8lgVT8pupWIdyc3NHjhwp6YsvvnD+p3755ZcBAwZcvnzZzmNOnDgh\nyWKxVLJCGEw/0WD/Y6BFC4mPAcB/mP4tCyZjnmCXl5f35Zdflven6tWrl5ycbJx4XJbvv//+\n0KFDQUFBlagOVqafaLD/MWBc4WMA8Bemf8uCyZgn2LVp0+bHH38s70+FhoY+/fTT9h8zf/78\n5cuXV7QuBBw+BuCMyu+zBoCSzBPsQkND27Vr5+0qAAAAvMb/gp3FYtm/f/++ffvy8vIkhYeH\nx8TENGnSxNt1IeCUPNwkLEySLlzQmDGFx/JdP2kRABxgEBcu4U+7Ys+cOfPss882bNiwZcuW\nvXr1SkxMTExM7NGjR3R0dNOmTSdPnnzhwgVv14gAUvJwk9OnJenpp62dJE6elKSrV6Xr+/4a\nNLA+g+/s+wMAmIPfjNgdPXq0a9eu+/fvj4mJSUhIaNq0ac2aNSX99ttvubm5GzZsmDhx4gcf\nfLBu3bq6DJLAI0p2kpg0Sfv3q1o1ayeJGTP0/ff61790771erBQAECj8JthNmDDhyJEjS5cu\nTUpKKnnvtWvX5s+f/+STT06aNGnmzJmeLw/OMOVEg+3hJuHhktSzp/WKcXad0YkIgH8x5VsW\nTM9vpmI/+eSTYcOGlZrqJAUHB6elpQ0cOPDDDz/0cGEIcLZHmRjn1dk2uu3bV5JatrReYbIV\nAOA+fhPsTp061dL247E0bdu2PX78uGfqAQwcbgIA8B1+E+wiIyO3b99u/zHbtm2LjIz0TD0A\nAAC+xm+CXd++fZctW/bKK69cunSp5L0FBQUZGRkrVqxITk72fG0AAAC+wG82T2RmZm7atGns\n2LEvvvhip06dmjRpEhYWZrFY8vPzDx48mJ2dff78+e7du7/wwgverhQAAMA7/CbY1alT59tv\nv50zZ8677767fv36a9euFd1VtWrV+Pj44cOHDx8+PDg42ItFAgAAeJHfBDtJISEh6enp6enp\nFy9ePHz4sNF5onbt2tHR0SHGWbEAAAABzG/W2NkKDQ2NiYlp3759+/btW7VqRaqDVxidJFq1\nsl6hkwRgMkOGKChIZ89q5EhFRKhGDXXurOxsnT+vMWMUFaWwMHXpoq1bvV0ocJ1fBjsAADyg\nZOfA7duVlKTkZGvnwF27lJCgK1e8XSsgiWAHAEBZbDsHxsUpJUV9+ujQIYWGato0xcerf3+l\npOj4ceXk3PCDDPXBWwh2gKfxjg/4Amd+Ez/+WLqxc2BMjHS9zYyhdWtJOnr0hidnqA/eQrAD\nPI13fMAXOPObePasJEVEWH/KGMOz7SVoNJsp9tvqzFBfRISOH9e6dXzHgysR7ABPq/DkDgAX\ncuY30Rif27Wr+M862TnQ/lBf3bqSNHYs3/HgSgQ7wDsqMLlTXsz5Ag7Z/00MD5ekEycq+OS2\nA3slh/qMKxERfMeDKxHsAO+w/45f6uROeTHnCzhk/zfxppuk0n4Tu3e3fmVKS5Ok3NxSFueV\nHNgreaVjR+ttN33HQ0Ah2AHe4cw7fiUx5ws4VOHfxKKvTH/+syTNnFnK4ryrVx0/T7161ttu\n+o6HgEKwA0zOA3O+QAAq+srUpYsknTxZyuK8HTscP0/JRpgu/46HgEKwA0zOA3O+QACy/cpk\ncOHiPKDCCHaAyXlgzhcwq5QUSWrY0HolM1OPPy7ZfEFKTVVGxg1XVPbiPMDdCHYA/B77f+F5\nTn5lKnpxrlolST17Wl+c+/ZJ0vPP8+KEKxHsAPg99v/C1xQN9RW9OPv00datmj/f+uK85RZJ\n+p//4cUJVyLYAZ6WlSWLRa1aWa9kZspiUbdu1iupqbJYNGiQ56vzS+z/RQV45jfRzovTOOik\nd29enHAlgh0Ak2D/L3xWqS/OomRZ9OLkOx4qj2AHwCTY/wufxYsTHkOwA0wr0OZ82f8Ln8WL\nEx5DsAMAoBwC7SsT/AvBDhXHGRMAAPgUgh0qjjMmEJj4SgPAZxHsUHGcMYHAFMhfaQi1gI8j\n2KGyOGMCgSaQv9IEcqgF/ALBDpXFNn54nVcWswfmV5oADLWVHKRkpwU8jGCHymIbPwJTIH+l\nCahQyyAl/AvBDgAqIpC/0gRUqA3AQUr4NYIdAKB8AjDUBtQgJfxapYLdmTNnDhw44KJKAADw\nAvur6BYvlqSxY62r6Ew/SAm/Zi/Y7dix46GHHmrWrFn37t3nzp177dq1Yg+YPn168+bN3Vke\nAADuZX8VXa9ekrRvX/FVdKYfpISfKjPYff311506dVq9evWJEye+//770aNH9+jR48yZM54s\nDgAAd7O/iu6WWySpXz9W0cE/lBnsXn755d9//3358uX5+fl5eXn/8z//88033/Tu3bugoMCT\n9cGXsY0fgGnYX0XXooXkaBXd669zejO8r8xgt2PHjuTk5L59+wYFBVWrVi09PX3NmjXbt28f\nOHBgyTlZAPBBbmqTwFcaU7K/1de4Yn8VnfGYch2MQicPuFyZwe7YsWMtjG8o1913331ZWVmr\nV69+5pln3F8YAFQWJ5C5nIlDbeW3+gYHS+U8GIWXKFyuzGAXERHxww8/FLs4bNiwcePGvfba\nazNmzHBzYQBQWZxABs8r18EobnqJMhAYyMoMdomJiR9//PHs2bOv3Pg14aWXXkpJSXnuuefS\n09PPnz/v/goBoFI4gQyVYQxSNmhgvVLWIKUxy1WB05td/hJlIDCQVSnrjokTJ3700UdPPfXU\nihUrPv/886LrQUFBb7/9dnh4+MyZMz1SIQBUSkC1SYDXVWBK1+UvUduBQElxcVq9WkuXqlMn\nTZsmSfHx2rRJs2YpJ0ddupTjmeH7yhyxu/nmm7ds2ZKWltauXbtidwUFBc2aNeuDDz5o2bKl\nm8sDgMoKwDYJ8C9ueokyVh2Yyhyxk3TLLbfMmTOnrHsTExMTbV81AADAZzBWHZjoFQsACGhm\n3erLWHVgItgBAACYBMEOAADAJAh2AAC4gFmndOFfCHaAH+MYUvv4oIWPc9NLdP16SfrtN+s7\nQ1aWJF28aH1nMM49gfkQ7AA/xjGkAEq66SZJeuop6zvD8eOSlJGhzz/XL79o7lwdPChJKSl8\nGzQbx8Huq6++On36dKl3ZWdnf/DBB64uCYCzaJkFBCw7A/bGV76cHK1ZI4tFKSmFZ9pFRqpj\nR0l67z116CBJzz3Ht0GzcRzsunfvvnHjxlLv2rRp04gRI1xdEoDy4RhSIADZGbAPDpakceOs\nEa3onaHo2+Cjj0rSnXfybdBsyjygeO/evXv37jVub9u2LTQ0tNgDLly4sHTp0kuXLrmxOgBO\n4BhSIADZ6RvWsaN27NCgQTp3rrBvWLF3hsREHTgg6YbMV4Rvg36tzGD3/vvvjxs3zrj94osv\nlvWwAQMGuL4oAOXBMaRAwCp1wN7YPFG1avGIVvTOEBVVGOzEt0HTKTPY/fWvf01JScnJyXnk\nkUeGDRt22223FXtAcHBwixYt+vTp4+YKAQBA6So2YM+3QROz1yu2UaNGffr0eeihh9LS0jp3\n7uyxmgAAgDOIaCjGXrAzrFq1ygN1AAAAoJIcBzuLxfL++++/++67R44cuVLalPvOnTvdUBgA\nAAFnyBAtXqwzZ/T88/roI506pWvX9MUXWrRIS5bowgUFBen22/XGG/ruO0mKjdWdd2r2bLVv\n7+3S4RscH3fy6quvDhw4cNWqVbt37z5SGg9UCZgMHSMAlKrYISZ33y1JDz+sr77SwIHKyFBw\nsH76SffdV3gK8YwZHDuHGzgOdrNmzerdu3dubm5BQcHZ0nigSsBkQo7sk5RU78uonz6rTMcI\nWmYB5lD0Ze/77yVp/XqtXq0rV9SypSRdvKh9+/Tvf+tPf1K/fvr9d126pKZNJal371KOneOd\nIZA5DnbHjx+fNGlSixYtPFANEBDOnavyzUZJMZ3qTsy8iY4RAIoG6mrUkKT09MIve+vWFT4g\nLa3wy54R9SRduFB4o1zHzpH5TM9xsIuIiLBYLB4oBQgUe/bo2jVJiVPaq2dP45oPnhHKfDHg\nMUWnDcfFSdJjjxV+2TN6SEjq27fwy57R9VXX41erVtYzTYhokDPBbvDgwe+9954HSgksixap\ncWNVqaKxY71dCjzu4kXj//t4xwg7DYvoKVl55GbXMNd7adFpw1WrFn7Zi462XjG+7OXne6My\n+A/HwW7ixIm5ublDhw799NNPd+3atbcED1RpNufOKTVV+fmaPFm9e3u7GnjWAw+oe3fjZtXb\nYjRqlO2dPnUAlW3DIuaLXY7c7AKmey8t+WWvZk3rFeP94do1z9YEf+P4uJNatWoZNxYtWlTq\nA5ioLbc9e3Thgh57TNebtiGAZGTonns0XpI0d666NPByPY6U2rCoiNfni/2XnUaf06ZJUny8\nNm0qbPTZpYs3S/VdpnsvLfnV7ibHwy/ADRwHu8GDB4eEhFSp4viRcJYxE3c9MSOw3H23rl2T\n/i1JvXqplaPHe5vL54uLHdOVl6fYWL32mtq10/jxWrZM584pNjZQzuUiN1dKgBESKgAAACAA\nSURBVL2XLlgg2fy6paRo5ky99pouX5aktDSlpgbQ7w7K4jiulTVQhwp64AF9+qkkTZ+u6dM1\ncqTeeMPbNQFlcnnDoqIpyO7dtWaNduzQqFFKSlJsrG6/XStX6sABpaYqIUGHD/vW3LQ7+Pg6\nS58WeO+lxivEOJdY0uOP6+23lZSkOnUkaexY3XprAP3uoCzlGOTNy8v76aefOLiusjIyNHWq\nJCUmavlyPfGEtwsCPIqle7Zo9FlxgfdeakzLhoUV/ud//Efh747xmmnePLB+d1AWp4Ldhg0b\nOnToULt27Xbt2n13/ctCnz59vvzyS3fWZlJ33124dj4mRn376o47yv0M5toFhsDEFCQqq/Lv\npT6p2Dlzzz8vi+WGQ0wGDSq8MmhQ4e/OmDHWM0343YHjYJednX3//ffv3r27t82eoxMnTuTk\n5CQkJGzZssWd5aEE0+0CC0xZSrU8/9dAPiOUKUigYurVs97mdwclOQ52L774YsOGDX/++eeF\nCxcWXaxfv/727dsbNmw4efJkN1aHkoxdYEOHaty4orNtAb/DFCRgy5mGEMZonO3YtoHfHdhy\nHOy+++67J554onHjxsWuN2jQYNSoURs3bnRPYShDgO0CAwA4wzjy+vx5SRo8WNWrKyJC9eqp\nRg01aqT69RUWpshINWjA2dcm5zjYnTt3rkmTJqXe1ahRo3zOwPakorNtp09XUFCxs20B16Kn\nJOBHjP3mRTuDW7bU6dOqVk2tW+vkSVWpojZtdPq0rl3TW29x9rWZOQ52DRs23LVrV6l3bdy4\nMTIy0tUloWyBtwsMMCtyM1zLWG8XEVH4n//xH0pM1LFjatWq8Ebz5ho1SqdPq3FjNs+ameNg\nl5CQMHfu3K03DtqeOXPm//2///f2228/9NBDbqsNJZh0F1jA6dZNFkthewEAFTVkUusgWc5e\nqk6/3SJFRxMnJlr3mxfdKNozy+ZZE3Mc7CZNmhQWFnbXXXcZGW7cuHFxcXGNGjWaOnVqdHT0\nRKMbDgAAnhVS1SIp6aOh9NstYhxWLCkqyrpntuhG0Z5ZNs+amFNTsZs3bx4xYsTBgwcl/fDD\nDz/88EOtWrWeeOKJnJyciKJhXwBwAlOQcJUqwRZJMXVPBshh18787gwerMcfl2y2ypa8AXNz\n6oDiBg0azJ0798SJE8eOHduzZ8+xY8dOnDgxd+7cBg18vX+5j2ImDvBhxu7Cs2fFBJ+vi4iQ\nlPjfnYsucNi1Hd27F+6ZHTlSaWmSlJvLq9qEytFSLCgoKCIiolWrVozSATCxom62TPD5BQ67\ndp6xZ3bcOP35z5I0cyavahOq4vARFovl/ffff/fdd48cOXKltP/Bd+7c6YbCUAmLFum553Ts\nmNLTNWOGt6sB/IxtN1tJcXFavVpLl6pTp8Jx9vh4bdqkWbOUk6MuXbxZKsRh1+UREaFdu9Ss\nmW65RW+/rZMnC6etxavaRBwHu1dffXXs2LGSatSoUZVfF99n9BwLCdHkyerY0dvVAP6KbrYw\nh0mT9MUXhbezsyVpwgQNHFh45csvFRam2FjNns2r2iQcT8XOmjWrd+/eubm5BQUFZ0vjgSpR\nDvQcA1yBCb4Kc36RIssZPcB2QCYlRZIOHrSeY/zyy9ZJ2JtuknhV+z/Hwe748eOTJk1q0aKF\nB6qBC9BzDHAFJvgqzPlFikZiZjmjSxTtmS26YeyZNdoItG0rSc88I4tF/frp9GmFh0tS06b6\n7DNdvqzjxwubGc2fr5o1ddNNCg5WbKy++YaQ7WccB7uIiAiLxeKBUuAU+ztq6TkGwNtsFyna\nP4XkzBlnH2mO80q8qFkz621jaYER7CZMUFSUnn7aeu+//62BA5WRoeBg/fST7rtPISGEbH/i\nONgNHjz4vffe80ApcAF6jgHwDc4sUrxwwdlHsvCrkmrUsN42kne1apLUuLEmTpTtnFx0tN5+\nWxkZ6tdPv/+uS5fUty8h25843jwxceLEAQMGDB069NFHH42Oji65f6KV7WmJ8K6779a1a9L1\nnmMA4CXOLFL8/XdnH1nqKFFWlrKybriSmanMzBuupKYqNbW8tZvQTSWGcYKCJOmee4pf79Ch\n8IYRsmWTqgnZfsFxsKt1fbXWokWLSn0AE7Vmw2kpACrN+UWKLGf0ovr1i1+pW7fwxooVhTfm\nzVNamvLy1LChJOXna8wYLVumc+cKt9MWNaiFL3Ac7AYPHhwSElKliuNHwgw4LQUAAkZwcPEr\nRZ/2RXfVras1a7Rjh0aMkKTXXlOvXlq5UgcOKDVVCQk6fJgs7kMcx7WyBupgTsZpKY89pnHj\nvF0K4B1M8MF8pk7V0qU3XLn1Vu3ebZ14LdKjR+GNotnb/v0VF6e4OM2erc2bVbUqZxr7tHKM\nw508eXLPnj0FBQW1atVq3bp1nTp13FcWvIbTUgAApTH6idpmQVbd+SCnesV+9dVXnTt3rl+/\nfpcuXXr16tW5c+d69er17NmTZmJmw2kpAIAyGGN4RYvwxEndPsnxiF12dnbPnj2vXr3arVu3\n1q1bV69evaCg4Oeff167dm3Xrl2zs7NbG4kdJpCRoXvu0fjxSkzUsGFq3tzbBQEAKqWspQW2\nawlSU/Xdd3rzTaeekCX3Ps7x/z5TpkypX7/+559/3qZNG9vr27Zte+CBByZNmsQiPPPgtBQA\nlVaZRYosZ3SHIUO0eLHOnNHzz+ujj5SXp9jYwnnVKVP0+ec6d041axY+OD9fI0fqo4904kTh\nlUuXCrfBHj8uMfHq8xxPxX7zzTdpaWnFUp2kuLi4tLS0tWvXuqcwAHDOokVq3FhVqmjsWG+X\nAviiUpu8/fCDHn5YDRsWdpW4fFnVq0vSq68WPsxYmBMZqfffL+zw1r+/JC1bZm/ulf6/Xuc4\n2J07d65x48al3tWsWbPTp0+7uiRUjv2eY4DJGAf05Odr8mT17u3tagDvKxmtVq+WpObNdfq0\nHn5Yo0erZs1SWrcZjUCKOrwZBxT/8ov1YUbD2dOn7TWfcL5TMCvz3MRxsGvQoMGuXbtKvevn\nn39u0KCBq0sCAKcZB/QMHapx49Szp7erAbyvZLQyRmB+/NEarQoKJOnhh60/VbRa3rbDm8G2\nw5vBzmys852CaU3mJo6D3f333//666+vWLHCtsOExWJZvnz5nDlzHnzwQXeWBwB2cUAPcKOS\n0So6WpJq17ZGqzvukKS8POtPFZ0wbNvPrawrDgfb6P/rRY6DXUZGRo0aNfr27RsZGdmjR48+\nffr06NEjMjIyMTGxdu3aGRkZHqgSAErBAT1AGWyjVXi4JN1/v/XKLbdI0rlzpfxgUcLLypLx\nCV90JTNTCxbc8ODUVFksGjSo+JNUuP8vKs9xsGvWrNnmzZtTUlIuXLiwdu3ajz/+eO3atZcv\nX05NTd2yZUtZy+8AwO0yMjR1qiQlJmr5cj3xhLcLAnyFbZAKCpKuHy9sME6ku3rVXX86/X+9\nyKnjaJo0abJw4UKLxXLs2LGCgoKwsLCGRitg+J1Fi/Tcczp2TOnpmjHD29UAlcMBPUAZSgYp\nzp8LEOX43/nYsWPHjh07e/bszTffHBwcXL9+ffeVZYfFYtm/f/++ffvy8vIkhYeHx8TENGnS\nxCvF+Blj/2BIiCZPVseO3q4GAAC4mFPBbsGCBVOnTj1w4IDtxTZt2mRkZAwqObXuNmfOnHnp\npZfee++9X3/9tdhd0dHRqampzz77bHXjHB6Uytg/+NhjGjeuzMcYp6UAAAA/5DjYzZs3Ly0t\nrVq1aj179oyKiqpZs+a5c+f27NmTk5MzePDgy5cvP/roox4o9OjRo127dt2/f39MTExCQkLT\npk1r1qwp6bfffsvNzd2wYcPEiRM/+OCDdevW1bXtYwdb7B8EALiIMw0tYmP1hz94u9BAY3Hk\n1ltv7d2799mzZ4td37dvX6tWrW677TaHz+ASjz/+eNWqVZcuXVrqvVevXp0zZ05QUNDTTz/t\n8j/6jTfekJSXl+fyZ/ao3r0tkvX/Ro70dkGAK2zaZJEszz/v7ToAX/H44xbJsmeP9UpGhkWy\nbNpkvbJggUWyLF5cqT8oJcUiWXr2tEyaZNm61bJwoSU01BIWZpEs//mfls2bLe+/b6lTx1Kz\nplv+dO+6dOmSpK+//trbhZTC8a7YAwcOTJgwIdzYLW2jefPm6enpubm57oibJX3yySfDhg1L\nSkoq9d7g4OC0tLSBAwd++OGHnqnH/7B/0IXoYQUg4JV6FnF+vgYM0Pz51rOICwr09dfq1s36\ng2UdkgKXcBzswsPDg4ODS70rODj4FuMwHPc7depUy5Yt7T+mbdu2x40exSjp7rsLT/wy9g8a\nx1OiAuhhBcCHZWXJYlGrVtYrmZmyWBxEqwr3eC3rLGLjCY2zkRMSaBrrOY6D3Z/+9KePP/64\n1LtWrVpV1hCay0VGRm7fvt3+Y7Zt2xYZGemZehC46GEFwHQq0ON1/XpJql3bmgUXLpSkW27R\nmDFasUKSXnhBkv76V5rGeo7jYDdlypQvvvhi6NChH3/88b/+9a9Dhw7t2rXrgw8+eOihhy5e\nvDh69OgjNtxXaN++fZctW/bKK68YE9vFFBQUZGRkrFixIjk52X01wCmmn6ZkDwoA06lAj1fj\nlOP0dGsWNObMJk9WaKh69ZKut5do0oSmsZ7jeFesMQaWnZ29aNGikvfGGAOv11ncdlJGZmbm\npk2bxo4d++KLL3bq1KlJkyZhYWEWiyU/P//gwYPZ2dnnz5/v3r37C8a3A3iL6Y/Ke+ABffqp\nJE2frunTNXKk3njD2zUFNg7oCUycte4e5erxajS0aNZMEydKUlycZszQTz8pJETTpik1VZJi\nY7V1q4zV+DSN9QzHwa5v377VqlXzQCn21alT59tvv50zZ8677767fv36a8Zx85KkqlWrxsfH\nDx8+fPjw4WUtB4TL2H8/deaoPL+WkaF77tH48UpM1LBhat7c2wUBgcf0XyC9pwI9Xm1XGter\nJ6lwObchMlJbt+rs2XI8ISrJcbBbvny5B+pwRkhISHp6enp6+sWLFw8fPmx0nqhdu3Z0dHSI\nsToA7ubw/dT005T0sAK8zvRfIL2nAj1eS7agtd1Uafy4bVNamsa6m1+2jgsNDS02BQwPsf9+\nyjQlAA8w/RdIv1KyBS1Nab3LqX/+a9euff/990ePHr1S2oCpJ7uKwcvsv58yTQnA3fgCCdjl\nONht2bJlwIABxRrF2vKRYJebmzty5EhJX3zxRbl+cOfOnaXutC1y6NChSlVmGg7fT5mmBOBu\nfIH0Gd27a/duNW1qvXLvvdqwQbGx1isdOmj5cnXp4vnqApfjYPfkk0+ePXv26aefbt26dVUf\nnhvPy8v78ssvy/tTubm5sbGxzmzmdd+GX8+p5P5B3k8BeB1fIAG7HAe7H3/88W9/+1tfn//9\nadOmzY8//ljen2rZsuVvv/1W6hRzkYULFz7zzDNBxsbuQMb7KQAAvs3xAcVhYWHRRk8Q3xYa\nGtquXbt27dqV9wfDwsLq2lWjRg13FAwAgI+oWCMyh0/YoIHLnhBOcjxiN3DgwPfff799+/Ye\nqMYZFotl//79+/btM447CQ8Pj4mJadKkibfrAgAggGRlKSvrhiuZmcrMvOFKamrhScXwGMfB\nbtq0aYMGDRo4cOAjjzwSGRlZcpldN9v47U5nzpx56aWX3nvvvV9//bXYXdHR0ampqc8++2z1\n6tU9UwwAAICvcRzsdu7c+cMPPxw+fHjZsmWlPsAzuwqOHj3atWvX/fv3x8TEJCQkNG3atGbN\nmpJ+++233NzcDRs2TJw48YMPPli3bl3dunU9UA8CFz2sAAC+ynGwe+qpp06cODFw4MCYmJgq\n3jt2cMKECUeOHFm6dGlSUlLJe69duzZ//vwnn3xy0qRJM2fO9Hx5foMGiwCA0gwZosWLdeaM\nnn9eH32kvDzFxuq119SuncaP17JlOndOsbGaPVs+szgLpXAc1Hbs2LFgwYI///nPHqjGjk8+\n+WTYsGGlpjpJwcHBaWlpGzdu/PDDDwl2ZQqEBoskVwCoEKM3Z1KSunfXmjXasUOjRikpSbGx\nuv12rVypAweUmqqEBB0+rJSUMlNg+/b6979Vo4Zq1dKVK7pwgYDoUY53xdasWbMCW01d7tSp\nUy1btrT/mLZt2x4/ftwz9fgloyHY0KEaN049e7rljzCmKadNc8uTO2Qk1/x8TZ58Q2NqAIAj\nxpxcTIwmTlRcnFJS1KePDh1SaKimTVN8vPr3V0qKjh9XTo41BUZFac0azZun7duVlKTk5MJu\nsK1a6dw5BQVp9mzrXaGhWrlS77yjXbuUkCC7R40VGjJEQUE6e1YjRyoiQjVqqHNnZWfr/HmN\nGaOoKIWFqUsXbd3qzn8av+I42PXr12/VqlUeKMW+yMjI7du323/Mtm3bIiMjPVOPXzJ9g0UP\nJFcAXufdL5Bml5hovW10ZX/kEeuV1q0l6ehReynwrrskqWtXjRypU6fUpo29gOiQnQRZgZgY\nCBwHuxkzZmzYsGH06NFffPHFrl279pbggSol9e3bd9myZa+88kqp7b8KCgoyMjJWrFiRnJzs\nmXr8zKJFql5d3btL0vTpCgrSqFEVeR4ffz81fXIFADeLirLeNtKb7RVjNK4oQtlJgYmJ1hRo\nJyA6VK5xRMiZNXbGJtMvvvhi7ty5pT7AM7tiMzMzN23aNHbs2BdffLFTp05NmjQJCwuzWCz5\n+fkHDx7Mzs4+f/589+7dX3jhBQ8U42eMCcoqVXT//frsM9M2BKM1OABUWsnWoXaaidpJgVFR\nMprMX7niOCA65OQ4IuRMsBs8eHBISIgX98Ma6tSp8+23386ZM+fdd99dv379NaO3lSSpatWq\n8fHxw4cPHz58eHBwsBeL9CG2ewiSk3XhgtLSNHiwPvvMtA3BaGULAJ5lJwXaucvYfispK0tP\nP+3U9ttyjSMGOMdxbdGiRR6owxkhISHp6enp6ekXL148fPiw0Xmidu3a0dHRIcYkPAzFdr/6\nxQRl5Xez0soWAPxB0Sd23bqOt98ayjWOGODKMQ538uTJPXv2FBQU1KpVq3Xr1nXq1HFfWfaF\nhobGGEOxKJWxh+CxxzRuXPEJSt8UCOewAAAkXR9yk9S/v+LiFBen1au1dKk6dSpcwh0fr02b\nNGsWy+YqwvHmCUlfffVV586d69ev36VLl169enXu3LlevXo9e/bcuXOnu+tDRdgO0WVkaOpU\nSUpMLLzhg9jNCgB+yziRxPjk6dNHCxdK0o4dunxZktLSCj98du8u8xlYNudCjkfssrOze/bs\nefXq1W7durVu3bp69eoFBQU///zz2rVru3btmp2d3dr454ePKLmHwDhcOiamcFesD/KLyWIA\nQGmMqVWj7eirr2rhQm3cqKee0i23SNLYsfrhBy1dqrFjNWxY6VOoLJtzIccjdlOmTKlfv/7O\nnTs3bdqUlZX1+uuvv/XWW999992WLVtCQ0MnTZrkgSpRDrZDdMuX64knXP9HLFqkxo1VpYrG\njnXBsz3wgAvOYQEAVE5WliwWtWplvZKZKYtF3bpZr6SmymLRoEE3/KARy+rVk6SYmMLht19+\nKQxnzZurbVtJOn3awdQqy+ZcwvGI3TfffPNf//Vfbdq0KXY9Li4uLS1t3rx57ikMFVVyD8FX\nX7ny+V2+Ho7drADgb7KylJV1w5WXXy5cStOtmxo21EsvacwYFbUjjYhQWtoNU6t79twQIuEq\njkfszp0717hx41Lvatas2enTp11dEnyby9fD3X134YidkUTvuMMFzwkAcLOiZl9ffy1Jf/qT\ntdnXF19I0ogR1mZfFZ5arfA4YsByHOwaNGiwa9euUu/6+eefGzRo4OqS4NtYDwcAgcq2c+vy\n5ZLUrFnhXR06KDtbnTurcWMZLaJeeIFmX17gONjdf//9r7/++ooVK2w7TFgsluXLl8+ZM+fB\nBx90Z3lwqco3BGM9HAAEMNvOrQ88IEn5+TpyRJJuu009e8piUX6+9uyRpHvuodmXFzgOdhkZ\nGTVq1Ojbt29kZGSPHj369OnTo0ePyMjIxMTE2rVrZ2RkeKBK+AoP7MyoDB9vZQsAfs62c+vN\nN0tSt27Kz5eksWPVqZMk3XefCgoKH8+pJZ7nONg1a9Zs8+bNKSkpFy5cWLt27ccff7x27drL\nly+npqZu2bKlrOV3MCfWwwFAwLPt3Gq7tdKIfcYeWEOpS+squWzOdjo4IkI1aljX9o0Zo6go\nhYVZ1/YFIKc6TzRp0mThwoUWi+XYsWMFBQVhYWENGzZ0d2UAAMAH2R44V7KjZ7Vq7v3Ti6aD\nu3d33JEsAI9QcTBi9+uvv3777bfG7aCgoEaNGrVq1aphw4Zz5sw5e/as+8uDKzBBCY9x7RmH\nAHySd9OS7XRwXJxSUtSnjw4dUmiopk1TfLz69w/otX32gt3GjRtbt249ceLEYtd37Njx5JNP\ntmvXbt++fe6sDYBfMc44zM/X5Mnq3dvb1QDwnE2bih9Kt3ixe08ksZ0OpiOZrTKD3dGjR/v3\n75+fn3/fffcVu+sPf/jDa6+9dvTo0QceeOCicfgFfApDdPAKev4CqJAKLJuznQ6mI5mtMtfY\nLViw4OTJkwsWLEhNTS12V1BQ0FNPPXXt2rX09PR33nln5MiRbi4SZmckUfg7zjgEUCHlWjZn\nKDkdHIDL6UpV5ojdihUrWrZsOXz48LIe8OSTTzZu3HjhwoVuqQuAf+GMQwA3GjJEI0ZIUlaW\ng3E4ls25UJnB7tChQ3fddddNN5X5gCpVqnTu3Pmnn35yT2EA/IqPn3EIwNWMU0vq1bNeMU4t\nKVpsZ4zD9eyp//gPrVmjefO0fbuSkpScrNBQrVypd94pbE3x++8Sy+ZcpMzc9ttvv91sHD5Y\ntptvvvmS0TcEQIDjjEMgAJTrCDrnx+FOnJBYNuciZQa7m2+++dChQ/Z/ePfu3fXr13d1SXAb\nlxxFwc6MQMCpJQBcxJlxuAsXJJbNuUiZwa5jx45ffvnlqVOnynrA3r17N23a1LlzZ/cUBlfj\nKAo4iZcKgBIq3O/BmXE4YyoWLlFmsBs2bFh+fv6IESOuXr1a8t7ffvtt6NChV69e/ctf/uLG\n6uBCHEUBJ/FSAVBC0cbVqCgHC+aKzZa6fByukh3JTK/MYNe/f/+ePXsuX768c+fOy5cvz8vL\nM66fOHHizTffjI2Nzc7O7tev38MPP+ypUlE5HEUBJ/FSAVACG1f9RZnBLigoaNmyZQ8++OCW\nLVsSExPDw8Pr1q1bu3btBg0apKamHjx4MDk5+e9//7sna0XFcRQFnMRLBUDZPLxxddIk6/zv\nM89I0oQJTs3/BjJ7LcXq1KmzevXq1atXDx48uHnz5leuXJHUunXrxx57bOPGjUuWLKlevbqn\n6kTlcBQFnMRLBUDZPLxx1XhCY/53zBhJOnjQqfnfQFZm54kiDz744IMPPuiBUuBGd9+ta9ek\n60dRAGXhpQKgbG7auPqf/6m1a2+4kpmpzEwZra+M+V9JL76o5GQtXapOnQrPZoiP16ZNmjVL\nOTnq0sUFlZiAvRE7AL6Oc0kAmB0HF5eL4xE7AD7KOJckJESTJ6tjR29XQ89fINAtWCBJ589r\n5Eh99JHy8nTXXTp9WufPa/x4LVumc+d0992qUcP6I6mpKtGRvjgOLi4XRuwAv8W5JAB8iZG6\nZs4s95Eo9nFwcbkQ7AC/xbkkAHyJ0V6+YUOORPEmgh3gBB9cysa5JAB8ku3CEJbEeR7BDnDE\nN1tscS4JAA9ypt+DEdr+/GfrFZbEeV7pmyeOHDni/FM0btzYRcUAPslYyvbYYxo3ztul2OBc\nEgA+iSVx3lV6sGvSpInzT2FhHxzMjaVsAAA/UXqwS05O9nAdcDtXHUWxaJGee07Hjik9XTNm\nuOAJfdwDD+jTTyVp+nRNn66RI/XGG96uyc04tQSABw0ZosWLdeaMnn++8JCU2Fi99pratVNY\nmCIjdeedio3V7Nlq377w4GJbzhyYElBKD3ZLlixx5ocLCgry8vJcWg98m68dnOYBGRm65x6N\nH6/ERA0bpubNvV0QAJhKSIgkJSWpe3etWaMdOzRqlJKSFBur22/XypU6cECpqUpI0OHDzOo6\nVqnNEytWrGjfvr2rSoEfCMCD0+6+u3DzqbGU7Y47vF0QAJiKscHC6BvGISmV51TniZMnTy5Z\nsuTAgQNXr14tunjx4sVVq1bl5+e7rTb4HlabAQDcgL5hruI42B04cKBTp04nTpwo5YerVJkw\nYYIbqoJPCsDVZgAAp2VlKSvrhivOL4mjb5irOA52L7zwwsWLF2fPnt22bdsePXpkZWU1btx4\n/fr177333ptvvtnbd471grux2gwA4B4ckuIqjoPdpk2bRo8ePXr06IsXL0q6/fbbO3fu3Lt3\n7+Tk5B49eqxcubJr167urxM+gIPTAADwbY43Txw9erRFixaSbrrpJkmXL182rt95552jR4/O\nyMhwa30IID7YtsvHGeeSTJvm7ToAoByGDFFQkM6e1ciRiojQwoWStGOHzp/XmDGKiirsqrN7\nt1er9FuOg12tWrWOHz8uKSQkJCwsbN++fUV33XbbbZs3b3ZjdQgcvtm2CwDgakXnm0RFac0a\nGdN+Tz2l5GSFhmrlSvXrJ0ljx7KoriIcB7vu3bu/8cYb69evl/SHP/xhzpw5RTth165dW61a\nNbfWh0Dh1oNUGAtEefGaAdym2Pkmxh7YX36xnm/Stq0knT7N+SYV4TjYjR8//tSpU88++6yk\nESNGbN68+bbbbktMTIyLi1uwYEGvXr3cXyQCgPsOUmEsEOXFawZwP9vzTQy255sYON+kAhxv\nnujUqdNXX32VnZ0t6S9/+cuePXtmzpy5fPnyoKCgPn36zJw50/1FwuzcepCKMRb42GMaN66C\nz0CLrUBT+dcMAEeKTjPJylLjxpo0yXolM1ONG2vECOtULH3DnOdU54n4Gu/zXAAAIABJREFU\n+PgnnnhCUlBQ0NSpU0+fPr1///6CgoIVK1bccsstbq4QplNykisjo3CtbGKili/XE0+48o/j\nUGWUF68ZwP0438RNytFS7OjRo9u2bVu3bt3u3btr1qxZvXp195UF0yp1kst9bbseeKDwmadP\nV1CQRo1y2TPDrHjNAPBnTrUUW7BgwdSpUw8cOGB7sU2bNhkZGYMGDXJLXTArD09ycagyyovX\nDAB/5jjYzZs3Ly0trVq1aj179oyKiqpZs+a5c+f27NmTk5MzePDgy5cvP/roox4oFD6h8qvN\nPDzJxaHKKC9eMwD8meNgN3PmzN69e//jH/8IDw+3vb5///77779/+vTpBDs4i26zAAC4k+M1\ndgcOHJgwYUKxVCepefPm6enpubm57ikMZuTWTRIAAAQ8x8EuPDw8ODi41LuCg4PZFYtycN8m\nCQCAc4p19KpRQ507Kzvb2tErLExdumjrVncVkJUli0WtWlmvZGbKYlG3btYrqamyWOTkMn6v\n/418iuNg96c//enjjz8u9a5Vq1YlJSW5uiQAAOAuxTp6zZun7duVlGTt6PXOO9q1SwkJftPR\ny3x/o8pwvMZuypQpffv2PXDgwKBBg2JiYmrUqFFQUPDzzz+/9dZbly9fHj169JEjR4oe3Lhx\nY3dWC5NatEhjxkjSunXeLgUATM62o5ekuDitXq2lS9Wpk6ZNk6T4eG3apFmzlJOjLl28WaqT\nzPc3qgzHwS4yMlJSdnb2okWLSt4bY/R4u87CAf0oL+Nku5tuksTREgDgGbYdvYxPctuOXq1b\nS/7W0avobzRkiJYulaRTpxQRobw8xcbqnnskaepUbdumc+cUG6vZs9W+vdeqdR/Hwa5v377V\nqlXzQCkIUMbJdmlpmjPHNU+4aJGee07Hjik9XTNmuOY5AcBcivp36fqIl+0VowmEf01cFtVv\nzMxKatpUr76qHTs0apT27JGk4GCtXKkDB5SaqoQEHT5swnYXjoPd8uXLPVAHApdrT7Y7d04p\nKbp6VZI+/phgBwClMl9Hr6L6q1yPNo8/rrg468yspMGDFR9v8pnZ0jdPHDt27MyZM0W37fNg\ntfBtJZvAOuTy9k1ffVWY6u6/X8OHS9cPVTbWWQDO4DUDD6vAmyfK48ZVY5J/zjU7qfQRu0aN\nGvXu3XvNmjXGbftPwbo6SNeXyoWEaPJkdezo7E+5vH3Tt99K0i23FJ6EDAA+rmJvniiPKiXC\njj/ONTup9GCXnJx85513Ft32YD3wWxVrAuva9k1FnS1OnlRQEJ0tAPgBD3fQhtmVHuyWLFlS\n6m2gTE4ulat8t1k7vvvuhv/8/HN3/UEA4Coe7qANs3N8QLHhp59+OnnypO1/btu2zT0lwQ+5\nfKlcxcyYoU6dJCksTH/9K2ukAPg6H3nzhIk4DnZXrlx5/PHH27Vrt3PnzqKL69ata9++/WOP\nPXbNmEdDgLPfBNZj64JHjChsSVO7tl5+WbRFAeDjvNFB2+Udvbyu5N9I0p49N/yNJG3a5Dd/\no8pwHOxef/31t95666GHHmratGnRxV69eiUnJy9cuHD27NnuLA9+wk4TWGNdcH6+Jk9W797e\nKhAAfJG/ddCmK6vvcxzsFi5c+PDDD69ataq5zY7F1q1bL1myJCEhgWAHB4x1wUOHatw49ezp\n7WoAABVHV1bf5zjY7d27949//GOpd917770HDx50dUkwF9YFA4BZ2HZljYtTSor69NGhQwoN\n1bRpio9X//5KSdHx48rJ8VqR5ptrLhfHwa527doHDhwo9a4DBw7Uq1fPxRXBTFgXDACmY74+\ns2biONg99NBDb7755urVq20vXrlyZcGCBf/7v/97//33u602+D9vrAsGALiV+frMmonjXrFT\npkz55z//+dBDD0VHR7du3bpatWpnz579+eefT58+3ahRoylTpnigSvgrZ84fduvJdoFj0SI9\n95yOHVN6Oh1y3Yh/Z8CMfWbNxPGIXaNGjbZt2zZq1KiCgoLPP/981apVX331VXBw8IgRI3Jy\ncqKjoz1QJQB72HrsGfw7A/B5jkfsJEVERMybN2/u3LlHjx69cOFCw4YNa9as6e7KgIpo2VKS\nAu37Bi2JPIN/ZwA+z6lgZwgKCoqMjHRfKYALpKUpLc3bRXgcW489g39nAD7PcbCzWCzvv//+\nu+++e+TIkSulLYa07UiBwMVSOW954AF9+qkkTZ+u6dM1cqTeeMPbNZkR/85wE9484VKOg92r\nr746duxYSTVq1KjK8kjA12Rk6J57NH68EhM1bJhsDhKHK/HvDMAfOA52s2bN6t2799y5c1u0\naOGBggCUjzNbj1F5/DsDUlaWsrJuuJKZqczMG66kpio11YM14UaOg93x48fff/99Uh0AAICP\nc3zcSUREhIXpfwAAAJ/neMRu8ODB7733XufOnT1QDUyIdcEAAHiK42A3ceLEAQMGDB069NFH\nH42Oji65f6KVbaNdAAAAeInjYFfr+qFNixYtKvUBTNTC59D3CQAQkJyaig0JCalSpRxHGQPe\nZPR9CgnR5Mnq2NHb1QAA4DmO41pZA3WAj6LvEwDPY6IAvqH0YHfs2LFq1arVrVvXuG3/KRo2\nbOj6uoAKo+8TAA9jogA+o/Rg16hRo969e69Zs8a4bf8pWGMHHxKYfZ/YeuwZ/DujLEwUwGeU\nHuySk5PvvPPOotserAeoHPo+AfA8JgrgM0oPdkuWLCn1NuDr6PsEwMMCc6IAvspx54mVK1f+\n9NNPHigFAAD/k5GhqVMlKTFRy5friSe8XRACmuNgl5ycvGrVKg+UAgCA/7n7bnXvLl2fKLjj\nDm8XhIDmONh169Ztw4YNv//+uweqAQAAQIU5Psfub3/7W3p6+kMPPfToo4/eeuut4eHhxR5A\nSzEAAABf4DjYFR1TZ5x+UhLHnQAAAPgCx8EuOTk5JCSkatWqQUFBHigIAAAAFeM42PnycSeX\nL1/evn17fn5+s2bNmnNiGQAACGwONk9cunQpOzt7/fr1DhuLuduUKVPWrVtne2X+/PkNGzbs\n1KnTfffd16JFiw4dOvzwww/eKg8AAMDr7AW7d955p2HDhnfdddcf//jHyMjIIUOG5OXleayy\nYiZMmPCpcQKkJOmTTz4ZNWrU+fPn+/XrN3LkyK5du27ZsuXee+/Nzc31VoXwFUbfp2nTvF0H\nAACeVuZU7MaNGx977LHg4ODevXvffPPN33333eLFiy9cuLB8+XJP1leW9PT08PDwb7/9tm3b\ntsaVDz/8cMCAAS+99NJbb73l3doAAAC8osxg98orrwQFBa1du7Z79+6SLl++PGjQoOXLl+/c\nubNdu3YerLAUJ06c2LNnz/jx44tSnaTExMRHHnnks88+82JhAIBAZEwUAD6gzKnY77777v77\n7zdSnaSQkJDMzExJGzdu9Exldly8eFGSbaoztGvX7tdff/VGRQAAAN5XZrA7derUrbfeanvF\n+M9Tp065vShHIiMjw8PDjxw5Uuz6L7/8UqtWLa+UBBRatEiNG6tKFY0d6+1SAAABp8xg9/vv\nv1evXt32SmhoqKRr1665vagyHDp0aPPmzXv37j1z5kxaWtqbb755/vz5onv/f3v3HhdVnf9x\n/IPAIDfFuyKIkqSmq6VoobBY2mpWSphJpfXAaAXL1F3JxV+JyCODdLtouln7cFtJTU3NVtfc\ntYw1y6XUdU3NCC9RghYJioACzu+PU7PEZRjAmTPnO6/nX8yZw8xnjs7Mm+/1yy+/3LBhw4gR\nI/QqD5CSEklIkNJSSU+XMWP0rgYA4HIaX8fOeaxfv379+vU1j+zcuXPixIkism7dut/+9rfl\n5eXPPvusTtUBIrm5Ul4u8fGSkqJ3KQAAV2SYYPeXv/yluIaSkpLi4uJ27dpp9xYXFwcEBLz9\n9ttDhw7Vt064tIoKERHGAxjXunXy9NNSWChz5siSJXpXAwBN5tbQTq9ubm4jRowYPXp0zYNp\naWnR0dEjR46seVCbVKGv0tJSHx+fVq0aWW+5eVatWpWYmHjp0iU/Pz97PD4UMXas1FhqUaZP\nl9de068aNF1JiXTrJiaTzJsnQ4fKLz/9AMDi6tWrXl5e+/btGz58uN611GatxW7fvn379u2r\ndTA7Ozs7O7vmEWcIdkQu6C81VaKjZf58iY2VqVOFPe4Mh550AMbXYLDLyspyZB2A4UVEiDa1\nKCxMYmL0rgZNR086AONrMNhNmTLFkXW0XF5e3vTp00Vk9+7detcCwGgsPemZmZKZSU86AIMy\nzOSJRl26dOmDDz7QuwoAxkRPOgAl2GW2gS769u175MiRI0eO6F0IrGL9XjiniAjRNtrRetIH\nDdK7IABoDnVa7Fq3bt2MTWxLSkoWLFig7VHWkOPHj7egLtSgrd9rMkl6urAwDQC9sK4N1GW8\nYGc2m0+dOnXy5MlLly6JSNu2bcPCwoKDg5v3aJWVlT/88ENlZaWVc2rub4EWYdYhAN3xFyaU\nZqRgd+HCheeeey4rK+v8+fO17urRo0dCQsLcuXNrbYPWqI4dO65du9b6OatWrTpw4EDTakW9\nmHUIQHf8hQmlGSbYFRQUjBgx4tSpU2FhYePGjQsJCfH19RWRixcv5uXlZWdnL1iwYPPmzXv2\n7LFsRwHnwqxDAM6AvzChNMMEu2efffbbb7/duHHjpEmT6t5bXV29atWqJ598Mi0t7eWXX3Z8\neWgcsw4B6I6/MKE6wwS7HTt2TJ06td5UJyLu7u4zZsz417/+tWXLFoKdk3KF9XsjI6WBPfoA\nOAX+woTqDBPsioqKbrjhBuvn9OvXb+vWrY6pBwBgPK7wFyZcm2HWsQsMDDx8+LD1cw4dOhQY\nGOiYeqAsVtqriasBAIZimBa7mJiYZcuWDR06dObMmV5eXrXuvXz58gsvvLBt27Z58+bpUh4U\nwToINbna1aAnHYDxGSbYLVy4cO/evcnJyYsWLRo2bFhwcLCfn5/ZbC4tLT1z5kxOTk5ZWVlU\nVNQzzzyjd6UwMtZBqImrAQBGY5hgFxAQ8Omnn65YsWLNmjUfffRRtTZIQkREPD09hwwZMm3a\ntGnTprm7u+tYJAyPdRBq4moAgNEYZoydiJhMpjlz5hw6dKi0tPSrr746cODAgQMHcnNzS0tL\nP/3008cff5xU5+z++U8RkRdecNIBW2PH/rRbaGamuLlJYqLeBemKqwEABmSYFruaWrduHRYW\npncVaKKSEsnMFBH59a9lzBi9q6kP6yDUxNUAAAMyUotdXUuXLo2MjNS7CtgmN1euXBERue02\nGT1a72rqExHxUxuVtg7CoEF6F6QrXa4Gk3ABoGUM2WJn8fXXX+/bt0/vKmAbbcDWvHmSkaF3\nKXBKrjYJFwDswNgtdjAMlx2wRROU7bRJuA8/LCkpTtqmCzVo69rwFyYUZewWOxiGaw7Yogmq\nSZiECwAtRosdHMI1h6811ARFM15dLtumCwDXlbGDXUZGRn5+vt5VAA2otwlKa8YrLZX0dCed\nHayL1FRZvFhEJDZWtm6VpCS9CwIAQzJ2sAsICAgKCtK7CqA+DTVBOWYkmeEaBV2zTRcArjdj\nBzvAedXbBLVunYwbJyLy+ed2fGoaBQHAVTF5ArCPiAjRNr7TmqBEpKREHnnkp4M5OeLmJtOn\ny2uvXf+nZo9XAHBVBDtAZN06efppKSyUOXPEbLbXs+TmSnW13Hab7N9v39nB12t6qbYqBADA\nOOiKhctzWMellrduuEHEniPJmF4KAC6MYAeX55jZDJa8tXatiMiuXdZObsnUB6aXAtBF3Q8u\nw83iUgJdsXB5jlkX17JEc3S0ZGfLLbc0eGYLlzWuO7YPAOyt7gcXK7TrhGAHR3HOAVtjx/7U\neJaZKZmZ9prNIDXylrZAT+fODZ7J1AcAhlP3g4uPMp3QFQvX5oQdl+ysBcBw6n5w8VGmE4Id\nXJuzrYvrylMf2JodMKi6H1yu/FGmN4Id4Ez0akFkjDOAZqv7weWEnSEugzF2gN00Y1ihLlMf\nGOMMoCXq/eBiFpdOaLGDwdHU1HKOWfAFAGB/tNjByIzY1BQU5HSzgxnjDACqoMUORkZTU8sx\nxhkAFEKLHYyMpqZ6NWlsn2XlZLtuXwsAcAha7GBYhmtqcs7lPJxtwRcAQAsQ7GBYTKcHAOCX\n6IqFYV2vlUGcc68zAACajhY7AAAARdBiBzgZWhABGE7dDy4+ynRCix0AANAP68xfV7TYAQAA\nnRhxnXnnRrADAAA60daZj4+XlBS9S1EEXbEAAEAnrDN/vRHsAJfnnCsnA3Aq9hgJZ7h15o2A\nrlgAAGCVnUbCsaWhHRDsYGRMpwcAB7DTSLjrtc48aqArFgAAWMVIOOMg2AEOx6JNAAyEkXCG\nQrADHEsbqlJaKunpMmaM3tU0hgwKIDVVFi8WEYmNla1bJSlJ74JgDWPsAMcy0KJNLBwKQBgJ\nZzAEO8CxDDRUxUAZFAAgInTFAg5lrKEqLc+g9OQCgGMR7AAHMtBQlZZnUGONJgQAJRDsAAeK\niPgpLWlDVQYN0rughrU8g2o9uQ8/LCkpMnr0dS8Q9aOVFHBtjLEDUJ+WD5c20GhCZTDfBYbD\nOvPXGy12AOzAWKMJlUErKeDyCHYA7MBAowlVQisp4PIIdgDswECjCZVBKykAxtgBgCJSUyU6\nWubPl9hYmTpVevXSuyAohJFwxkGwAwAlsD0AALpiAQAAlEGwAwAAUATBDnAsbahKRoZDn5RF\nawHANRDsgMYYPRU1e2svXTIoAKAFmDwBWKXAUv7aorXx8ZKSoncpAAD7ItgBVimQili0FgBc\nBl2xgFVGT0U6LlpLTy4AOBzBDmiYAkv5N3VrL6MPKAQA10ZXLNAwBZbyb9KitQoMKHRxbA8A\nuDxa7ICGudqGp9qAwocflpQUGT1a72oAoAH0LTSMFjsAPzP6gEIAroC+BatosQMgIkoMKATg\nCuhbsIpgB0BEmj7NAgB0Qd+CVQQ7QCEtGXfiagMKARgRfQuNIdgBqmj21mFwMMZ9A81G30Jj\nmDwBGNm6dfL001JYKHPmyOTJht8kwxUw7htoiSYt4eSSCHaAYdWKCIw7MQQFNqkD4MToigUM\nq+bUsKVLGxx3wtZeToX8DcCeCHaAVc6cimpGBMadGALjvgHYGcEOMKZaEeGvf23CnFYG7+uF\n/A3AzhhjBxhT3X1sL12y6RcZvK8jxn0DsDOCHWBMdSPCxx/b9IsM3gcAdRHsABdjZfC+NqAQ\nAGBYjLEDXAmD9wFAabTYAa6k7sg8ADAW+hasItgBroTB+wCgNLpiAQAAFEGwAwAAUARdsYAq\nGHcCAC6PYAcADkT+BmBPBDvAsIgIAIBfYowdAACAIgh2AAAAiiDYAQAAKIJgB7gYbWReRobe\ndTjEunUSFCQeHpKcrHcpAOAITJ4AoKiSEklIEJNJ0tNl6FC9qwEARyDYAVBUbq6Ul0t8vKSk\n6F0KADgIXbEAFFVRISLi7693HQDgOAQ7ACoaO1aiokREMjPFzU0SE/UuCAAcgWAHQEWpqbJ4\nsYhIbKxs3SpJSXoXBACOwBg7ACqKiJDqahGRsDCJidG7GgBwEFrsAAAAFEGwAwAAUATBDgAA\nQBEEOwAAAEUYL9iZzeaTJ0/u3r1769atW7du/fDDD/Pz8/UuCnAabKIFx+N/HeA0jDQr9sKF\nC88991xWVtb58+dr3dWjR4+EhIS5c+d6e3vrUhvgFNhEC47H/zrAmRgm2BUUFIwYMeLUqVNh\nYWHjxo0LCQnx9fUVkYsXL+bl5WVnZy9YsGDz5s179uxp166d3sUCOtFlE6116+Tpp6WwUObM\nkSVLHPe8cBJs3QY4E8MEu2efffbbb7/duHHjpEmT6t5bXV29atWqJ598Mi0t7eWXX3Z8eYBT\ncPwmWrTWgK3bAGdimDF2O3bsmDp1ar2pTkTc3d1nzJjxwAMPbNmyxcGFAc5Cl020tNaahx+W\nlBQZPdoRz2i7yEgxmyUjQ+86lMbWbYCTMUywKyoquuGGG6yf069fv3PnzjmmHsDp6LKJFq01\nLo6t2wAnY5hgFxgYePjwYevnHDp0KDAw0DH1AE4nIuKnthNtE61Bg+z+jLTWwPH/6wBYZZhg\nFxMTs2nTpqVLl165cqXuvZcvX05NTd22bdvkyZMdXxvgomitAQAnY5jJEwsXLty7d29ycvKi\nRYuGDRsWHBzs5+dnNptLS0vPnDmTk5NTVlYWFRX1zDPP6F0p4DIiIqS6WuTn1hoAgN4ME+wC\nAgI+/fTTFStWrFmz5qOPPqrWvk5ERMTT03PIkCHTpk2bNm2au7u7jkUCRsWSJQCgBMMEOxEx\nmUxz5syZM2dORUVFfn7+pUuXRKRNmzY9evQwmUx6VwcYFkuWAIAqjBTsLFq3bh0WFqZ3FYAq\nWGAWAFRhmMkTAOzFxiVL2A8UAJyeOsEuLy9v9OjRo51tiVTAydm4ZInWXVtaKunpMmaMIwsE\nANhOnWB36dKlDz744IMPPtC7EMBQbFyy5DruMEHLHwDYjSHH2NWrb9++R44c0bsKQFfaJlpN\nYuOSJddrhwkmaqinGf/rANiNOi12rVu3HjBgwIABA/QuBFDOddxhwpn3lgUA41OnxU5EioqK\nLly40Lt3b9t/5dSpU/379y8vL2/0zFat1AnBQNOkpkp0tMyfL7GxMnWq9Or1v7ua2lrD3rIA\nYE9KBbslS5ZkZmaam/I1ExISsnPnzsrKSivnHD16dPbs2R4eSl0roAmu1w4TY8fKrl0iIpmZ\nkpkp06fLa69dnwoBACKiWLBrhlatWkVHR1s/x8fHxzHFAIqz0vIHALgeXD3YAXAc9pYFADsz\nTLALDw9v9JzvvvvOAZUAAAA4J8MEu0OHDomIp6enlXOqqqocVQ4AAIDTMcxMz+TkZF9f3y++\n+KKiYXPnztW7TAAAAN0YJtilp6f37t37wQcftD6DFUCTaUuWZGToXQcAoKUME+w8PT3Xrl17\n9OjR+fPn610LAACAMzLMGDsR6devX2FhoZWBdHfddVdAQIAjSwIAAHAeRgp2ItKmTRsr90ZH\nRze6KB2A5mA/UAAwAsN0xQIAAMA6Ywe7pUuXRkZG6l0FAJsxUQMA7MnYwe7rr7/et2+f3lUA\nAAA4BWMHOwAAAFgQ7AAAABRBsAMAAFCEsYNdRkZGfn6+3lUAAAA4BYOtY1dLQEAAKxIDAABo\njN1iBwAAAAuCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAH\nAKpbt06CgsTDQ5KT9S4FgH0Ze+cJAEAjSkokIUFMJklPl6FD9a4GgH0R7ABAabm5Ul4u8fGS\nkqJ3KQDsjq5YAHZGP6C+KipERPz99a4DgCMQ7ADYk9YPWFoq6ekyZoze1biesWMlKkpEJDNT\n3NwkMVHvggDYF12xAOyJfkB9paZKdLTMny+xsTJ1qvTqpXdBAOyLYAfAnugH1FdEhFRXi4iE\nhUlMjN7VALA7umIB2A39gADgWAQ7AHaTmiqLF4uIxMbK1q2SlKR3QQCgOLpiAdgN/YAA4Fi0\n2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAoglmxAKC0yEgxm/UuAoCD0GIHAACgCIId\nAACAIuiKBWBP9AMCgAPRYgcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2\nAAAAiiDYAXAO69ZJUJB4eEhyst6lAIBRsUAxACdQUiIJCWIySXq6DB2qdzUAYFQEOwBOIDdX\nysslPl5SUvQuBQAMjK5YAE6gokJExN9f7zoAwNgIdgD0NnasREWJiGRmipubJCbqXRAAGBXB\nDoDeUlNl8WIRkdhY2bpVkpL0LggAjIoxdgD0FhEh1dUiImFhEhOjdzUAYGC02AEAACiCYAcA\nAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAimCBYgBOIDJSzGa9iwAA\nw6PFDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEO\nAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEV46F2AAZhMJhHx8vLSuxAAAOAs\ntHjgbNzMZrPeNRjA4cOHq6qq9K7iF65cuTJixIiFCxfecMMNeteCZnr++ef79OkTGxurdyFo\npp07d37yySfp6el6F4JmKigoePrpp5cvXx4QEKB3LWim5OTk+Pj4iRMnOvh5PTw8Bg0a5OAn\ntQXBzqjKy8t9fHz2799/66236l0LmmnUqFGRkZFpaWl6F4JmevHFF9euXXvgwAG9C0EznThx\nom/fvmfPnu3WrZvetaCZbrzxxuTk5Mcff1zvQpwFY+wAAAAUQbADAABQBMEOAABAEQQ7AAAA\nRRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsDMqd3d3d3d359yoDjYymUyenp56\nV4HmM5lMvAcNzWQyubm58TY0NN6GtbClmIGdPHkyNDRU7yrQfOfOnfPz8/P19dW7EDRTRUXF\njz/+GBgYqHchaD4+SI0uPz+/a9eupHMLgh0AAIAi6IoFAABQBMEOAABAEQQ7AAAARRDsAAAA\nFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbAD\nAABQBMEOAABAEQQ7AAAARRDsjO3ChQtz584NCQnx8vLq1atXTEzM/v379S4KTVNZWZmSkuLu\n7h4eHq53LbBVcXHx7Nmze/bsaTKZAgMDExISCgoK9C4KTcNbz9D4+muIm9ls1rsGNNOPP/44\nZMiQ06dP33333YMHDz558uSGDRs8PDxycnJ+9atf6V0dbHL8+PEpU6bk5uZevnz5lltu+fzz\nz/WuCI27evVqRETEwYMHJ06cOHjw4Ly8vKysrKCgoAMHDrRr104Eu0RQAAAPxUlEQVTv6mAT\n3nqGxtefNWYY1hNPPCEiy5cvtxzZvHmziIwbN07HqmC7kpISb2/v8PDw3NxcLy+vIUOG6F0R\nbPLiiy+KSGZmpuXIhg0bROT3v/+9jlXBdrz1jI6vPyvoijUwT0/PUaNGTZ8+3XLkvvvu8/b2\nPnr0qI5VwXZVVVUzZsz45JNPevfurXctaII1a9b4+/vPmjXLcuSBBx7o3bt3VlaWmT4QI+Ct\nZ3R8/VlBV6xSrly54u/vP2zYsI8//ljvWtA0rVu3HjBgAP1Bzq+iosLPz2/kyJG7d++ueTw+\nPv7NN9/My8sLDQ3VqzY0A289NfD1Z0GLnVJWrVpVWVkZFxendyGAsvLz86urq4ODg2sdDwkJ\nEZGTJ0/qURTg6vj6syDYqSM7Ozs5OTkyMjIxMVHvWgBlXbp0SUR8fX1rHffz87PcC8CR+Pqr\nyUPvAtC44uLiP/zhD5abvXv3njt3bq1z1q9fHx8fP2DAgG3btnl48M/qXGz5F4SxuLm51Tqi\nDWupexyAXfH1VwuXwABKS0tXrVpluTlixIiascBsNi9cuHDRokVjx47duHGjv7+/HjXCGuv/\ngjCWNm3aSH0tcxcvXhQR3oCAw/D1Vy+CnQEEBQU1NMfFbDYnJCSsXr165syZL730kru7u4Nr\ngy2s/AvCcHr06OHh4XHmzJlax/Py8kQkLCxMj6IAl8PXX0MYY2dsc+bMWb169eLFi5ctW8Z/\na8ABTCbTkCFDcnJyysrKLAevXbuWnZ0dHBzco0cPHWsDXAdffw0h2BnYli1bXnnllVmzZqWk\npOhdC+BCHnvssbKysiVLlliOvP7662fPnk1ISNCxKsB18PVnBevYGVjv3r3z8vJmzpzp4+NT\n66558+axtZHzy87O3rlzp/bz0qVLO3Xq9Oijj2o3k5OTO3TooF9psKa6uvr222/fu3fvhAkT\nBg8efPz48Q0bNgwYMGD//v1134xwQrz1jI6vPysIdgZmZf7dqVOnevbs6cBa0BwZGRkN/bmZ\nm5vLmvjOrLS0NC0tbdOmTWfPnu3cuXNMTMyiRYvat2+vd12wCW89o+PrzwqCHQAAgCIYYwcA\nAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAi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LhCBDu4TPE84zXXSNJ//Zd9ntGII2PH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\nx48XkZiYmKKiok2bNlV/rZycnAcffNDf39/ON96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xpJS1NaWqGR5GQlJzv7EA7VAgDKKJCC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\n6uBSr1y5slu3bq1bty74hb+/f79+/fbs2VNcXFz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"text/plain": [ "plot without title" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + }, + "text/plain": { + "height": 420, + "width": 420 + } + }, "output_type": "display_data" } ], "source": [ - "Gender <- substring(sex[1:191],1,1)\n", + "Gender <- substring(sex[1:dim(ijc)[2]],1,1)\n", "\n", - "plotMDS(y[,c(1:191)], labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + "plotMDS(y[,c(1:dim(ijc)[2])], labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + " gene.selection=\"common\")\n", + "plotMDS(y_voom[,c(1:dim(ijc)[2])], labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", " gene.selection=\"common\")\n", - "plotMDS(y_voom[,c(1:191)], labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + "plotMDS(y_dup_voom[,c(1:dim(ijc)[2])], labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", " gene.selection=\"common\")\n", - "plotMDS(y[,c(192:382)], labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + "plotMDS(y[,c((dim(ijc)[2]+1)):(dim(ijc)[2]+dim(sjc)[2])], labels=Gender, top=250, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", " gene.selection=\"common\")\n", - "plotMDS(y_voom[,c(192:382)], labels=Gender, top=500, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", - " gene.selection=\"common\")\n" + "plotMDS(y_voom[,c((dim(ijc)[2]+1)):(dim(ijc)[2]+dim(sjc)[2])], labels=Gender, top=250, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + " gene.selection=\"common\")\n", + "plotMDS(y_dup_voom[,c((dim(ijc)[2]+1):(dim(ijc)[2]+dim(sjc)[2]))], labels=Gender, top=250, col=ifelse(Gender==\"m\",\"blue\",\"red\"), \n", + " gene.selection=\"common\")" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "fit <- lmFit(y_voom, design)\n", - "fit <- eBayes(fit)" + "fit <- lmFit(y_dup_voom, design=design, block=donor, correlation = dup_cor$consensus.correlation)\n", + "fit <- eBayes(fit, robust=TRUE)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1942,7 +1504,7 @@ "text/plain": [ "sex_as_events_results_refined\n", "FALSE TRUE \n", - "42202 409 " + "42200 411 " ] }, "metadata": {}, @@ -1953,7 +1515,7 @@ "text/plain": [ "sex_results_refined\n", "FALSE TRUE \n", - "42204 407 " + "42203 408 " ] }, "metadata": {}, @@ -1982,9 +1544,45 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fetching fromGTF.tar.gz from GitHub ..\n", + "\n", + "downloading fromGTF.tar.gz ...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " |======================================================================| 100%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Done!\n", + "\n", + "\n", + "Decompressing fromGTF.tar.gz into ../data\n", + "\n", + "Done!\n", + "\n", + "\n", + "Decompressing fromGTF.*.txt.gz into ../data\n", + "\n", + "Done!\n", + "\n", + "\n" + ] + }, { "data": { "text/html": [ @@ -2072,6 +1670,24 @@ } ], "source": [ + "# fromGTF.tar.gz\n", + "if (! (file.exists(\"../data/fromGTF.tar.gz\"))) {\n", + " system(\"mkdir -p ../data\", intern = TRUE)\n", + " message(\"Fetching fromGTF.tar.gz from GitHub ..\")\n", + " # Download archive from GitHub release with tag \"dge\"\n", + " piggyback::pb_download(file = \"fromGTF.tar.gz\",\n", + " dest = \"../data\",\n", + " repo = \"adeslatt/sbas_gtf\",\n", + " tag = \"rMATS.3.2.5.gencode.v30\",\n", + " show_progress = TRUE)\n", + " message(\"Done!\\n\")\n", + " message(\"Decompressing fromGTF.tar.gz into ../data\")\n", + " system(\"mkdir -p ../data && tar xvfz ../data/fromGTF.tar.gz -C ../data\", intern = TRUE)\n", + " message(\"Done!\\n\")\n", + " message(\"Decompressing fromGTF.*.txt.gz into ../data\")\n", + " system(\"gunzip ../data/fromGTF*.txt.gz \", intern = TRUE)\n", + " message(\"Done!\\n\")\n", + "}\n", "fromGTF.SE <- read.table(\"../data/fromGTF.SE.txt\", header=TRUE)\n", "head(fromGTF.SE)\n", "genes <- factor(fromGTF.SE$geneSymbol)\n", @@ -2080,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2093,12 +1709,12 @@ "\t<dbl><dbl><dbl><dbl><dbl><dbl>\n", "\n", "\n", - "\t190765.8789480.462345254.328932.071534e-1838.827015e-179398.2269\n", - "\t59656.0885330.597411752.396444.863173e-1781.036123e-173386.6747\n", - "\t190705.4282750.350268450.184191.049979e-1711.491355e-167372.9366\n", - "\t59625.9044020.682046845.763772.050934e-1582.184809e-154344.0765\n", - "\t63005.4991130.760186338.959273.827125e-1363.261552e-132295.0173\n", - "\t343575.6341451.462511938.172542.041761e-1331.450024e-129288.3820\n", + "\t190765.8820040.462345259.939293.249431e-1981.384615e-193432.7360\n", + "\t59656.0958960.597411758.213838.730492e-1941.860075e-189423.1656\n", + "\t190705.4345360.350268455.612506.602101e-1879.377404e-183408.1777\n", + "\t59625.9081740.682046850.969635.496989e-1745.855805e-170380.0420\n", + "\t63005.4978050.760186343.299961.270316e-1501.082589e-146328.4099\n", + "\t343575.6368471.462511942.433498.258637e-1485.865146e-144321.4354\n", "\n", "\n" ], @@ -2108,12 +1724,12 @@ " & logFC & AveExpr & t & P.Value & adj.P.Val & B\\\\\n", " & & & & & & \\\\\n", "\\hline\n", - "\t19076 & 5.878948 & 0.4623452 & 54.32893 & 2.071534e-183 & 8.827015e-179 & 398.2269\\\\\n", - "\t5965 & 6.088533 & 0.5974117 & 52.39644 & 4.863173e-178 & 1.036123e-173 & 386.6747\\\\\n", - "\t19070 & 5.428275 & 0.3502684 & 50.18419 & 1.049979e-171 & 1.491355e-167 & 372.9366\\\\\n", - "\t5962 & 5.904402 & 0.6820468 & 45.76377 & 2.050934e-158 & 2.184809e-154 & 344.0765\\\\\n", - "\t6300 & 5.499113 & 0.7601863 & 38.95927 & 3.827125e-136 & 3.261552e-132 & 295.0173\\\\\n", - "\t34357 & 5.634145 & 1.4625119 & 38.17254 & 2.041761e-133 & 1.450024e-129 & 288.3820\\\\\n", + "\t19076 & 5.882004 & 0.4623452 & 59.93929 & 3.249431e-198 & 1.384615e-193 & 432.7360\\\\\n", + "\t5965 & 6.095896 & 0.5974117 & 58.21383 & 8.730492e-194 & 1.860075e-189 & 423.1656\\\\\n", + "\t19070 & 5.434536 & 0.3502684 & 55.61250 & 6.602101e-187 & 9.377404e-183 & 408.1777\\\\\n", + "\t5962 & 5.908174 & 0.6820468 & 50.96963 & 5.496989e-174 & 5.855805e-170 & 380.0420\\\\\n", + "\t6300 & 5.497805 & 0.7601863 & 43.29996 & 1.270316e-150 & 1.082589e-146 & 328.4099\\\\\n", + "\t34357 & 5.636847 & 1.4625119 & 42.43349 & 8.258637e-148 & 5.865146e-144 & 321.4354\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ @@ -2122,22 +1738,22 @@ "\n", "| | logFC <dbl> | AveExpr <dbl> | t <dbl> | P.Value <dbl> | adj.P.Val <dbl> | B <dbl> |\n", "|---|---|---|---|---|---|---|\n", - "| 19076 | 5.878948 | 0.4623452 | 54.32893 | 2.071534e-183 | 8.827015e-179 | 398.2269 |\n", - "| 5965 | 6.088533 | 0.5974117 | 52.39644 | 4.863173e-178 | 1.036123e-173 | 386.6747 |\n", - "| 19070 | 5.428275 | 0.3502684 | 50.18419 | 1.049979e-171 | 1.491355e-167 | 372.9366 |\n", - "| 5962 | 5.904402 | 0.6820468 | 45.76377 | 2.050934e-158 | 2.184809e-154 | 344.0765 |\n", - "| 6300 | 5.499113 | 0.7601863 | 38.95927 | 3.827125e-136 | 3.261552e-132 | 295.0173 |\n", - "| 34357 | 5.634145 | 1.4625119 | 38.17254 | 2.041761e-133 | 1.450024e-129 | 288.3820 |\n", + "| 19076 | 5.882004 | 0.4623452 | 59.93929 | 3.249431e-198 | 1.384615e-193 | 432.7360 |\n", + "| 5965 | 6.095896 | 0.5974117 | 58.21383 | 8.730492e-194 | 1.860075e-189 | 423.1656 |\n", + "| 19070 | 5.434536 | 0.3502684 | 55.61250 | 6.602101e-187 | 9.377404e-183 | 408.1777 |\n", + "| 5962 | 5.908174 | 0.6820468 | 50.96963 | 5.496989e-174 | 5.855805e-170 | 380.0420 |\n", + "| 6300 | 5.497805 | 0.7601863 | 43.29996 | 1.270316e-150 | 1.082589e-146 | 328.4099 |\n", + "| 34357 | 5.636847 | 1.4625119 | 42.43349 | 8.258637e-148 | 5.865146e-144 | 321.4354 |\n", "\n" ], "text/plain": [ " logFC AveExpr t P.Value adj.P.Val B \n", - "19076 5.878948 0.4623452 54.32893 2.071534e-183 8.827015e-179 398.2269\n", - "5965 6.088533 0.5974117 52.39644 4.863173e-178 1.036123e-173 386.6747\n", - "19070 5.428275 0.3502684 50.18419 1.049979e-171 1.491355e-167 372.9366\n", - "5962 5.904402 0.6820468 45.76377 2.050934e-158 2.184809e-154 344.0765\n", - "6300 5.499113 0.7601863 38.95927 3.827125e-136 3.261552e-132 295.0173\n", - "34357 5.634145 1.4625119 38.17254 2.041761e-133 1.450024e-129 288.3820" + "19076 5.882004 0.4623452 59.93929 3.249431e-198 1.384615e-193 432.7360\n", + "5965 6.095896 0.5974117 58.21383 8.730492e-194 1.860075e-189 423.1656\n", + "19070 5.434536 0.3502684 55.61250 6.602101e-187 9.377404e-183 408.1777\n", + "5962 5.908174 0.6820468 50.96963 5.496989e-174 5.855805e-170 380.0420\n", + "6300 5.497805 0.7601863 43.29996 1.270316e-150 1.082589e-146 328.4099\n", + "34357 5.636847 1.4625119 42.43349 8.258637e-148 5.865146e-144 321.4354" ] }, "metadata": {}, @@ -2150,7 +1766,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2272,30 +1888,30 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
  1. '19076'
  2. '10150'
  3. '5965'
  4. '34357'
  5. '19070'
  6. '5963'
\n" + "
  1. '10150'
  2. '19076'
  3. '5965'
  4. '34357'
  5. '5963'
  6. '19070'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", - "\\item '19076'\n", "\\item '10150'\n", + "\\item '19076'\n", "\\item '5965'\n", "\\item '34357'\n", - "\\item '19070'\n", "\\item '5963'\n", + "\\item '19070'\n", "\\end{enumerate*}\n" ], "text/markdown": [ - "1. '19076'\n", - "2. '10150'\n", + "1. '10150'\n", + "2. '19076'\n", "3. '5965'\n", "4. '34357'\n", - "5. '19070'\n", - "6. '5963'\n", + "5. '5963'\n", + "6. '19070'\n", "\n", "\n" ], "text/plain": [ - "[1] \"19076\" \"10150\" \"5965\" \"34357\" \"19070\" \"5963\" " + "[1] \"10150\" \"19076\" \"5965\" \"34357\" \"5963\" \"19070\"" ] }, "metadata": {}, @@ -2384,7 +2000,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -2464,12 +2080,12 @@ "\t<int><fct><fct><fct><fct><int><int><int><int><int><int>\n", "\n", "\n", - "\t1907619076ENSG00000183878.15UTY chrY-133060371330611213305398133055471330618513306250\n", "\t1015010150ENSG00000229807.11XIST chrX-738332377383337473831065738312747383743973841474\n", + "\t1907619076ENSG00000183878.15UTY chrY-133060371330611213305398133055471330618513306250\n", "\t5965 5965ENSG00000012817.15KDM5D chrY-197413171974148819739527197396621974173419741857\n", "\t3435734357ENSG00000198692.10EIF1AYchrY+205844732058452420582589205826932058802320588105\n", - "\t1907019070ENSG00000183878.15UTY chrY-132510161325118713248378132498821326027713260404\n", "\t5963 5963ENSG00000012817.15KDM5D chrY-197395271973966219735620197357501974131719741488\n", + "\t1907019070ENSG00000183878.15UTY chrY-132510161325118713248378132498821326027713260404\n", "\n", "\n" ], @@ -2479,12 +2095,12 @@ " & ID & GeneID & geneSymbol & chr & strand & exonStart\\_0base & exonEnd & upstreamES & upstreamEE & downstreamES & downstreamEE\\\\\n", " & & & & & & & & & & & \\\\\n", "\\hline\n", - "\t19076 & 19076 & ENSG00000183878.15 & UTY & chrY & - & 13306037 & 13306112 & 13305398 & 13305547 & 13306185 & 13306250\\\\\n", "\t10150 & 10150 & ENSG00000229807.11 & XIST & chrX & - & 73833237 & 73833374 & 73831065 & 73831274 & 73837439 & 73841474\\\\\n", + "\t19076 & 19076 & ENSG00000183878.15 & UTY & chrY & - & 13306037 & 13306112 & 13305398 & 13305547 & 13306185 & 13306250\\\\\n", "\t5965 & 5965 & ENSG00000012817.15 & KDM5D & chrY & - & 19741317 & 19741488 & 19739527 & 19739662 & 19741734 & 19741857\\\\\n", "\t34357 & 34357 & ENSG00000198692.10 & EIF1AY & chrY & + & 20584473 & 20584524 & 20582589 & 20582693 & 20588023 & 20588105\\\\\n", - "\t19070 & 19070 & ENSG00000183878.15 & UTY & chrY & - & 13251016 & 13251187 & 13248378 & 13249882 & 13260277 & 13260404\\\\\n", "\t5963 & 5963 & ENSG00000012817.15 & KDM5D & chrY & - & 19739527 & 19739662 & 19735620 & 19735750 & 19741317 & 19741488\\\\\n", + "\t19070 & 19070 & ENSG00000183878.15 & UTY & chrY & - & 13251016 & 13251187 & 13248378 & 13249882 & 13260277 & 13260404\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ @@ -2493,29 +2109,29 @@ "\n", "| | ID <int> | GeneID <fct> | geneSymbol <fct> | chr <fct> | strand <fct> | exonStart_0base <int> | exonEnd <int> | upstreamES <int> | upstreamEE <int> | downstreamES <int> | downstreamEE <int> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 19076 | 19076 | ENSG00000183878.15 | UTY | chrY | - | 13306037 | 13306112 | 13305398 | 13305547 | 13306185 | 13306250 |\n", "| 10150 | 10150 | ENSG00000229807.11 | XIST | chrX | - | 73833237 | 73833374 | 73831065 | 73831274 | 73837439 | 73841474 |\n", + "| 19076 | 19076 | ENSG00000183878.15 | UTY | chrY | - | 13306037 | 13306112 | 13305398 | 13305547 | 13306185 | 13306250 |\n", "| 5965 | 5965 | ENSG00000012817.15 | KDM5D | chrY | - | 19741317 | 19741488 | 19739527 | 19739662 | 19741734 | 19741857 |\n", "| 34357 | 34357 | ENSG00000198692.10 | EIF1AY | chrY | + | 20584473 | 20584524 | 20582589 | 20582693 | 20588023 | 20588105 |\n", - "| 19070 | 19070 | ENSG00000183878.15 | UTY | chrY | - | 13251016 | 13251187 | 13248378 | 13249882 | 13260277 | 13260404 |\n", "| 5963 | 5963 | ENSG00000012817.15 | KDM5D | chrY | - | 19739527 | 19739662 | 19735620 | 19735750 | 19741317 | 19741488 |\n", + "| 19070 | 19070 | ENSG00000183878.15 | UTY | chrY | - | 13251016 | 13251187 | 13248378 | 13249882 | 13260277 | 13260404 |\n", "\n" ], "text/plain": [ " ID GeneID geneSymbol chr strand exonStart_0base exonEnd \n", - "19076 19076 ENSG00000183878.15 UTY chrY - 13306037 13306112\n", "10150 10150 ENSG00000229807.11 XIST chrX - 73833237 73833374\n", + "19076 19076 ENSG00000183878.15 UTY chrY - 13306037 13306112\n", "5965 5965 ENSG00000012817.15 KDM5D chrY - 19741317 19741488\n", "34357 34357 ENSG00000198692.10 EIF1AY chrY + 20584473 20584524\n", - "19070 19070 ENSG00000183878.15 UTY chrY - 13251016 13251187\n", "5963 5963 ENSG00000012817.15 KDM5D chrY - 19739527 19739662\n", + "19070 19070 ENSG00000183878.15 UTY chrY - 13251016 13251187\n", " upstreamES upstreamEE downstreamES downstreamEE\n", - "19076 13305398 13305547 13306185 13306250 \n", "10150 73831065 73831274 73837439 73841474 \n", + "19076 13305398 13305547 13306185 13306250 \n", "5965 19739527 19739662 19741734 19741857 \n", "34357 20582589 20582693 20588023 20588105 \n", - "19070 13248378 13249882 13260277 13260404 \n", - "5963 19735620 19735750 19741317 19741488 " + "5963 19735620 19735750 19741317 19741488 \n", + "19070 13248378 13249882 13260277 13260404 " ] }, "metadata": {}, @@ -2670,7 +2286,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2750,12 +2366,12 @@ "\t<int><fct><fct><fct><fct><int><int><int><int><int><int>\n", "\n", "\n", - "\t1907619076ENSG00000183878.15UTY chrY-133060371330611213305398133055471330618513306250\n", "\t1015010150ENSG00000229807.11XIST chrX-738332377383337473831065738312747383743973841474\n", + "\t1907619076ENSG00000183878.15UTY chrY-133060371330611213305398133055471330618513306250\n", "\t5965 5965ENSG00000012817.15KDM5D chrY-197413171974148819739527197396621974173419741857\n", "\t3435734357ENSG00000198692.10EIF1AYchrY+205844732058452420582589205826932058802320588105\n", - "\t1907019070ENSG00000183878.15UTY chrY-132510161325118713248378132498821326027713260404\n", "\t5963 5963ENSG00000012817.15KDM5D chrY-197395271973966219735620197357501974131719741488\n", + "\t1907019070ENSG00000183878.15UTY chrY-132510161325118713248378132498821326027713260404\n", "\n", "\n" ], @@ -2765,12 +2381,12 @@ " & ID & GeneID & geneSymbol & chr & strand & exonStart\\_0base & exonEnd & upstreamES & upstreamEE & downstreamES & downstreamEE\\\\\n", " & & & & & & & & & & & \\\\\n", "\\hline\n", - "\t19076 & 19076 & ENSG00000183878.15 & UTY & chrY & - & 13306037 & 13306112 & 13305398 & 13305547 & 13306185 & 13306250\\\\\n", "\t10150 & 10150 & ENSG00000229807.11 & XIST & chrX & - & 73833237 & 73833374 & 73831065 & 73831274 & 73837439 & 73841474\\\\\n", + "\t19076 & 19076 & ENSG00000183878.15 & UTY & chrY & - & 13306037 & 13306112 & 13305398 & 13305547 & 13306185 & 13306250\\\\\n", "\t5965 & 5965 & ENSG00000012817.15 & KDM5D & chrY & - & 19741317 & 19741488 & 19739527 & 19739662 & 19741734 & 19741857\\\\\n", "\t34357 & 34357 & ENSG00000198692.10 & EIF1AY & chrY & + & 20584473 & 20584524 & 20582589 & 20582693 & 20588023 & 20588105\\\\\n", - "\t19070 & 19070 & ENSG00000183878.15 & UTY & chrY & - & 13251016 & 13251187 & 13248378 & 13249882 & 13260277 & 13260404\\\\\n", "\t5963 & 5963 & ENSG00000012817.15 & KDM5D & chrY & - & 19739527 & 19739662 & 19735620 & 19735750 & 19741317 & 19741488\\\\\n", + "\t19070 & 19070 & ENSG00000183878.15 & UTY & chrY & - & 13251016 & 13251187 & 13248378 & 13249882 & 13260277 & 13260404\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ @@ -2779,29 +2395,29 @@ "\n", "| | ID <int> | GeneID <fct> | geneSymbol <fct> | chr <fct> | strand <fct> | exonStart_0base <int> | exonEnd <int> | upstreamES <int> | upstreamEE <int> | downstreamES <int> | downstreamEE <int> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| 19076 | 19076 | ENSG00000183878.15 | UTY | chrY | - | 13306037 | 13306112 | 13305398 | 13305547 | 13306185 | 13306250 |\n", "| 10150 | 10150 | ENSG00000229807.11 | XIST | chrX | - | 73833237 | 73833374 | 73831065 | 73831274 | 73837439 | 73841474 |\n", + "| 19076 | 19076 | ENSG00000183878.15 | UTY | chrY | - | 13306037 | 13306112 | 13305398 | 13305547 | 13306185 | 13306250 |\n", "| 5965 | 5965 | ENSG00000012817.15 | KDM5D | chrY | - | 19741317 | 19741488 | 19739527 | 19739662 | 19741734 | 19741857 |\n", "| 34357 | 34357 | ENSG00000198692.10 | EIF1AY | chrY | + | 20584473 | 20584524 | 20582589 | 20582693 | 20588023 | 20588105 |\n", - "| 19070 | 19070 | ENSG00000183878.15 | UTY | chrY | - | 13251016 | 13251187 | 13248378 | 13249882 | 13260277 | 13260404 |\n", "| 5963 | 5963 | ENSG00000012817.15 | KDM5D | chrY | - | 19739527 | 19739662 | 19735620 | 19735750 | 19741317 | 19741488 |\n", + "| 19070 | 19070 | ENSG00000183878.15 | UTY | chrY | - | 13251016 | 13251187 | 13248378 | 13249882 | 13260277 | 13260404 |\n", "\n" ], "text/plain": [ " ID GeneID geneSymbol chr strand exonStart_0base exonEnd \n", - "19076 19076 ENSG00000183878.15 UTY chrY - 13306037 13306112\n", "10150 10150 ENSG00000229807.11 XIST chrX - 73833237 73833374\n", + "19076 19076 ENSG00000183878.15 UTY chrY - 13306037 13306112\n", "5965 5965 ENSG00000012817.15 KDM5D chrY - 19741317 19741488\n", "34357 34357 ENSG00000198692.10 EIF1AY chrY + 20584473 20584524\n", - "19070 19070 ENSG00000183878.15 UTY chrY - 13251016 13251187\n", "5963 5963 ENSG00000012817.15 KDM5D chrY - 19739527 19739662\n", + "19070 19070 ENSG00000183878.15 UTY chrY - 13251016 13251187\n", " upstreamES upstreamEE downstreamES downstreamEE\n", - "19076 13305398 13305547 13306185 13306250 \n", "10150 73831065 73831274 73837439 73841474 \n", + "19076 13305398 13305547 13306185 13306250 \n", "5965 19739527 19739662 19741734 19741857 \n", "34357 20582589 20582693 20588023 20588105 \n", - "19070 13248378 13249882 13260277 13260404 \n", - "5963 19735620 19735750 19741317 19741488 " + "5963 19735620 19735750 19741317 19741488 \n", + "19070 13248378 13249882 13260277 13260404 " ] }, "metadata": {}, @@ -2955,7 +2571,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -3004,31 +2620,31 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
  1. 'UTY-19076'
  2. 'XIST-10150'
  3. 'KDM5D-5965'
  4. 'EIF1AY-34357'
  5. 'UTY-19070'
  6. 'KDM5D-5963'
\n" + "
  1. 'XIST-10150'
  2. 'UTY-19076'
  3. 'KDM5D-5965'
  4. 'EIF1AY-34357'
  5. 'KDM5D-5963'
  6. 'UTY-19070'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", - "\\item 'UTY-19076'\n", "\\item 'XIST-10150'\n", + "\\item 'UTY-19076'\n", "\\item 'KDM5D-5965'\n", "\\item 'EIF1AY-34357'\n", - "\\item 'UTY-19070'\n", "\\item 'KDM5D-5963'\n", + "\\item 'UTY-19070'\n", "\\end{enumerate*}\n" ], "text/markdown": [ - "1. 'UTY-19076'\n", - "2. 'XIST-10150'\n", + "1. 'XIST-10150'\n", + "2. 'UTY-19076'\n", "3. 'KDM5D-5965'\n", "4. 'EIF1AY-34357'\n", - "5. 'UTY-19070'\n", - "6. 'KDM5D-5963'\n", + "5. 'KDM5D-5963'\n", + "6. 'UTY-19070'\n", "\n", "\n" ], "text/plain": [ - "[1] \"UTY-19076\" \"XIST-10150\" \"KDM5D-5965\" \"EIF1AY-34357\" \"UTY-19070\" \n", - "[6] \"KDM5D-5963\" " + "[1] \"XIST-10150\" \"UTY-19076\" \"KDM5D-5965\" \"EIF1AY-34357\" \"KDM5D-5963\" \n", + "[6] \"UTY-19070\" " ] }, "metadata": {}, @@ -3138,7 +2754,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -3150,7 +2766,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -3177,7 +2793,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -3223,14 +2839,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Generating sha256 checksums of the artefacts in the `..data/` directory .. \n" + "Generating sha256 checksums of the artefacts in the `..data/` directory .. \n", + "\n" ] }, { @@ -3251,72 +2868,196 @@ "text": [ "Done!\n", "\n", - "Warning message in data.table::fread(paste0(\"../metadata/\", notebookid, \"_sha256sums.txt\"), :\n", - "“Stopped early on line 7. Expected 2 fields but found 5. Consider fill=TRUE and comment.char=. First discarded non-empty line: <>”" + "\n" ] }, { "data": { "text/html": [ "\n", - "\n", + "\n", "\n", "\t\n", "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A data.table: 6 × 2A data.table: 31 × 2
sha256sumfile
<chr><chr>
d03c22e998eae9c96ab6e07340266fdff0d8f4f896fe7bd62ab80bb5aa7857f1./BreastMammaryTissue_DGE_ijc_sex.csv
ebe1713963cd88e7bb2ac961e7d7209b38d2b0e8136b28fe4425ac8d04ad0b18./rmats_final.mxe.jcec.skiplen.txt.gz
6e47d3c15687f037426a6678e877227da2431f9ad20de41914e1fbb1701b32ad./BreastMammaryTissue_sex_as_events_universe.txt
490afac0f9797126ea051b167f409bbc011a8bdbe190b991ce0da972c5424cbf./Cells-EBV-transformedlymphocytes_DGE.csv
38307c2edf8434a57dc1113575887aafd735a529412046ba10f46c716fedcd2f./Brain-CerebellarHemisphere_DGE.csv
b8347e8cd7fc16b23041a2809aa38e1590813c8858c6a2aa14f8fc4c134fba3e./.ipynb_checkpoints/BreastMammaryTissue,.DGE_isoforms_robust-checkpoint.csv
ccf16742fe3db84abc66404b4c50981e6dee33b0de3f481111a2b44f6f931813./BreastMammaryTissue_DGE_ijc_sex.csv
d9b424c4c6a3084bf89e21ee0c0a806a882f0dac448b3c0bf61c8bfe1b002f58./BreastMammaryTissue_sex_as_events_universe.txt
741763f61d32c98446317ad30c45cdb3199039b0c696ab83461eb91b0e8611fe./BreastMammaryTissue_DGE_sjc_sex.csv
28764f9f1e0f14e399d555e448b5490c7221454e7d34d220f79b537376ca017b./fromGTF.novelEvents.SE.txt
e532e4d8ee0f5e5ba221017a1c07c5b1376f7d711aacbba5cc713badd8bd3e47./BreastMammaryTissue_ijc_sex_universe.txt
fb7fced98c23d7f0e69f820a8cf1616c3a2a88386074ae5a14690f313ae210d0./BreastMammaryTissue_sjc_sex_universe.txt
b408d18141c7de21b42d5fa78e442e482d6c249b90b45800271f5124c11dd3d3./BreastMammaryTissue_sex_gene_set.txt
5146c849c9354129590c59da5e6040a074523b4827d0975e3f5fa43737af7e9a./README.md
efe91f83b5b3d4a6497e7265979ec2616f1e330005b3117fbd6e0da0c4b37c79./BreastMammaryTissue_DGE_sjc_sex_refined.csv
eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6./fromGTF.novelEvents.A5SS.txt
94df4d4e2ab738e06c68e19e7f9eaefba64389a65827726b127713dbd20a7c30./BreastMammaryTissue_sex_universe.txt
eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6./fromGTF.novelEvents.A3SS.txt
5ded52e3f6c0c09b7fe09e81a5bc58bf6d9c4afdaed3d19a159b9dfb0c49ea4d./BreastMammaryTissue_DGE_sex_as_events_refined.csv
be32c4925ad8ebf7414623d68e390a1631e08f4459c189f7f346040a542320b5./fromGTF.RI.txt
c14069b1ed8642720b742864ca6b930b13d35e5a41592dabd9ba0b2dcb953783./fromGTF.novelEvents.RI.txt
dd188bc93b89167bfd8faa80323ca753b1a9557574e7157e6a6f9afe7f48e1b3./SraRunTable.noCram.noExome.noWGS.totalRNA.txt.gz
63888f391d15f13590b9bcb9d472af4c438947122e63eedca232b1b25b082ebe./BreastMammaryTissue_ijc_sex_gene_set.txt
6426a7ecd614d3aeec414aac830d415c4c23e032df075737429f44546aabeee8./BreastMammaryTissue_DGE_ijc_sex_refined.csv
5cfa91f71a21538245f3b5c198fc45f8652c899bbce2fd5ad9bd52a8a08566b6./fromGTF.MXE.txt
d7639443651c3b508772ee89728f8ab41d62414672c41e68a78947be14e07cd6./rmats_final.se.jc.sjc.txt.gz
68ea5ae95f3ca659e86b685304361957d183bf3273f8e74b276d5295d275686f./BreastMammaryTissue_sex_as_events_gene_set.txt
bfd9fa65689a7d157b09b5efd472f235b627f85a73b9f0493c881882acce773d./fromGTF.tar.gz
849658f50abd888f8708a1ffd65ff523a14d1d5eab194583bed1caebd5cac92a./rmats_final.se.jc.ijc.txt.gz
22cb3faa337ae24b7b423a985fd2056047400eccb83b716eff2e33cce9159e53./fromGTF.SE.txt
b2e407257b128afc0765497a4a21cf371bf4e3ed28c9532d3d55d32518e329e4./BreastMammaryTissue_sjc_sex_gene_set.txt
c277ab764b652e8d65e07277df72933fee465806a5e6c1b018509f868ccfddad./fromGTF.A5SS.txt
6e3f1d335ce05c887363d092b0100b9f3603a964c8fe6e8c20f4c547b4f52280./fromGTF.novelEvents.MXE.txt
51c8250a1796196e3879d88186c5450e5b5efc1e9f3fa578f388e9ff380e2deb./fromGTF.A3SS.txt
f31a68097c9133a5fd11d32c4d1194b59442366e50e627189ac1cbcdb6e7f13f./BreastMammaryTissue_DGE_sex_refined.csv
98001376224e926573071d352f6b22fc6a2dbcca33a16ba88573b49375b90699./BreastMammaryTissue_DGE_sex_as_events.csv
969def456d3d089241b56f95bc23dfa4205334f25989485a4febb4dcccccf891./BreastMammaryTissue_DGE_sex.csv
\n" ], "text/latex": [ - "A data.table: 6 × 2\n", + "A data.table: 31 × 2\n", "\\begin{tabular}{ll}\n", " sha256sum & file\\\\\n", " & \\\\\n", "\\hline\n", - "\t d03c22e998eae9c96ab6e07340266fdff0d8f4f896fe7bd62ab80bb5aa7857f1 & ./BreastMammaryTissue\\_DGE\\_ijc\\_sex.csv \\\\\n", - "\t ebe1713963cd88e7bb2ac961e7d7209b38d2b0e8136b28fe4425ac8d04ad0b18 & ./rmats\\_final.mxe.jcec.skiplen.txt.gz \\\\\n", - "\t 6e47d3c15687f037426a6678e877227da2431f9ad20de41914e1fbb1701b32ad & ./BreastMammaryTissue\\_sex\\_as\\_events\\_universe.txt \\\\\n", - "\t 490afac0f9797126ea051b167f409bbc011a8bdbe190b991ce0da972c5424cbf & ./Cells-EBV-transformedlymphocytes\\_DGE.csv \\\\\n", - "\t 38307c2edf8434a57dc1113575887aafd735a529412046ba10f46c716fedcd2f & ./Brain-CerebellarHemisphere\\_DGE.csv \\\\\n", - "\t b8347e8cd7fc16b23041a2809aa38e1590813c8858c6a2aa14f8fc4c134fba3e & ./.ipynb\\_checkpoints/BreastMammaryTissue,.DGE\\_isoforms\\_robust-checkpoint.csv\\\\\n", + "\t ccf16742fe3db84abc66404b4c50981e6dee33b0de3f481111a2b44f6f931813 & ./BreastMammaryTissue\\_DGE\\_ijc\\_sex.csv \\\\\n", + "\t d9b424c4c6a3084bf89e21ee0c0a806a882f0dac448b3c0bf61c8bfe1b002f58 & ./BreastMammaryTissue\\_sex\\_as\\_events\\_universe.txt \\\\\n", + "\t 741763f61d32c98446317ad30c45cdb3199039b0c696ab83461eb91b0e8611fe & ./BreastMammaryTissue\\_DGE\\_sjc\\_sex.csv \\\\\n", + "\t 28764f9f1e0f14e399d555e448b5490c7221454e7d34d220f79b537376ca017b & ./fromGTF.novelEvents.SE.txt \\\\\n", + "\t e532e4d8ee0f5e5ba221017a1c07c5b1376f7d711aacbba5cc713badd8bd3e47 & ./BreastMammaryTissue\\_ijc\\_sex\\_universe.txt \\\\\n", + "\t fb7fced98c23d7f0e69f820a8cf1616c3a2a88386074ae5a14690f313ae210d0 & ./BreastMammaryTissue\\_sjc\\_sex\\_universe.txt \\\\\n", + "\t b408d18141c7de21b42d5fa78e442e482d6c249b90b45800271f5124c11dd3d3 & ./BreastMammaryTissue\\_sex\\_gene\\_set.txt \\\\\n", + "\t 5146c849c9354129590c59da5e6040a074523b4827d0975e3f5fa43737af7e9a & ./README.md \\\\\n", + "\t efe91f83b5b3d4a6497e7265979ec2616f1e330005b3117fbd6e0da0c4b37c79 & ./BreastMammaryTissue\\_DGE\\_sjc\\_sex\\_refined.csv \\\\\n", + "\t eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6 & ./fromGTF.novelEvents.A5SS.txt \\\\\n", + "\t 94df4d4e2ab738e06c68e19e7f9eaefba64389a65827726b127713dbd20a7c30 & ./BreastMammaryTissue\\_sex\\_universe.txt \\\\\n", + "\t eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6 & ./fromGTF.novelEvents.A3SS.txt \\\\\n", + "\t 5ded52e3f6c0c09b7fe09e81a5bc58bf6d9c4afdaed3d19a159b9dfb0c49ea4d & ./BreastMammaryTissue\\_DGE\\_sex\\_as\\_events\\_refined.csv\\\\\n", + "\t be32c4925ad8ebf7414623d68e390a1631e08f4459c189f7f346040a542320b5 & ./fromGTF.RI.txt \\\\\n", + "\t c14069b1ed8642720b742864ca6b930b13d35e5a41592dabd9ba0b2dcb953783 & ./fromGTF.novelEvents.RI.txt \\\\\n", + "\t dd188bc93b89167bfd8faa80323ca753b1a9557574e7157e6a6f9afe7f48e1b3 & ./SraRunTable.noCram.noExome.noWGS.totalRNA.txt.gz \\\\\n", + "\t 63888f391d15f13590b9bcb9d472af4c438947122e63eedca232b1b25b082ebe & ./BreastMammaryTissue\\_ijc\\_sex\\_gene\\_set.txt \\\\\n", + "\t 6426a7ecd614d3aeec414aac830d415c4c23e032df075737429f44546aabeee8 & ./BreastMammaryTissue\\_DGE\\_ijc\\_sex\\_refined.csv \\\\\n", + "\t 5cfa91f71a21538245f3b5c198fc45f8652c899bbce2fd5ad9bd52a8a08566b6 & ./fromGTF.MXE.txt \\\\\n", + "\t d7639443651c3b508772ee89728f8ab41d62414672c41e68a78947be14e07cd6 & ./rmats\\_final.se.jc.sjc.txt.gz \\\\\n", + "\t 68ea5ae95f3ca659e86b685304361957d183bf3273f8e74b276d5295d275686f & ./BreastMammaryTissue\\_sex\\_as\\_events\\_gene\\_set.txt \\\\\n", + "\t bfd9fa65689a7d157b09b5efd472f235b627f85a73b9f0493c881882acce773d & ./fromGTF.tar.gz \\\\\n", + "\t 849658f50abd888f8708a1ffd65ff523a14d1d5eab194583bed1caebd5cac92a & ./rmats\\_final.se.jc.ijc.txt.gz \\\\\n", + "\t 22cb3faa337ae24b7b423a985fd2056047400eccb83b716eff2e33cce9159e53 & ./fromGTF.SE.txt \\\\\n", + "\t b2e407257b128afc0765497a4a21cf371bf4e3ed28c9532d3d55d32518e329e4 & ./BreastMammaryTissue\\_sjc\\_sex\\_gene\\_set.txt \\\\\n", + "\t c277ab764b652e8d65e07277df72933fee465806a5e6c1b018509f868ccfddad & ./fromGTF.A5SS.txt \\\\\n", + "\t 6e3f1d335ce05c887363d092b0100b9f3603a964c8fe6e8c20f4c547b4f52280 & ./fromGTF.novelEvents.MXE.txt \\\\\n", + "\t 51c8250a1796196e3879d88186c5450e5b5efc1e9f3fa578f388e9ff380e2deb & ./fromGTF.A3SS.txt \\\\\n", + "\t f31a68097c9133a5fd11d32c4d1194b59442366e50e627189ac1cbcdb6e7f13f & ./BreastMammaryTissue\\_DGE\\_sex\\_refined.csv \\\\\n", + "\t 98001376224e926573071d352f6b22fc6a2dbcca33a16ba88573b49375b90699 & ./BreastMammaryTissue\\_DGE\\_sex\\_as\\_events.csv \\\\\n", + "\t 969def456d3d089241b56f95bc23dfa4205334f25989485a4febb4dcccccf891 & ./BreastMammaryTissue\\_DGE\\_sex.csv \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A data.table: 6 × 2\n", + "A data.table: 31 × 2\n", "\n", "| sha256sum <chr> | file <chr> |\n", "|---|---|\n", - "| d03c22e998eae9c96ab6e07340266fdff0d8f4f896fe7bd62ab80bb5aa7857f1 | ./BreastMammaryTissue_DGE_ijc_sex.csv |\n", - "| ebe1713963cd88e7bb2ac961e7d7209b38d2b0e8136b28fe4425ac8d04ad0b18 | ./rmats_final.mxe.jcec.skiplen.txt.gz |\n", - "| 6e47d3c15687f037426a6678e877227da2431f9ad20de41914e1fbb1701b32ad | ./BreastMammaryTissue_sex_as_events_universe.txt |\n", - "| 490afac0f9797126ea051b167f409bbc011a8bdbe190b991ce0da972c5424cbf | ./Cells-EBV-transformedlymphocytes_DGE.csv |\n", - "| 38307c2edf8434a57dc1113575887aafd735a529412046ba10f46c716fedcd2f | ./Brain-CerebellarHemisphere_DGE.csv |\n", - "| b8347e8cd7fc16b23041a2809aa38e1590813c8858c6a2aa14f8fc4c134fba3e | ./.ipynb_checkpoints/BreastMammaryTissue,.DGE_isoforms_robust-checkpoint.csv |\n", + "| ccf16742fe3db84abc66404b4c50981e6dee33b0de3f481111a2b44f6f931813 | ./BreastMammaryTissue_DGE_ijc_sex.csv |\n", + "| d9b424c4c6a3084bf89e21ee0c0a806a882f0dac448b3c0bf61c8bfe1b002f58 | ./BreastMammaryTissue_sex_as_events_universe.txt |\n", + "| 741763f61d32c98446317ad30c45cdb3199039b0c696ab83461eb91b0e8611fe | ./BreastMammaryTissue_DGE_sjc_sex.csv |\n", + "| 28764f9f1e0f14e399d555e448b5490c7221454e7d34d220f79b537376ca017b | ./fromGTF.novelEvents.SE.txt |\n", + "| e532e4d8ee0f5e5ba221017a1c07c5b1376f7d711aacbba5cc713badd8bd3e47 | ./BreastMammaryTissue_ijc_sex_universe.txt |\n", + "| fb7fced98c23d7f0e69f820a8cf1616c3a2a88386074ae5a14690f313ae210d0 | ./BreastMammaryTissue_sjc_sex_universe.txt |\n", + "| b408d18141c7de21b42d5fa78e442e482d6c249b90b45800271f5124c11dd3d3 | ./BreastMammaryTissue_sex_gene_set.txt |\n", + "| 5146c849c9354129590c59da5e6040a074523b4827d0975e3f5fa43737af7e9a | ./README.md |\n", + "| efe91f83b5b3d4a6497e7265979ec2616f1e330005b3117fbd6e0da0c4b37c79 | ./BreastMammaryTissue_DGE_sjc_sex_refined.csv |\n", + "| eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6 | ./fromGTF.novelEvents.A5SS.txt |\n", + "| 94df4d4e2ab738e06c68e19e7f9eaefba64389a65827726b127713dbd20a7c30 | ./BreastMammaryTissue_sex_universe.txt |\n", + "| eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6 | ./fromGTF.novelEvents.A3SS.txt |\n", + "| 5ded52e3f6c0c09b7fe09e81a5bc58bf6d9c4afdaed3d19a159b9dfb0c49ea4d | ./BreastMammaryTissue_DGE_sex_as_events_refined.csv |\n", + "| be32c4925ad8ebf7414623d68e390a1631e08f4459c189f7f346040a542320b5 | ./fromGTF.RI.txt |\n", + "| c14069b1ed8642720b742864ca6b930b13d35e5a41592dabd9ba0b2dcb953783 | ./fromGTF.novelEvents.RI.txt |\n", + "| dd188bc93b89167bfd8faa80323ca753b1a9557574e7157e6a6f9afe7f48e1b3 | ./SraRunTable.noCram.noExome.noWGS.totalRNA.txt.gz |\n", + "| 63888f391d15f13590b9bcb9d472af4c438947122e63eedca232b1b25b082ebe | ./BreastMammaryTissue_ijc_sex_gene_set.txt |\n", + "| 6426a7ecd614d3aeec414aac830d415c4c23e032df075737429f44546aabeee8 | ./BreastMammaryTissue_DGE_ijc_sex_refined.csv |\n", + "| 5cfa91f71a21538245f3b5c198fc45f8652c899bbce2fd5ad9bd52a8a08566b6 | ./fromGTF.MXE.txt |\n", + "| d7639443651c3b508772ee89728f8ab41d62414672c41e68a78947be14e07cd6 | ./rmats_final.se.jc.sjc.txt.gz |\n", + "| 68ea5ae95f3ca659e86b685304361957d183bf3273f8e74b276d5295d275686f | ./BreastMammaryTissue_sex_as_events_gene_set.txt |\n", + "| bfd9fa65689a7d157b09b5efd472f235b627f85a73b9f0493c881882acce773d | ./fromGTF.tar.gz |\n", + "| 849658f50abd888f8708a1ffd65ff523a14d1d5eab194583bed1caebd5cac92a | ./rmats_final.se.jc.ijc.txt.gz |\n", + "| 22cb3faa337ae24b7b423a985fd2056047400eccb83b716eff2e33cce9159e53 | ./fromGTF.SE.txt |\n", + "| b2e407257b128afc0765497a4a21cf371bf4e3ed28c9532d3d55d32518e329e4 | ./BreastMammaryTissue_sjc_sex_gene_set.txt |\n", + "| c277ab764b652e8d65e07277df72933fee465806a5e6c1b018509f868ccfddad | ./fromGTF.A5SS.txt |\n", + "| 6e3f1d335ce05c887363d092b0100b9f3603a964c8fe6e8c20f4c547b4f52280 | ./fromGTF.novelEvents.MXE.txt |\n", + "| 51c8250a1796196e3879d88186c5450e5b5efc1e9f3fa578f388e9ff380e2deb | ./fromGTF.A3SS.txt |\n", + "| f31a68097c9133a5fd11d32c4d1194b59442366e50e627189ac1cbcdb6e7f13f | ./BreastMammaryTissue_DGE_sex_refined.csv |\n", + "| 98001376224e926573071d352f6b22fc6a2dbcca33a16ba88573b49375b90699 | ./BreastMammaryTissue_DGE_sex_as_events.csv |\n", + "| 969def456d3d089241b56f95bc23dfa4205334f25989485a4febb4dcccccf891 | ./BreastMammaryTissue_DGE_sex.csv |\n", "\n" ], "text/plain": [ - 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"execution_count": 33, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -3350,10 +3091,14 @@ "output_type": "stream", "text": [ "Saving `devtools::session_info()` objects in ../metadata/devtools_session_info.rds ..\n", + "\n", "Done!\n", "\n", + "\n", "Saving `utils::sessionInfo()` objects in ../metadata/utils_session_info.rds ..\n", + "\n", "Done!\n", + "\n", "\n" ] }, @@ -3361,7 +3106,7 @@ "data": { "text/plain": [ " setting value \n", - " version R version 3.6.1 (2019-07-05)\n", + " version R version 3.6.2 (2019-12-12)\n", " os Ubuntu 18.04.3 LTS \n", " system x86_64, linux-gnu \n", " ui X11 \n", @@ -3369,7 +3114,7 @@ " collate en_US.UTF-8 \n", " ctype en_US.UTF-8 \n", " tz Etc/UTC \n", - " date 2020-05-10 " + " date 2020-05-13 " ] }, "metadata": {}, @@ -3379,56 +3124,62 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", "\t\n", "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A packages_info: 9 × 11A packages_info: 11 × 11
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BiobaseBiobase 2.46.02.46.0/opt/conda/lib/R/library/Biobase /opt/conda/lib/R/library/Biobase TRUEFALSE2019-10-29Bioconductor NA/opt/conda/lib/R/library
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limmalimma 3.42.03.42.0/opt/conda/lib/R/library/limma /opt/conda/lib/R/library/limma TRUEFALSE2019-10-29Bioconductor NA/opt/conda/lib/R/library
multtestmulttest 2.40.02.40.0/opt/conda/lib/R/library/multtest /opt/conda/lib/R/library/multtest TRUEFALSE2019-05-02Bioconductor NA/opt/conda/lib/R/library
R.methodsS3R.methodsS3 1.8.0 1.8.0 /opt/conda/lib/R/library/R.methodsS3 /opt/conda/lib/R/library/R.methodsS3 TRUEFALSE2020-02-14CRAN (R 3.6.1)NA/opt/conda/lib/R/library
R.ooR.oo 1.23.01.23.0/opt/conda/lib/R/library/R.oo /opt/conda/lib/R/library/R.oo TRUEFALSE2019-11-03CRAN (R 3.6.1)NA/opt/conda/lib/R/library
R.utilsR.utils 2.9.2 2.9.2 /opt/conda/lib/R/library/R.utils /opt/conda/lib/R/library/R.utils TRUEFALSE2019-12-08CRAN (R 3.6.1)NA/opt/conda/lib/R/library
tibbletibble 2.1.3 2.1.3 /opt/conda/lib/R/library/tibble /opt/conda/lib/R/library/tibble TRUEFALSE2019-06-06CRAN (R 3.6.1)NA/opt/conda/lib/R/library
BiobaseBiobase 2.46.0 2.46.0 /opt/conda/lib/R/library/Biobase /opt/conda/lib/R/library/Biobase TRUEFALSE2019-10-29Bioconductor NA/opt/conda/lib/R/library
BiocGenericsBiocGenerics0.32.0 0.32.0 /opt/conda/lib/R/library/BiocGenerics/opt/conda/lib/R/library/BiocGenericsTRUEFALSE2019-10-29Bioconductor NA/opt/conda/lib/R/library
edgeRedgeR 3.28.1 3.28.1 /opt/conda/lib/R/library/edgeR /opt/conda/lib/R/library/edgeR TRUEFALSE2020-02-26Bioconductor NA/opt/conda/lib/R/library
limmalimma 3.42.2 3.42.2 /opt/conda/lib/R/library/limma /opt/conda/lib/R/library/limma TRUEFALSE2020-02-03Bioconductor NA/opt/conda/lib/R/library
multtestmulttest 2.42.0 2.42.0 /opt/conda/lib/R/library/multtest /opt/conda/lib/R/library/multtest TRUEFALSE2019-10-29Bioconductor NA/opt/conda/lib/R/library
piggybackpiggyback 0.0.10.990.0.10.99/opt/conda/lib/R/library/piggyback /opt/conda/lib/R/library/piggyback TRUEFALSE2020-05-13Github (ropensci/piggyback@87f71e8)NA/opt/conda/lib/R/library
R.methodsS3R.methodsS3 1.8.0 1.8.0 /opt/conda/lib/R/library/R.methodsS3 /opt/conda/lib/R/library/R.methodsS3 TRUEFALSE2020-02-14CRAN (R 3.6.2) NA/opt/conda/lib/R/library
R.ooR.oo 1.23.0 1.23.0 /opt/conda/lib/R/library/R.oo /opt/conda/lib/R/library/R.oo TRUEFALSE2019-11-03CRAN (R 3.6.2) NA/opt/conda/lib/R/library
R.utilsR.utils 2.9.2 2.9.2 /opt/conda/lib/R/library/R.utils /opt/conda/lib/R/library/R.utils TRUEFALSE2019-12-08CRAN (R 3.6.2) NA/opt/conda/lib/R/library
statmodstatmod 1.4.34 1.4.34 /opt/conda/lib/R/library/statmod /opt/conda/lib/R/library/statmod TRUEFALSE2020-02-17CRAN (R 3.6.2) NA/opt/conda/lib/R/library
tibbletibble 2.1.3 2.1.3 /opt/conda/lib/R/library/tibble /opt/conda/lib/R/library/tibble TRUEFALSE2019-06-06CRAN (R 3.6.1) NA/opt/conda/lib/R/library
\n" ], "text/latex": [ - "A packages\\_info: 9 × 11\n", + "A packages\\_info: 11 × 11\n", "\\begin{tabular}{r|lllllllllll}\n", " & package & ondiskversion & loadedversion & path & loadedpath & attached & is\\_base & date & source & md5ok & library\\\\\n", " & & & & & & & & & & & \\\\\n", "\\hline\n", - "\tBiobase & Biobase & 2.46.0 & 2.46.0 & /opt/conda/lib/R/library/Biobase & /opt/conda/lib/R/library/Biobase & TRUE & FALSE & 2019-10-29 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", - "\tBiocGenerics & BiocGenerics & 0.32.0 & 0.32.0 & /opt/conda/lib/R/library/BiocGenerics & /opt/conda/lib/R/library/BiocGenerics & TRUE & FALSE & 2019-10-29 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", - "\tedgeR & edgeR & 3.28.1 & 3.28.1 & /opt/conda/lib/R/library/edgeR & /opt/conda/lib/R/library/edgeR & TRUE & FALSE & 2020-02-26 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", - "\tlimma & limma & 3.42.0 & 3.42.0 & /opt/conda/lib/R/library/limma & /opt/conda/lib/R/library/limma & TRUE & FALSE & 2019-10-29 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", - "\tmulttest & multtest & 2.40.0 & 2.40.0 & /opt/conda/lib/R/library/multtest & /opt/conda/lib/R/library/multtest & TRUE & FALSE & 2019-05-02 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", - "\tR.methodsS3 & R.methodsS3 & 1.8.0 & 1.8.0 & /opt/conda/lib/R/library/R.methodsS3 & /opt/conda/lib/R/library/R.methodsS3 & TRUE & FALSE & 2020-02-14 & CRAN (R 3.6.1) & NA & /opt/conda/lib/R/library\\\\\n", - "\tR.oo & R.oo & 1.23.0 & 1.23.0 & /opt/conda/lib/R/library/R.oo & /opt/conda/lib/R/library/R.oo & TRUE & FALSE & 2019-11-03 & CRAN (R 3.6.1) & NA & /opt/conda/lib/R/library\\\\\n", - "\tR.utils & R.utils & 2.9.2 & 2.9.2 & /opt/conda/lib/R/library/R.utils & /opt/conda/lib/R/library/R.utils & TRUE & FALSE & 2019-12-08 & CRAN (R 3.6.1) & NA & /opt/conda/lib/R/library\\\\\n", - "\ttibble & tibble & 2.1.3 & 2.1.3 & /opt/conda/lib/R/library/tibble & /opt/conda/lib/R/library/tibble & TRUE & FALSE & 2019-06-06 & CRAN (R 3.6.1) & NA & /opt/conda/lib/R/library\\\\\n", + "\tBiobase & Biobase & 2.46.0 & 2.46.0 & /opt/conda/lib/R/library/Biobase & /opt/conda/lib/R/library/Biobase & TRUE & FALSE & 2019-10-29 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", + "\tBiocGenerics & BiocGenerics & 0.32.0 & 0.32.0 & /opt/conda/lib/R/library/BiocGenerics & /opt/conda/lib/R/library/BiocGenerics & TRUE & FALSE & 2019-10-29 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", + "\tedgeR & edgeR & 3.28.1 & 3.28.1 & /opt/conda/lib/R/library/edgeR & /opt/conda/lib/R/library/edgeR & TRUE & FALSE & 2020-02-26 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", + "\tlimma & limma & 3.42.2 & 3.42.2 & /opt/conda/lib/R/library/limma & /opt/conda/lib/R/library/limma & TRUE & FALSE & 2020-02-03 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", + "\tmulttest & multtest & 2.42.0 & 2.42.0 & /opt/conda/lib/R/library/multtest & /opt/conda/lib/R/library/multtest & TRUE & FALSE & 2019-10-29 & Bioconductor & NA & /opt/conda/lib/R/library\\\\\n", + "\tpiggyback & piggyback & 0.0.10.99 & 0.0.10.99 & /opt/conda/lib/R/library/piggyback & /opt/conda/lib/R/library/piggyback & TRUE & FALSE & 2020-05-13 & Github (ropensci/piggyback@87f71e8) & NA & /opt/conda/lib/R/library\\\\\n", + "\tR.methodsS3 & R.methodsS3 & 1.8.0 & 1.8.0 & /opt/conda/lib/R/library/R.methodsS3 & /opt/conda/lib/R/library/R.methodsS3 & TRUE & FALSE & 2020-02-14 & CRAN (R 3.6.2) & NA & /opt/conda/lib/R/library\\\\\n", + "\tR.oo & R.oo & 1.23.0 & 1.23.0 & /opt/conda/lib/R/library/R.oo & /opt/conda/lib/R/library/R.oo & TRUE & FALSE & 2019-11-03 & CRAN (R 3.6.2) & NA & /opt/conda/lib/R/library\\\\\n", + "\tR.utils & R.utils & 2.9.2 & 2.9.2 & /opt/conda/lib/R/library/R.utils & /opt/conda/lib/R/library/R.utils & TRUE & FALSE & 2019-12-08 & CRAN (R 3.6.2) & NA & /opt/conda/lib/R/library\\\\\n", + "\tstatmod & statmod & 1.4.34 & 1.4.34 & /opt/conda/lib/R/library/statmod & /opt/conda/lib/R/library/statmod & TRUE & FALSE & 2020-02-17 & CRAN (R 3.6.2) & NA & /opt/conda/lib/R/library\\\\\n", + "\ttibble & tibble & 2.1.3 & 2.1.3 & /opt/conda/lib/R/library/tibble & /opt/conda/lib/R/library/tibble & TRUE & FALSE & 2019-06-06 & CRAN (R 3.6.1) & NA & /opt/conda/lib/R/library\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A packages_info: 9 × 11\n", + "A packages_info: 11 × 11\n", "\n", "| | package <chr> | ondiskversion <chr> | loadedversion <chr> | path <chr> | loadedpath <chr> | attached <lgl> | is_base <lgl> | date <chr> | source <chr> | md5ok <lgl> | library <fct> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|\n", - "| Biobase | Biobase | 2.46.0 | 2.46.0 | /opt/conda/lib/R/library/Biobase | /opt/conda/lib/R/library/Biobase | TRUE | FALSE | 2019-10-29 | Bioconductor | NA | /opt/conda/lib/R/library |\n", - "| BiocGenerics | BiocGenerics | 0.32.0 | 0.32.0 | /opt/conda/lib/R/library/BiocGenerics | /opt/conda/lib/R/library/BiocGenerics | TRUE | FALSE | 2019-10-29 | Bioconductor | NA | /opt/conda/lib/R/library |\n", - "| edgeR | edgeR | 3.28.1 | 3.28.1 | /opt/conda/lib/R/library/edgeR | /opt/conda/lib/R/library/edgeR | TRUE | FALSE | 2020-02-26 | Bioconductor | NA | /opt/conda/lib/R/library |\n", - "| limma | limma | 3.42.0 | 3.42.0 | /opt/conda/lib/R/library/limma | /opt/conda/lib/R/library/limma | TRUE | FALSE | 2019-10-29 | Bioconductor | NA | /opt/conda/lib/R/library |\n", - "| multtest | multtest | 2.40.0 | 2.40.0 | /opt/conda/lib/R/library/multtest | /opt/conda/lib/R/library/multtest | TRUE | FALSE | 2019-05-02 | Bioconductor | NA | /opt/conda/lib/R/library |\n", - "| R.methodsS3 | R.methodsS3 | 1.8.0 | 1.8.0 | /opt/conda/lib/R/library/R.methodsS3 | /opt/conda/lib/R/library/R.methodsS3 | TRUE | FALSE | 2020-02-14 | CRAN (R 3.6.1) | NA | /opt/conda/lib/R/library |\n", - "| R.oo | R.oo | 1.23.0 | 1.23.0 | /opt/conda/lib/R/library/R.oo | /opt/conda/lib/R/library/R.oo | TRUE | FALSE | 2019-11-03 | CRAN (R 3.6.1) | NA | /opt/conda/lib/R/library |\n", - "| R.utils | R.utils | 2.9.2 | 2.9.2 | /opt/conda/lib/R/library/R.utils | /opt/conda/lib/R/library/R.utils | TRUE | FALSE | 2019-12-08 | CRAN (R 3.6.1) | NA | /opt/conda/lib/R/library |\n", - "| tibble | tibble | 2.1.3 | 2.1.3 | /opt/conda/lib/R/library/tibble | /opt/conda/lib/R/library/tibble | TRUE | FALSE | 2019-06-06 | CRAN (R 3.6.1) | NA | /opt/conda/lib/R/library |\n", + "| Biobase | Biobase | 2.46.0 | 2.46.0 | /opt/conda/lib/R/library/Biobase | /opt/conda/lib/R/library/Biobase | TRUE | FALSE | 2019-10-29 | Bioconductor | NA | /opt/conda/lib/R/library |\n", + "| BiocGenerics | BiocGenerics | 0.32.0 | 0.32.0 | /opt/conda/lib/R/library/BiocGenerics | /opt/conda/lib/R/library/BiocGenerics | TRUE | FALSE | 2019-10-29 | Bioconductor | NA | /opt/conda/lib/R/library |\n", + "| edgeR | edgeR | 3.28.1 | 3.28.1 | /opt/conda/lib/R/library/edgeR | /opt/conda/lib/R/library/edgeR | TRUE | FALSE | 2020-02-26 | Bioconductor | NA | /opt/conda/lib/R/library |\n", + "| limma | limma | 3.42.2 | 3.42.2 | /opt/conda/lib/R/library/limma | /opt/conda/lib/R/library/limma | TRUE | FALSE | 2020-02-03 | Bioconductor | NA | /opt/conda/lib/R/library |\n", + "| multtest | multtest | 2.42.0 | 2.42.0 | /opt/conda/lib/R/library/multtest | /opt/conda/lib/R/library/multtest | TRUE | FALSE | 2019-10-29 | Bioconductor | NA | /opt/conda/lib/R/library |\n", + "| piggyback | piggyback | 0.0.10.99 | 0.0.10.99 | /opt/conda/lib/R/library/piggyback | /opt/conda/lib/R/library/piggyback | TRUE | FALSE | 2020-05-13 | Github (ropensci/piggyback@87f71e8) | NA | /opt/conda/lib/R/library |\n", + "| R.methodsS3 | R.methodsS3 | 1.8.0 | 1.8.0 | /opt/conda/lib/R/library/R.methodsS3 | /opt/conda/lib/R/library/R.methodsS3 | TRUE | FALSE | 2020-02-14 | CRAN (R 3.6.2) | NA | /opt/conda/lib/R/library |\n", + "| R.oo | R.oo | 1.23.0 | 1.23.0 | /opt/conda/lib/R/library/R.oo | /opt/conda/lib/R/library/R.oo | TRUE | FALSE | 2019-11-03 | CRAN (R 3.6.2) | NA | /opt/conda/lib/R/library |\n", + "| R.utils | R.utils | 2.9.2 | 2.9.2 | /opt/conda/lib/R/library/R.utils | /opt/conda/lib/R/library/R.utils | TRUE | FALSE | 2019-12-08 | CRAN (R 3.6.2) | NA | /opt/conda/lib/R/library |\n", + "| statmod | statmod | 1.4.34 | 1.4.34 | /opt/conda/lib/R/library/statmod | /opt/conda/lib/R/library/statmod | TRUE | FALSE | 2020-02-17 | CRAN (R 3.6.2) | NA | /opt/conda/lib/R/library |\n", + "| tibble | tibble | 2.1.3 | 2.1.3 | /opt/conda/lib/R/library/tibble | /opt/conda/lib/R/library/tibble | TRUE | FALSE | 2019-06-06 | CRAN (R 3.6.1) | NA | /opt/conda/lib/R/library |\n", "\n" ], "text/plain": [ @@ -3436,11 +3187,13 @@ "Biobase Biobase 2.46.0 2.46.0 \n", "BiocGenerics BiocGenerics 0.32.0 0.32.0 \n", "edgeR edgeR 3.28.1 3.28.1 \n", - "limma limma 3.42.0 3.42.0 \n", - "multtest multtest 2.40.0 2.40.0 \n", + "limma limma 3.42.2 3.42.2 \n", + "multtest multtest 2.42.0 2.42.0 \n", + "piggyback piggyback 0.0.10.99 0.0.10.99 \n", "R.methodsS3 R.methodsS3 1.8.0 1.8.0 \n", "R.oo R.oo 1.23.0 1.23.0 \n", "R.utils R.utils 2.9.2 2.9.2 \n", + "statmod statmod 1.4.34 1.4.34 \n", "tibble tibble 2.1.3 2.1.3 \n", " path \n", "Biobase /opt/conda/lib/R/library/Biobase \n", @@ -3448,30 +3201,36 @@ "edgeR /opt/conda/lib/R/library/edgeR \n", "limma /opt/conda/lib/R/library/limma \n", "multtest /opt/conda/lib/R/library/multtest \n", + "piggyback /opt/conda/lib/R/library/piggyback \n", "R.methodsS3 /opt/conda/lib/R/library/R.methodsS3 \n", "R.oo /opt/conda/lib/R/library/R.oo \n", "R.utils /opt/conda/lib/R/library/R.utils \n", + "statmod /opt/conda/lib/R/library/statmod \n", "tibble /opt/conda/lib/R/library/tibble \n", " loadedpath attached is_base date \n", "Biobase /opt/conda/lib/R/library/Biobase TRUE FALSE 2019-10-29\n", "BiocGenerics /opt/conda/lib/R/library/BiocGenerics TRUE FALSE 2019-10-29\n", "edgeR /opt/conda/lib/R/library/edgeR TRUE FALSE 2020-02-26\n", - "limma /opt/conda/lib/R/library/limma TRUE FALSE 2019-10-29\n", - "multtest /opt/conda/lib/R/library/multtest TRUE FALSE 2019-05-02\n", + "limma /opt/conda/lib/R/library/limma TRUE FALSE 2020-02-03\n", + "multtest /opt/conda/lib/R/library/multtest TRUE FALSE 2019-10-29\n", + "piggyback /opt/conda/lib/R/library/piggyback TRUE FALSE 2020-05-13\n", "R.methodsS3 /opt/conda/lib/R/library/R.methodsS3 TRUE FALSE 2020-02-14\n", "R.oo /opt/conda/lib/R/library/R.oo TRUE FALSE 2019-11-03\n", "R.utils /opt/conda/lib/R/library/R.utils TRUE FALSE 2019-12-08\n", + "statmod /opt/conda/lib/R/library/statmod TRUE FALSE 2020-02-17\n", "tibble /opt/conda/lib/R/library/tibble TRUE FALSE 2019-06-06\n", - " source md5ok library \n", - "Biobase Bioconductor NA /opt/conda/lib/R/library\n", - "BiocGenerics Bioconductor NA /opt/conda/lib/R/library\n", - "edgeR Bioconductor NA /opt/conda/lib/R/library\n", - "limma Bioconductor NA /opt/conda/lib/R/library\n", - "multtest Bioconductor NA /opt/conda/lib/R/library\n", - "R.methodsS3 CRAN (R 3.6.1) NA /opt/conda/lib/R/library\n", - "R.oo CRAN (R 3.6.1) NA /opt/conda/lib/R/library\n", - "R.utils CRAN (R 3.6.1) NA /opt/conda/lib/R/library\n", - "tibble CRAN (R 3.6.1) NA /opt/conda/lib/R/library" + " source md5ok library \n", + "Biobase Bioconductor NA /opt/conda/lib/R/library\n", + "BiocGenerics Bioconductor NA /opt/conda/lib/R/library\n", + "edgeR Bioconductor NA /opt/conda/lib/R/library\n", + "limma Bioconductor NA /opt/conda/lib/R/library\n", + "multtest Bioconductor NA /opt/conda/lib/R/library\n", + "piggyback Github (ropensci/piggyback@87f71e8) NA /opt/conda/lib/R/library\n", + "R.methodsS3 CRAN (R 3.6.2) NA /opt/conda/lib/R/library\n", + "R.oo CRAN (R 3.6.2) NA /opt/conda/lib/R/library\n", + "R.utils CRAN (R 3.6.2) NA /opt/conda/lib/R/library\n", + "statmod CRAN (R 3.6.2) NA /opt/conda/lib/R/library\n", + "tibble CRAN (R 3.6.1) NA /opt/conda/lib/R/library" ] }, "metadata": {}, @@ -3493,6 +3252,13 @@ "dev_session_info$platform\n", "dev_session_info$packages[dev_session_info$packages$attached==TRUE, ]" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -3507,7 +3273,7 @@ "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", - "version": "3.6.1" + "version": "3.6.2" } }, "nbformat": 4, diff --git a/metadata/BreastMammaryTissueJunctionAnalysis_devtools_session_info.rds b/metadata/BreastMammaryTissueJunctionAnalysis_devtools_session_info.rds index 4937138..2a9c3e2 100644 Binary files a/metadata/BreastMammaryTissueJunctionAnalysis_devtools_session_info.rds and b/metadata/BreastMammaryTissueJunctionAnalysis_devtools_session_info.rds differ diff --git a/metadata/BreastMammaryTissueJunctionAnalysis_sha256sums.txt b/metadata/BreastMammaryTissueJunctionAnalysis_sha256sums.txt index d657492..9f89f53 100644 --- a/metadata/BreastMammaryTissueJunctionAnalysis_sha256sums.txt +++ b/metadata/BreastMammaryTissueJunctionAnalysis_sha256sums.txt @@ -1,198 +1,31 @@ -d03c22e998eae9c96ab6e07340266fdff0d8f4f896fe7bd62ab80bb5aa7857f1 ./BreastMammaryTissue_DGE_ijc_sex.csv -ebe1713963cd88e7bb2ac961e7d7209b38d2b0e8136b28fe4425ac8d04ad0b18 ./rmats_final.mxe.jcec.skiplen.txt.gz -6e47d3c15687f037426a6678e877227da2431f9ad20de41914e1fbb1701b32ad ./BreastMammaryTissue_sex_as_events_universe.txt -490afac0f9797126ea051b167f409bbc011a8bdbe190b991ce0da972c5424cbf ./Cells-EBV-transformedlymphocytes_DGE.csv -38307c2edf8434a57dc1113575887aafd735a529412046ba10f46c716fedcd2f ./Brain-CerebellarHemisphere_DGE.csv -b8347e8cd7fc16b23041a2809aa38e1590813c8858c6a2aa14f8fc4c134fba3e ./.ipynb_checkpoints/BreastMammaryTissue,.DGE_isoforms_robust-checkpoint.csv -f117a76a29a921bea1c77bebafaa6a94e294a00e9da740daf8e12c7c5c4ddc3b ./.ipynb_checkpoints/Breast - Mammary Tissue-anno-gene_set-Term-For-Term-Benjamini-Hochberg-checkpoint.txt -b2e407257b128afc0765497a4a21cf371bf4e3ed28c9532d3d55d32518e329e4 ./.ipynb_checkpoints/BreastMammaryTissue_sjc_sex_gene_set-checkpoint.txt -de1e2811f58cf183dfbabe1c6b1538f831bbc83f24ebefd0899207a63223f2a4 ./.ipynb_checkpoints/BreastMammaryTissue.DGE_isoforms_gene-checkpoint.txt -2c1b558cdd3a2c6bffac32a58f9335f6722728e439cbe916c10cf7b766c006b1 ./.ipynb_checkpoints/Adipose-Subcutaneous_DGE-checkpoint.csv -90b4bc15e6ffc3ae149e50b2d48a6c482c21140ea1c6bfb262b503270898908a ./.ipynb_checkpoints/Thyroid_DGE-checkpoint.csv -522f99614273541347ff284a4eceda69fb7839c185b834e53747fbf349235a66 ./.ipynb_checkpoints/BreastMammaryTissue.DGE_isoforms_gene_robust-checkpoint.txt -e39978188a1bdec9ed704c0a1a0e396b3b1b6c19df0c8c9458f808c51a539fe4 ./.ipynb_checkpoints/Adipose-Visceral(Omentum)_DGE-checkpoint.csv -646f4edb6d7ceb203dc225acadca78a4b456b1a2a583770822046bde720bcbc0 ./.ipynb_checkpoints/BreastMammaryTissue.gene_set-checkpoint.txt -a9060ec5cbb68159f1f7b1329f7e0f35e3ebe699459b5aa3bf5b6f448eea0342 ./.ipynb_checkpoints/anno-gene_set-Term-For-Term-Benjamini-Hochberg-checkpoint.txt -4b0c36cf5b75567bda4282839515dfdf1496d452415143bf05d147f0c950622c ./.ipynb_checkpoints/BreastMammaryTissue.DGE_isoforms_robust-checkpoint.csv -63888f391d15f13590b9bcb9d472af4c438947122e63eedca232b1b25b082ebe ./.ipynb_checkpoints/BreastMammaryTissue_ijc_sex_gene_set-checkpoint.txt -219318a26dc2cd7fde88d89aacf1c1381d5af826150911b57ac17e61be1ddf3c ./.ipynb_checkpoints/Brain-Amygdala_DGE-checkpoint.csv -6142ec3ad2739882d288d998463840e1a29147a76537e82abe641a2c8dde3fd9 ./.ipynb_checkpoints/SraRunTable.noCram.noExome.noWGS.totalRNA-checkpoint.txt -80f709ee41b2e9c08283149c2dcf5979d479edcec3134bacd149556de2a644eb ./.ipynb_checkpoints/BreastMammaryTissue.DGE_isoforms-checkpoint.csv -072e353fd7d3ac611dfe2129d9b9739d0f03cb2255e23de6f67a832b357b5b41 ./.ipynb_checkpoints/Lung_DGE-checkpoint.csv -826bf6c7711ea5ba48469c1612ffb238551441fcc81fb840e971584f0d6758d7 ./.ipynb_checkpoints/Breast - Mammary Tissue-table-gene_set-Term-For-Term-Benjamini-Hochberg-checkpoint.txt -60d5567a03456473d2af7b085d1d13d5d851cf78cc59cfd06598db60050fe932 ./.ipynb_checkpoints/BreastMammaryTissue_sex_gene_set-checkpoint.txt -d198eb10bb520145f4d4a84533f2f6239b9e12f6cca7315d4394529be67cfc8d ./.ipynb_checkpoints/BreastMammaryTissue_sex_as_events_gene_set-checkpoint.txt -3c2b1e7b7d790226bf24fa1b87e59f2516b0439b37d738ce74e43879686650a1 ./Breast-MammaryTissue_DGE.csv -eae3f98905a92c8b723ffe85845fadf7f486bb1c5e17522aeb6e419e6005f1d4 ./Esophagus-Mucosa_DGE.csv -5f90dad03fc9b792ec434aae8dde3d4ebdd5ff6c8a799912f874bf92569c2c5d ./BreastMammaryTissue_DGE_sjc_sex.csv -2d353f064094a4aa5270f7653a85581520553e03b53f0712bf41934c85b21112 ./Heart-AtrialAppendage_DGE.csv -be53124f034b2e33e0868604b2ff8f8864af7427a39127229de888cb9b12420a ./reduced_y.rds -0d608f15ef03b589bf111b15a84b67c96b80531cf217c314d005c6ce8f4ce1cf ./Liver_DGE.csv -1d057b2b0283a0f2541219ee1c3cb83c1e2aa650d007c04c8cd3bc39493e7bd0 ./rmats_final.ri.jc.skiplen.txt.gz -d802915847965a30ca0c8d6486dc4c8c5a64984c11aadeee8eb1d8484ba0b248 ./rmats_final.se.jc.inc.txt.gz -3b7bab338de56cb58a6561881c277e196412125e1e73425b87a78ce65c2e2710 ./Heart-AtrialAppendage_refined_DGE.csv -2aa3a7b6f12ffe41d871011865810d8ba2894a7aaab9b34d9ecccd129c421da5 ./Pancreas_refined_DGE.csv +ccf16742fe3db84abc66404b4c50981e6dee33b0de3f481111a2b44f6f931813 ./BreastMammaryTissue_DGE_ijc_sex.csv +d9b424c4c6a3084bf89e21ee0c0a806a882f0dac448b3c0bf61c8bfe1b002f58 ./BreastMammaryTissue_sex_as_events_universe.txt +741763f61d32c98446317ad30c45cdb3199039b0c696ab83461eb91b0e8611fe ./BreastMammaryTissue_DGE_sjc_sex.csv 28764f9f1e0f14e399d555e448b5490c7221454e7d34d220f79b537376ca017b ./fromGTF.novelEvents.SE.txt -fd258e795ef0128caa7234bbd83131720db2896e7481854b062ec7be085a961e ./Nerve-Tibial_DGE.csv -ce88b3d98923ccd5b421dc3b2f8f617c020e99320392eade573aaaa1fec7b020 ./Brain-Nucleusaccumbens(basalganglia)_refined_DGE.csv -86f3a2b104b1ffe297df668b0b0e1e4d5f61836c3ad8a574b89bf24a090ca9c0 ./rmats_final.ri.jc.sjc.txt.gz -4b9cffb76cf84c41025b5909bfe75270c6d504a404d42cba631053e9f6ecd69b ./Brain-Anteriorcingulatecortex(BA24)_DGE.csv -85acd073d8742ef4a680a10ed509ccf6779f9fbfa86fb3fd2c5629ddfa7094dd ./rmats_final.a5ss.jc.skiplen.txt.gz -2c22da474cfcf3e0f2eed020b22d11b32192d92a483d185ff90738622204babf ./reduced_obj.rds -82d2699a6abe6b0899f1120cf07d48e7f1772f56205cfb4f5418b13772e6647f ./Brain-Nucleusaccumbens(basalganglia)_DGE.csv -d9b2ebd4f9c0875a5dc61139be40a4d687001ed424677ebc54fc061eda998d60 ./Breast-MammaryTissue_refined_DGE.csv -c95b3a1fd3ad7201e0e2cf8c615a895ce3bfffc5bcdef940c8be975bf25225ab ./Brain-Amygdala_refined_DGE.csv -85acd073d8742ef4a680a10ed509ccf6779f9fbfa86fb3fd2c5629ddfa7094dd ./rmats_final.a5ss.jcec.skiplen.txt.gz -9fca10a9d633c4008029c6562576cd20a0a11fd20461e20426e1c8a5689ad3a7 ./Brain-Cortex_refined_DGE.csv -18e2c7a83c98dcf59ddab53e1281923979d49da6ea3acb68114c5a44057c57bc ./gtex.rds -f1cce0ddff847545e68bf9971c9e16e6959df156c3bc83d6ec262bd0e509e526 ./rmats_final.ri.jc.inclen.txt.gz -5580aed723a22a403831aeaa5b30534088ded2f27e52be8966ed396f404844de ./Pancreas_DGE.csv -ca55853290e316d539d60570326e3e76a5bd6530b37862e112cea8bf66fbac13 ./rmats_final.ri.jcec.inclen.txt.gz -345d1cb793f99041b24c2c970e3272657af0d423ae4540ba6e0066857eae24c3 ./Kidney-Cortex_refined_DGE.csv -4589dbb46226f12477b38346ba5a0168c7e71810d9b75e3e7850a8a299d35ffa ./Lung_refined_DGE.csv -7792a86956468da3c64c287274971b6d1a00e97022f7db3d82b7163a38afbedc ./Pituitary_DGE.csv -0e70cbcd1803731445421872517c3f76ac9ceb06a0cf5bad4697ca90ca998d97 ./Colon-Transverse_refined_DGE.csv -86f3a2b104b1ffe297df668b0b0e1e4d5f61836c3ad8a574b89bf24a090ca9c0 ./rmats_final.ri.jcec.sjc.txt.gz -c6997aa953b368369e492d1de429a5f631b0d0f1ed11d83ab59914f9682cced3 ./rmats_final.a3ss.jcec.skiplen.txt.gz -edb12417aa5f92cc92e88006403767d6bf1bbf0b9f5aa5be7d88fae3c7d5a208 ./rmats_final.a5ss.jc.inc.txt.gz -40862f8a65a577cb64fdf85ddba0f6ad34c4443f0fe4a359ce116cd90e095fbc ./BreastMammaryTissue_ijc_sex_universe.txt -c6997aa953b368369e492d1de429a5f631b0d0f1ed11d83ab59914f9682cced3 ./rmats_final.a3ss.jc.skiplen.txt.gz -c31ef0734421539307dd16dced1628a9501dc7106f385eece4041f34fb133749 ./Skin-SunExposed(Lowerleg)_DGE.csv -a1396ecb4b470df39cac8008d92a8fc5505fd908ea0650b427bbcd43b28417e1 ./Adipose-Visceral(Omentum)_DGE.csv -29196d21f2f712a57f683faa4b476c46bd5944f00c5b55c0a5b8de996df59b33 ./Skin-SunExposed(Lowerleg)_refined_DGE.csv -67edbb3b73a8cab355402c91c325053bca7dbcdc6ffdc440a2c31cb580dabb7d ./Heart-LeftVentricle_refined_DGE.csv -25fc2f4f348d661428209dc28a58dfeefceb45f302d6980402a2bc279605903e ./Bladder_refined_DGE.csv -6bb3f6a27984abbfd54b4f9d6c4c47cd512dfb2a1137c8c98efb68cfcf412e7b ./BreastMammaryTissue_sjc_sex_universe.txt -178204848a3769c8461a112d9ac71720234c221e674df30dbe1ac59ec9a796dc ./Brain-Cerebellum_refined_DGE.csv -c152ca5e78057944a7e545429cc0c0a58ad5c7df3b92aa1ab1f47a6be6929368 ./Pituitary_refined_DGE.csv -eb2e011090cd251e46852cfc8100209fdd754ebf570fcd308bd6e233066bdbf6 ./AdrenalGland_DGE.csv -42180a0df350630904a942b0ba0325c61b57bb3862484c90ce166841a8baa6a6 ./rmats_final.a3ss.jc.inclen.txt.gz -efa2de745b97e000d14ca091da2b68d2775013734b0d4bcaa7576a0f7c98accb ./Brain-FrontalCortex(BA9)_DGE.csv -285049d2d3e5dc63d091325d21590f44ffdf039f5dde04101981c5ae6dd4905b ./Brain-Cerebellum_DGE.csv -975546650515aa3b14b2242478540e94a516c3b30d41dd492060936098bda353 ./Artery-Aorta_refined_DGE.csv -60d5567a03456473d2af7b085d1d13d5d851cf78cc59cfd06598db60050fe932 ./BreastMammaryTissue_sex_gene_set.txt -8da05d58584bc2855a73a7d2b64600d9c56b46610b6c2fe16da81a8ba4ba6d25 ./rmats_final.a3ss.jcec.inclen.txt.gz -be0a380b418ee9821ed6c342998720b4ecb6ce4d72db83864dd8e069fae5091f ./Brain-Anteriorcingulatecortex(BA24)_refined_DGE.csv -ef57c39fc6348dbd751f87b1ab062568d0a9e4b79fce3dd3c22f8eaf01958c3b ./rmats_final.a3ss.jc.sjc.txt.gz +e532e4d8ee0f5e5ba221017a1c07c5b1376f7d711aacbba5cc713badd8bd3e47 ./BreastMammaryTissue_ijc_sex_universe.txt +fb7fced98c23d7f0e69f820a8cf1616c3a2a88386074ae5a14690f313ae210d0 ./BreastMammaryTissue_sjc_sex_universe.txt +b408d18141c7de21b42d5fa78e442e482d6c249b90b45800271f5124c11dd3d3 ./BreastMammaryTissue_sex_gene_set.txt 5146c849c9354129590c59da5e6040a074523b4827d0975e3f5fa43737af7e9a ./README.md -181ed243ff15ccbc025a0d49a57e373575a592b695161f0011c17772abb6217b ./Brain-Caudate(basalganglia)_refined_DGE.csv -4c4d597c9ddb0ebd4025945e38fe679fd9e13abb954303d3bf4437d52228b4c6 ./rmats_final.se.jc.skiplen.txt.gz -b58ee83e3b816dca67bb42c2f852e45e1a2cb940e3b9405f655d90016271b9d4 ./Adipose-Visceral(Omentum)_refined_DGE.csv -be843c6100848ffb68e807864657c7df2a6a89b7b07f32c6331c6704ff712807 ./WholeBlood_refined_DGE.csv -6837354792d557a7f6d5a70c734c9c1e7c8537aa5828797b093d16b5daeb2259 ./Kidney-Cortex_DGE.csv -f69298f6024fcf6fc3661d8d2eaee9644fc61df1d984f3256eb8490ac0ee2b0f ./Brain-Putamen(basalganglia)_DGE.csv -5064c349c235f372b4578836a4b3584170b156f8b670facf1ad265c38e508a8f ./Brain-Hypothalamus_refined_DGE.csv -20aeaa0179d4760d511f8086b414f6474aede2e87c5d7e2c691bece71563956e ./Artery-Coronary_refined_DGE.csv -4bc55fe3dca4e4243d4b5c7542692a9ba2266950f063ab57fe45d62a1422dfcb ./Brain-Amygdala_DGE.csv -8ab5027bca2629baa57a7548c2617530db9b333f2bfb45ce673110ae4bef2ef0 ./Thyroid_refined_DGE.csv -d07299991c3cedf846b69859301fe61ab69fe544f2de686ba812b8f918cb2ef4 ./Esophagus-Mucosa_refined_DGE.csv -bf9a6f9a0a7083fe7de99cbbd336d73cd68e814dcbfa2a312093b9e803df4324 ./rmats_final.mxe.jc.skiplen.txt.gz -76025a327f2b7594d897e880c90d97f0fc237f15bace352178e5d953615106c4 ./Brain-Caudate(basalganglia)_DGE.csv -1952fbf96eecb16d9a3af80a3c8ba4b4852506dc67afe82562fd6ce04af0125f ./rmats_final.a3ss.jc.ijc.txt.gz -d95f98d01a147d5357d1d065503041630c05f0969b8e2fd4e9be53df5a121992 ./rmats_final.a5ss.jc.inclen.txt.gz -1d057b2b0283a0f2541219ee1c3cb83c1e2aa650d007c04c8cd3bc39493e7bd0 ./rmats_final.ri.jcec.skiplen.txt.gz -0057f626fb8067de6062387042b922b577158fe891387d02d07e82ae62988830 ./BreastMammaryTissue_DGE_sjc_sex_refined.csv +efe91f83b5b3d4a6497e7265979ec2616f1e330005b3117fbd6e0da0c4b37c79 ./BreastMammaryTissue_DGE_sjc_sex_refined.csv eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6 ./fromGTF.novelEvents.A5SS.txt -ec941ebc9647caa1ff89e9b9c25fa87fab618865076b8b2794d7f35b8e2fea4d ./rmats_final.a5ss.jcec.sjc.txt.gz -1ae7a2755a343d5de7b1b67c53f1af14af6ecbbfaf4f9f37438a855e05787699 ./MinorSalivaryGland_DGE.csv -ff1e9336cf836432b0d90bd0e27a6f39caefdd723c3a5743da5a149dfa7b3d24 ./BreastMammaryTissue_sex_universe.txt -0a412fde634261bb71d7914129af55394c7dfb8b3c44f21fafd6d0c33404df4a ./Adipose-Subcutaneous_refined_DGE.csv -c9612c5a4d71968c1bf289b7b6ae44aba9a3fe63ab32d2cf937b5f55b3733c9c ./BreastMammaryTissue_DGE_sex_events.csv -fa4537694e869167da2913fbfe9c900764380a59d7ac275894204758fee30823 ./rmats_final.a3ss.jcec.ijc.txt.gz -adbaf726a93d9bd85729922109f087506eb7741a2ae68c0a413c9b72659b7ff3 ./Heart-LeftVentricle_DGE.csv +94df4d4e2ab738e06c68e19e7f9eaefba64389a65827726b127713dbd20a7c30 ./BreastMammaryTissue_sex_universe.txt eb61abf2dff12314fc82b79bd492a1baddf6d37bd080ac14cdb0caa39cd09bc6 ./fromGTF.novelEvents.A3SS.txt -a5707f0f3ae5071f8b252f6de64378cccb4873ac4a8088ba6f306c32abfaf6f1 ./rmats_final.mxe.jcec.ijc.txt.gz -2fcfd212f124cd3bd742a830bf8bd322dbcd29837ea6ec235a5d839b25f56d9f ./SmallIntestine-TerminalIleum_refined_DGE.csv -85a1261cad10d3cd65bcf092de690759c58bdec551a2881fe8286dcfb5a93418 ./BreastMammaryTissue_DGE_sex_as_events_refined.csv -5332c603ec0d651beb23192c63c8345aeabf2fe49f0a381216d0ad1150dd7d4c ./rmats_final.ri.jcec.ijc.txt.gz +5ded52e3f6c0c09b7fe09e81a5bc58bf6d9c4afdaed3d19a159b9dfb0c49ea4d ./BreastMammaryTissue_DGE_sex_as_events_refined.csv be32c4925ad8ebf7414623d68e390a1631e08f4459c189f7f346040a542320b5 ./fromGTF.RI.txt c14069b1ed8642720b742864ca6b930b13d35e5a41592dabd9ba0b2dcb953783 ./fromGTF.novelEvents.RI.txt -26c2cef9f156f1a338594a2e5dea72636866d01d585d4009ec68e1fbb5a6bd2d ./Colon-Sigmoid_refined_DGE.csv -aaf41c1cddb3f43596b4253a3ddd3578c305cf525b4dcb18c4539c32853f88a4 ./Artery-Aorta_DGE.csv -13ebeabf14f1ce053668e8ab5741e488304a53edf51fe42b58a5b664182fc829 ./Brain-Spinalcord(cervicalc-1)_DGE.csv -f7bc0b25cd752f6cc5f4c5af26a12c3078b15bcb79418fd132dd5457594f10f1 ./Esophagus-GastroesophagealJunction_refined_DGE.csv -bc7badde66f42a666ee533d54cff47792f410afe47f55c6249aece7685c66f90 ./rmats_final.a3ss.jc.inc.txt.gz -9a3224d2a944763ba5f7069829156854c3ee1d3384a2662296cdf04d88478725 ./rmats_final.a5ss.jc.ijc.txt.gz +dd188bc93b89167bfd8faa80323ca753b1a9557574e7157e6a6f9afe7f48e1b3 ./SraRunTable.noCram.noExome.noWGS.totalRNA.txt.gz 63888f391d15f13590b9bcb9d472af4c438947122e63eedca232b1b25b082ebe ./BreastMammaryTissue_ijc_sex_gene_set.txt -fbebdeab24ea9671ae7d2c14178be4d11ba645443a4d9d27c4f0af4220248c23 ./BreastMammaryTissue_DGE_ijc_sex_refined.csv -e670becbcb8923684df6c6967c3ec4b15485084fc3845568638b49781ab9f1fa ./rmats_final.se.jcec.skiplen.txt.gz -90b4bc15e6ffc3ae149e50b2d48a6c482c21140ea1c6bfb262b503270898908a ./Thyroid_DGE.csv -380dfaa01cf0e34c69be245d2a25022de200107cded8eb39386a1a98662999b3 ./rmats_final.se.jcec.inc.txt.gz -ce5697b7cca29c998d103ffa346607863c267b163189494b545d3e739860878f ./Stomach_refined_DGE.csv -906895e78f984b7c196f5ffa20a743b83f8d1bd8e5ede54aa5a91506ac852b68 ./SplicingIndex_chr.tsv -5240fff4e71842778d9fc262095a0d84ea92716febd2075b43be91ca89619369 ./Skin-NotSunExposed(Suprapubic)_refined_DGE.csv +6426a7ecd614d3aeec414aac830d415c4c23e032df075737429f44546aabeee8 ./BreastMammaryTissue_DGE_ijc_sex_refined.csv 5cfa91f71a21538245f3b5c198fc45f8652c899bbce2fd5ad9bd52a8a08566b6 ./fromGTF.MXE.txt -86442de25e101d017546fcfc4ce3689d15e46e0654dd14e635582b76c0c58ee4 ./rmats_final_intersect_8674.tar.gz -1190c528e7bb89fe979ed1b9d90031a0294470dedbf08d971a27cbc07469d10a ./Skin-NotSunExposed(Suprapubic)_DGE.csv -37f14593955fb8b1c2c5d0880bd6562740e67bd14d3d6884e362bacb7f0b7276 ./Esophagus-Muscularis_refined_DGE.csv -883712df0f3651bed5b10ade161e6d644e9c28014368435c7353e4bf4cd216a4 ./rmats_final.se.jcec.ijc.txt.gz -c1ae268b5e0e66b8e9fae15c2efe294a4b49641467c0148c27c9c2204e447031 ./Liver_refined_DGE.csv -61a4d369386d9f79fb02aef6a36481b70203e563bdc68c9bc7f5d03e17c42a41 ./Cells-Culturedfibroblasts_refined_DGE.csv -68b907f4e4bbb4c02cb6d092f426f955e0121b6de71f93dc6dbad2daceb9964f ./Stomach_DGE.csv -ef57c39fc6348dbd751f87b1ab062568d0a9e4b79fce3dd3c22f8eaf01958c3b ./rmats_final.a3ss.jcec.sjc.txt.gz -a99d7aae0b60ed2ee5fe339f01b455c8c66839db0bc2fe0a620c75547eea24e7 ./SmallIntestine-TerminalIleum_DGE.csv -1de1314915e6e3112033c100d95da7bebcc829dd6e94d35b2dd00bfb713dec98 ./DGENormFactorsy.rds -b92ad0568c9c93f9d8b02139f7a5868b1f579b475956b91ead68e648b038db62 ./Spleen_refined_DGE.csv -10909349f2f3b0de88f31c28e2526f547e32d074c98401e21ba023064821abac ./rmats_final.mxe.jcec.inc.txt.gz -65c09b69229c8fd5fe44fd5b904a1ded4460ad9c3e9ae1b23f481806051fda7c ./Spleen_DGE.csv -a00d062cae1f8f90a4d5ab07e4a6fa294881741fcffda0df0f50d5dd8992bb14 ./rmats_final.se.jc.sjc.txt.gz -538aefe3e3145d82796737cdd97434664a499c3e4cf4f9525e920302135cb909 ./rmats_final.ri.jcec.inc.txt.gz -d198eb10bb520145f4d4a84533f2f6239b9e12f6cca7315d4394529be67cfc8d ./BreastMammaryTissue_sex_as_events_gene_set.txt -d65d6a96d27dfc762246005683833eee2a64e11564dc162e30620f49fb892d4b ./Brain-Cortex_DGE.csv -241d8fa448020f48a65323114992c9c2321f167138fc86424da4c63983318b93 ./Brain-CerebellarHemisphere_refined_DGE.csv -31be3cd71b5a057c44e521e7ae28ba4c36f750d068bc7fba528eb4e8bacf58a4 ./Brain-Spinalcord(cervicalc-1)_refined_DGE.csv -6142ec3ad2739882d288d998463840e1a29147a76537e82abe641a2c8dde3fd9 ./SraRunTable.noCram.noExome.noWGS.totalRNA.txt -70a0b382f35dbe3f0dbb4b5f1a0871645cfbf13157c42ee3231babfc48135a7e ./rmats_final.se.jcec.inclen.txt.gz -74a5dafef35db9674d2b7fbea3fa75cd6548c0d04718ca693bba808967281a36 ./rmats_final.a5ss.jcec.inc.txt.gz -d4f7d9a41d13f27398fc35e202a69729f38d2d42ce6595e77b47681a157ba6d6 ./Kidney-Medulla_DGE.csv -c20e29c710e99666fcd1bb6264e00b0072f615e3073d1b434a6407d9d45da78e ./rmats_final.mxe.jcec.inclen.txt.gz -6c7a8b7c8815d5903710a0a6a276f7f8cdb34d8ea47a0383f8aa62936aa7e0fa ./fromGTF.tar.gz -b3d01e96dc763f7b5ec46bee4914a08e0606a6d313786b72b03795187db4f910 ./rmats_final.a5ss.jcec.ijc.txt.gz -4ed4a6396510074de139fd68699c82674c57a59251b3fb6ce797058d21f6dbc8 ./rmats_final.se.jc.ijc.txt.gz -92f6251a7f2de8b91288af9fdfb76b4430aa206b9c4318cb3d726bb23d3d9dc2 ./Significant_events.tsv -d4c3f78720a82018b229802ffc7f9400cb5dc1500acbec0a025d3d6e6dda8660 ./AdrenalGland_refined_DGE.csv -7c36d6879dbf08b0154a773aa087f1f394831c1e4be6d5f31afbf70e36e04035 ./Brain-Substantianigra_DGE.csv -22d88c624259a1571e86e45df7baf3ea3a6f8f41dd31ca5529f91df3056d96a8 ./Brain-Putamen(basalganglia)_refined_DGE.csv -4a8d96881f6e424ae6fc55a4c34a3fe97e75b4fc9fb4813488b3322ecb130a38 ./Cells-EBV-transformedlymphocytes_refined_DGE.csv -295d5139012ec1ed94afea97ce11d6a06a3ab030b33d61fc705dd84a19e15281 ./rmats_final.ri.jc.inc.txt.gz -ce7cfd476dd881cf8997118dad34565685a9db56c24aa3896a840b054d5383e5 ./Kidney-Medulla_refined_DGE.csv -7f397bddbfb35e7eadf5fbe26ba069e829a21fa9765a1ed5b1d9f118e7530d6e ./WholeBlood_DGE.csv -cfa4e92ff1923f391b73a76b59805985a7da1273e5ed0970eb0d243673ba2cc7 ./Artery-Tibial_DGE.csv -4da0eec01a3df18f56f40f48ad4a444eff577f5b895053ebe3caeda6a52becc7 ./Esophagus-GastroesophagealJunction_DGE.csv -62481ddd3158a0009ea2b813c9be694d5cd576acee5da9f99f1f9577b6193b41 ./Muscle-Skeletal_DGE.csv -4cff41064f29fe16e0b549eec5ffb59e4eae38efe548d6d4692042e9ad2204a4 ./rmats_final.a5ss.jcec.inclen.txt.gz -2e2f75be0a1057dd231b2a7ca7f47f07565fba8522936e720b78f6aecbdb3fd3 ./Muscle-Skeletal_refined_DGE.csv -d7639443651c3b508772ee89728f8ab41d62414672c41e68a78947be14e07cd6 ./rmats_final.se.jcec.sjc.txt.gz +d7639443651c3b508772ee89728f8ab41d62414672c41e68a78947be14e07cd6 ./rmats_final.se.jc.sjc.txt.gz +68ea5ae95f3ca659e86b685304361957d183bf3273f8e74b276d5295d275686f ./BreastMammaryTissue_sex_as_events_gene_set.txt +bfd9fa65689a7d157b09b5efd472f235b627f85a73b9f0493c881882acce773d ./fromGTF.tar.gz +849658f50abd888f8708a1ffd65ff523a14d1d5eab194583bed1caebd5cac92a ./rmats_final.se.jc.ijc.txt.gz 22cb3faa337ae24b7b423a985fd2056047400eccb83b716eff2e33cce9159e53 ./fromGTF.SE.txt -fdcaf28412c4a9be6de6c7e47c1d28e4d986a44c3d86ee96cae107872954f6ef ./rmats_final.a3ss.jcec.inc.txt.gz -7239a05424eed9822b2fe2ba9ce1a3894c1aab5f895f9e39a540b36cf0598fae ./rmats_final.ri.jc.ijc.txt.gz -88520b1bc33869bd8efaa3984c95cf855e04df122648bc109d00d5821debbd6e ./Colon-Sigmoid_DGE.csv b2e407257b128afc0765497a4a21cf371bf4e3ed28c9532d3d55d32518e329e4 ./BreastMammaryTissue_sjc_sex_gene_set.txt -4c7d1f1a9dbcfd148025fcd727c22e9ce12f6bbf3d94dcf898e0daebd54f7dba ./Esophagus-Muscularis_DGE.csv c277ab764b652e8d65e07277df72933fee465806a5e6c1b018509f868ccfddad ./fromGTF.A5SS.txt -51975d86053bfa441c941a6a8a5208d08640cd24f708bced1a1d5aa43d3117ed ./rmats_final.mxe.jcec.sjc.txt.gz -d601f816e710aa450d670c94bb03d7b31a0160d6ef17cbef8e2d55f7d8222264 ./MinorSalivaryGland_refined_DGE.csv -aee4faefee801e4bca5b2250fc0914c1f024404f5df525b617f920f2f80f9729 ./Lung_DGE.csv -3e1a88ddc110bf83c566907703b2022d36054bfcc72b131b7fd9efea142ffa11 ./Cells-Culturedfibroblasts_DGE.csv -ec941ebc9647caa1ff89e9b9c25fa87fab618865076b8b2794d7f35b8e2fea4d ./rmats_final.a5ss.jc.sjc.txt.gz -a2424d8ba1e4752524d36802786d401d70dd4b04501b09f961a69295e08eac47 ./rmats_final.mxe.jc.inclen.txt.gz -11b73d5a25dc6667529da47bf5bbfd6aa9f1dedfa628702a3a536d243685904f ./rmats_final.mxe.jc.ijc.txt.gz 6e3f1d335ce05c887363d092b0100b9f3603a964c8fe6e8c20f4c547b4f52280 ./fromGTF.novelEvents.MXE.txt -9e4b534d05e0d42867519e45951b5b5116aabd2096e37af916411a3d314cef48 ./Brain-FrontalCortex(BA9)_refined_DGE.csv -a8b78248a4132954b8223b6b55341a4ca64feb726e4614094cab3bd7df8154f3 ./Nerve-Tibial_refined_DGE.csv 51c8250a1796196e3879d88186c5450e5b5efc1e9f3fa578f388e9ff380e2deb ./fromGTF.A3SS.txt -2c1b558cdd3a2c6bffac32a58f9335f6722728e439cbe916c10cf7b766c006b1 ./Adipose-Subcutaneous_DGE.csv -454d0369cbf3441c6474749c3cc26caf39dac7238d9d0981898cc0ebee9747d5 ./Colon-Transverse_DGE.csv -5cf99b4abc23ce9f5d68fbad06f2c1778da9928df04247ee6a7524b7fa396ce6 ./rmats_final.se.jc.inclen.txt.gz -4ad589d8fcf6a88226e61dec1f541f244e60fcb8cbcc2a4bd7d07cdc0ade5274 ./rmats_final.mxe.jc.inc.txt.gz -60256efaa9183236ab42a725739f90318b26da908fe66d0c5cc155a22c50b982 ./BreastMammaryTissue_DGE_sex_refined.csv -0f268a87c34227df6caf6d0d2a58aa10e2a7cfffb2e60f43febcbc3f062a2b9d ./BreastMammaryTissue_DGE_sex_as_events.csv -9dea990bfe892ca4113642e823643d382ddbcdd276ed88e9139549ed8f8f930b ./Brain-Hippocampus_refined_DGE.csv -171965d1f008769a85768fe6a03326756f4cccfddf093841b4cfa0dd9f9c674e ./rmats_final.mxe.jc.sjc.txt.gz -d9ffef4ed51d44e60867a1c1c12569d23901435e20f83c681fec3bf2862bbf6f ./Artery-Coronary_DGE.csv -123b86d18f52d90bb7a61f19486ed872046d8fdadd28f84389463f45710930d1 ./Brain-Substantianigra_refined_DGE.csv -e87b47b7e0701732414e80f180f16a40a1474dc41d47fb7291fc6a82bb3c2cb9 ./Artery-Tibial_refined_DGE.csv -1f25799df2650de10c54b5beac57cbc09c3289ded6d8600e98bcd3af9896d7cf ./Brain-Hypothalamus_DGE.csv -0c1f8cc1de30d945454549cd680bb38e5a2663ab4167cb5c20490e72fefef81f ./Brain-Hippocampus_DGE.csv -afac698dd2ede36d3f7ba1a98f94893fb528e15664fe0c7b0e535fab7629ebbd ./Bladder_DGE.csv -c9612c5a4d71968c1bf289b7b6ae44aba9a3fe63ab32d2cf937b5f55b3733c9c ./BreastMammaryTissue_DGE_sex.csv +f31a68097c9133a5fd11d32c4d1194b59442366e50e627189ac1cbcdb6e7f13f ./BreastMammaryTissue_DGE_sex_refined.csv +98001376224e926573071d352f6b22fc6a2dbcca33a16ba88573b49375b90699 ./BreastMammaryTissue_DGE_sex_as_events.csv +969def456d3d089241b56f95bc23dfa4205334f25989485a4febb4dcccccf891 ./BreastMammaryTissue_DGE_sex.csv diff --git a/metadata/BreastMammaryTissueJunctionAnalysis_utils_info.rds b/metadata/BreastMammaryTissueJunctionAnalysis_utils_info.rds index c511a7a..a59c76b 100644 Binary files a/metadata/BreastMammaryTissueJunctionAnalysis_utils_info.rds and b/metadata/BreastMammaryTissueJunctionAnalysis_utils_info.rds differ