Ricardo Rivero H. 2024-02-03
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readr)
library(forcats)
library(MoMAColors)kallisto_full = read_tsv("TCGA.Kallisto.fullIDs.cibersort.relative.tsv") %>%
janitor::clean_names() #CIBERSORT immune fractions## Rows: 11373 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): SampleID, CancerType
## dbl (25): B.cells.naive, B.cells.memory, Plasma.cells, T.cells.CD8, T.cells....
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
leuk = read_tsv("TCGA_all_leuk_estimate.masked.20170107.tsv") %>% #Leukocyte Fraction
janitor::clean_names() #CIBERSORT immune fractions## Rows: 10816 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): ACC, TCGA-OR-A5J1-01A-11D-A29J-05
## dbl (1): 0.0463739104963611
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
colnames(leuk) <- c('cancer_type','sample_id','leukocyte_frac')#View(kallisto_full)#demonstrate relative cell abundace
kallisto_full$total_fraction = rowSums(kallisto_full[3:24])
head(kallisto_full$total_fraction)## [1] 1 1 1 1 1 1
# [1] 1 1 1 1 1 1
#Check the range of values
range(kallisto_full$total_fraction) #[1] 1 1## [1] 1 1
#Check TCGA barcode and spot the keys to bind datasets
#Kallisto dataset has barcode separated by dots "." while leukocyte dataset has barcode separated by dashes "-", we need to reconcile the separators before binding.
#If we try to bind as is, it will not match
#Example:
head(leuk$sample_id %in% kallisto_full$sample_id) #[1] FALSE FALSE FALSE FALSE FALSE FALSE## [1] FALSE FALSE FALSE FALSE FALSE FALSE
kallisto_ids <- kallisto_full %>%
mutate(sample_id = gsub("\\.", "-", sample_id),
sample_id = str_extract(sample_id, '^(TCGA-[A-Z0-9]{2}-[A-Z0-9]{4}-[0-9]{2}[A-Z])')) #Be careful to add the escape character, else it will change all the strings to "----"#The CIBERSORT is using the analyte barcode, which we differs from the Leukocyte dataset which is using "shipped portion"
leuk_ids <- leuk %>%
mutate(sample_id = str_extract(sample_id, "^(TCGA-[A-Z0-9]{2}-[A-Z0-9]{4}-[0-9]{2}[A-Z])"))#Bind the two datasets
full_df <- merge(leuk_ids, kallisto_ids, by = "sample_id") %>%
mutate(cancer_type = cancer_type.x) %>%
select(-cancer_type.y, -cancer_type.x) %>%
mutate(cancer_type = as.factor(cancer_type))#Get the absolute infiltration value per cell type
absolute_infiltration <- full_df %>%
mutate(across(3:24, ~ .x * leukocyte_frac))
#Calculate the T-cell infiltration level by sumation of the values for all the T-cell subpopulation (columns 6 through 12)
absolute_infiltration$total_tcell <- rowSums(absolute_infiltration[6:12])#Visualize in box-plot and reorder by median per cancer-type
ggplot(absolute_infiltration, aes(x= fct_reorder(cancer_type, total_tcell, .fun= median, .desc = TRUE), y=total_tcell)) +
geom_boxplot(outlier.shape = NA, aes(fill = cancer_type)) +
geom_jitter(width = 0.2,size=0.2, alpha=0.4) +
labs(x= "Cancer Type", y = "Total T-cell infiltration") +
theme_minimal() +
scale_fill_manual(values = moma.colors("Ohchi", 30, type = "continuous"),
guide =guide_legend(keywidth=0.7, keyheight=0.7, ncol=1, order=3, title.position = "top",title.hjust = 0.5),
name = "Cancer type") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#Get the median level of immune infiltration of B_cells, T_cells, NK_cells, Monocytes, Macrophages, DCs, Mast and Eosinophils per cancer type and plot them in a heatmap
#B cells
absolute_infiltration$total_bcell <- rowSums(absolute_infiltration[3:5]) #B cells are Naive, Memory and Plasma Cells
#Natural Killer Cells
absolute_infiltration$total_nk <- rowSums(absolute_infiltration[13:14]) #NK can be either resting or activated
#Macrophages
absolute_infiltration$total_macro <- rowSums(absolute_infiltration[16:18]) #Macrophages are M0, M1 and M2
#Dendritic cells
absolute_infiltration$total_dc <- rowSums(absolute_infiltration[19:20]) #DC are resting or activated
#Mast cells
absolute_infiltration$total_mast <- rowSums(absolute_infiltration[21:23]) #Mast are resting or activated#Calculate median values
median_value_cancer <- absolute_infiltration %>%
group_by(cancer_type) %>%
summarize(median_bcell = median(total_bcell),
medial_tcell = median(total_tcell),
median_nk = median(total_nk),
median_mono = median(monocytes),
median_macro = median(total_macro),
median_dc = median(total_dc),
median_mast = median(total_mast),
median_eo = median(eosinophils),
median_neutro = median(neutrophils))
#Pivot to long data for heatmap visualization
long_data <- pivot_longer(median_value_cancer, cols = -cancer_type, names_to = "cell_type", values_to = "median_value")#Heatmap visualization
ggplot(long_data, aes(x = cancer_type, y = forcats::fct_rev(cell_type), size = median_value, fill = median_value)) +
geom_point(shape = 21, stroke = 0) +
scale_size(range = c(1, 15)) +
scale_fill_moma_c("Ohchi") +
scale_y_discrete(labels=c("medial_tcell"="T Cells","median_bcell"="B Cells", "median_nk"="NK",
'median_mono'='Monocytes','median_macro'='Macrophages','median_dc'='Dendritic Cells',
'median_mast'='Mast Cells', 'median_eo'='Eosinophils','median_neutro'='Neutrophils'))+
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
guides(size = FALSE) +
labs(size = "Median Value", fill = "Median Value", x = "Cancer Type", y = "Median Infiltration Level")## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#Step 2 Get the mutation data
#We will use TCGA biolinks for getting mutation data
library(TCGAbiolinks)
query <- GDCquery(
project = "TCGA-LUAD",
data.category = "Simple Nucleotide Variation",
access = "open",
data.type = "Masked Somatic Mutation",
workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)## --------------------------------------
## o GDCquery: Searching in GDC database
## --------------------------------------
## Genome of reference: hg38
## --------------------------------------------
## oo Accessing GDC. This might take a while...
## --------------------------------------------
## ooo Project: TCGA-LUAD
## --------------------
## oo Filtering results
## --------------------
## ooo By access
## ooo By data.type
## ooo By workflow.type
## ----------------
## oo Checking data
## ----------------
## ooo Checking if there are duplicated cases
## ooo Checking if there are results for the query
## -------------------
## o Preparing output
## -------------------
GDCdownload(query)## Downloading data for project TCGA-LUAD
## GDCdownload will download 618 files. A total of 61.056443 MB
## Downloading as: Sat_Feb__3_15_30_22_2024.tar.gz
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maf <- GDCprepare(query)
maf <- getMC3MAF()## ----------------------------------------------------------------------------------------------------
## o Starting to download Public MAF from GDC
## o More information at: https://gdc.cancer.gov/about-data/publications/mc3-2017
## o Please, cite: Cell Systems. Volume 6 Issue 3: p271-281.e7, 28 March 2018 10.1016/j.cels.2018.03.002
## o Uncompressing file
## o Reading MAF
## indexing mc3.v0.2.8.PUBLIC.maf [----------------------------] 6.28TB/s, eta: 0sindexing mc3.v0.2.8.PUBLIC.maf [===-------------------------] 1.66GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [===-------------------------] 1.55GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [===-------------------------] 1.56GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [====------------------------] 1.51GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [====------------------------] 1.66GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [====------------------------] 1.58GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [=====-----------------------] 1.69GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [=====-----------------------] 1.60GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [======----------------------] 1.52GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [======----------------------] 1.54GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf [======----------------------] 1.52GB/s, eta: 2sindexing mc3.v0.2.8.PUBLIC.maf 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## o Adding project_id information
## ----------------------------------------------------------------------------------------------------
#Mutations of interest: EGFR wildtype, Exon20 insertion mutation, Exon19 deletion mutation, pL855R mutation and other misssense mutations.
#Get the patients in the CIBERSORT dataset to include only LUAD
luad_ciber <- full_df %>%
filter(cancer_type == "LUAD")
#Filter LUAD mutation data with patient IDs from the cibersort dataset
maf_filtered <- maf %>%
mutate(sample_id = str_extract(Tumor_Sample_Barcode, "^(TCGA-[A-Z0-9]{2}-[A-Z0-9]{4}-[0-9]{2}[A-Z])")) %>%
filter(sample_id %in% luad_ciber$sample_id,
SYMBOL == "EGFR") %>%
mutate(mutation = paste(Variant_Classification, sub("^(\\d+)/\\d+$", "Exon\\1", Exon_Number), gsub("p.", "",HGVSp_Short), sep = "_"),
type = case_when(Exon_Number=="19/28" | Variant_Type == "DEL" ~ "Exon_19_Del",
Exon_Number== "20/28" | Variant_Type == "INS" ~ "Exon_20_Ins",
HGVSp_Short == "p.L858R" ~ "L858R",
HGVSp_Short != "p.L858R" | Variant_Classification == "Missense_Mutation" ~ "Other_Missense")) %>%
drop_na(type)
luad_ciber_bind <- merge(luad_ciber, maf_filtered, by.x= "sample_id", by.y = "sample_id", all.x = T) %>%
mutate(mutation = ifelse(is.na(mutation),"Wildtype", mutation))
#Filter the columns as we already created a mutation column and we no longer need 145 variables
incl_colums <- colnames(luad_ciber)
luad_ciber_bind <- luad_ciber_bind %>%
select(all_of(incl_colums), mutation, type) %>%
mutate(type = ifelse(is.na(type),"Wildtype", type))
#Get number of mutations to establish the most common ones other than mutations of interest
#View(table(luad_ciber_bind$type))#Get the median level of immune infiltration of B_cells, T_cells, NK_cells, Monocytes, Macrophages, DCs, Mast and Eosinophils per cancer type and plot them in a radar plot
#T Cells
luad_ciber_bind$total_tcell <- rowSums(luad_ciber_bind[6:12])
#B cells
luad_ciber_bind$total_bcell <- rowSums(luad_ciber_bind[3:5]) #B cells are Naive, Memory and Plasma Cells
#Natural Killer Cells
luad_ciber_bind$total_nk <- rowSums(luad_ciber_bind[13:14]) #NK can be either resting or activated
#Macrophages
luad_ciber_bind$total_macro <- rowSums(luad_ciber_bind[16:18]) #Macrophages are M0, M1 and M2
#Dendritic cells
luad_ciber_bind$total_dc <- rowSums(luad_ciber_bind[19:20]) #DC are resting or activated
#Mast cells
luad_ciber_bind$total_mast <- rowSums(luad_ciber_bind[21:23]) #Mast are resting or activated
median_value_mutation <- luad_ciber_bind %>%
group_by(type) %>%
summarize(median_bcell = median(total_bcell),
medial_tcell = median(total_tcell),
median_nk = median(total_nk),
median_mono = median(monocytes),
median_macro = median(total_macro),
median_dc = median(total_dc),
median_mast = median(total_mast),
median_eo = median(eosinophils),
median_neutro = median(neutrophils))#Spider/Radar plot
library(fmsb)
#Get min and max
types <- median_value_mutation$type
median_value_mutation <- median_value_mutation[,-1]
rownames(median_value_mutation) <- types## Warning: Setting row names on a tibble is deprecated.
max_min <- data.frame(
median_bcell = c(1, 0), medial_tcell = c(1, 0), median_nk = c(1, 0),
median_mono = c(1, 0), median_macro = c(1, 0), median_dc = c(1, 0),
median_mast = c(1, 0), median_eo = c(1, 0), median_neutro = c(1, 0)
)
rownames(max_min) <- c("Max", "Min")
df <- rbind(max_min, median_value_mutation)
wildtype <- median_value_mutation[c()]
#Function radar plot
# Define colors and titles
colors <- moma.colors("Ohchi", 5, type = "discrete")
titles <- c("Exon 19 Deletion", "Exon 20 Insertion", "L858R Mutation", 'Other Missense Mutations', 'Wildtype')
vlabels <- c('B Cells',' T Cells', 'NK Cells', 'Monocytes', 'Macrophages', 'Dendritic Cells', 'Mast Cells', 'Eosinophils', 'Neutrophils')
# Create the radar chart
radarchart(df[-c(1,2),] , axistype=0 ,maxmin = F,
#custom polygon
pcol=colors , pfcol=NULL , plwd=2 , plty=1,
#custom the grid
cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8,
#custom labels
vlcex=0.8, vlabels = vlabels, title = "Median Cell Infiltration per Mutation type in Lung Adenocarcinoma"
)
legend(x=1.4, y=1, legend = titles, bty = "n", pch=20 , col=colors , text.col = "black", cex=1.2, pt.cex=3)