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duotang-sandbox.Rmd
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---
title: |
{width=1lm}
subtitle: "Duotang - Sandbox, a playground for genomic epidemiology analyses and mathematical modelling notebook"
author: "Pillar 6"
output:
html_document:
keep_md: true
code_folding: hide
theme: cerulean
toc: true
toc_float: true
params:
datestamp:
label: "Datestamp"
input: date
format: yyyy-mm-dd
value: "2023-01-30"
datadir:
label: "DataDir"
input: text
value: "data_needed/"
---
```{r setup, include=FALSE, warning=FALSE}
#list.of.packages <- c("tidyr", "knitr", "lubridate", "parallel", "ggplot2", "r2d3", "jsonlite", "tidyverse", )
#new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
#if(length(new.packages)) install.packages(new.packages)
library(tidyr)
library(knitr) # Needed to set root directory
library(lubridate) # dates are awful
library(parallel) # speed up selection plotting (#133)
library(ggplot2) # Work-horse plotting package
library(ggfree)
library(r2d3) # used for passing data to JavaScript D3 framework
library(jsonlite)
library(tidyverse)
library(reshape2) #used to plot case count selection plots
library(DT)
library(plotly)
theme_set(theme_classic())
# You would need to change this folder to be wherever you wanted the html file to be written.
opts_knit$set(root.dir = getwd())
```
```{r variables, include=FALSE, warning=FALSE}
############################
##### GLOBAL VARIABLES #####
############################
source("scripts/utils.R")
#Date of the VirusSeq release, load from params passed in during CLI command
VirusSeq_release=format(as.Date(params$datestamp),"%B %d, %Y")
Variants_Canada_over_time_Start_Date=as.Date('2021-01-01')
pangoversion="4.2 (Viral AI)"
#read in the color data for variants.
VOCVOI <- read.csv("resources/vocvoi.csv")
#define canadian provinces that are available.
all.regions = data.frame(name=c("Canada","British Columbia", "Alberta",
"Saskatchawan", "Manitoba", "Ontario",
"Quebec", "Nova Scotia", "New Brunswick",
"Newfoundland and Labrador", "East provinces (NL+NS+NB)"),
shortname=c("Canada","BC", "AB",
"SK", "MA", "ON",
"QC", "NS", "NB","NL","East"))
pal <- VOCVOI$color
names(pal) <- VOCVOI$name
pal["other"] <- 'grey' # named character vector
## 1. LOAD processed metadata of Canadian sequences (with latest pangolin, division, and full seq IDs)
#Download metadata from VirusSeq, put the date here:
# this can be made more compact for faster loading
meta <- read.csv(gzfile(paste0(params$datadir, "/GSDMetadataCleaned.tsv.gz")), sep="\t")
meta$province <- meta$geo_loc_name_state_province_territory
# Select only the column we want to use later
columnlist=c("fasta_header_name", "province", "sample_collection_date",
"lineage", "raw_lineage")
meta <- meta[ , columnlist]
### metadata cleaning
unknown.str <- c("Undeclared", "Not Provided", "Restricted Access", "Missing",
"Not Applicable","","NA","unknown")
meta <- as.data.frame(apply(meta, 2, function(x) {
x[is.element(x, unknown.str)] <- "Unknown"
x
}))
meta$sample_collection_date <- as.Date(meta$sample_collection_date)
meta$week <- cut(meta$sample_collection_date, 'week')
meta$month <- gsub("-..$","",as.character(cut(meta$sample_collection_date, 'month')))
source("./scripts/scanlineages.R")
meta$pango_group <- create.pango.group(VOCVOI, meta)
meta$pango_group <- as.factor(meta$pango_group)
metaV <- read.csv(gzfile(paste0(params$datadir, "/virusseq.metadata.csv.gz")), sep="\t")
metaV$province <- metaV$geo_loc_name_state_province_territory
metaV <- metaV[ , columnlist]
metaV <- as.data.frame(apply(metaV, 2, function(x) {x[is.element(x, unknown.str)] <- "Unknown";return(x)}))
metaV$sample_collection_date <- as.Date(metaV$sample_collection_date)
metaV$week <- cut(metaV$sample_collection_date, 'week')
metaV$month <- gsub("-..$","",as.character(cut(metaV$sample_collection_date, 'month')))
metaV$pango_group <- create.pango.group(VOCVOI, metaV)
metaV$pango_group <- as.factor(metaV$pango_group)
## 2. LOAD epidemiological data (PHAC)
epidataCANall <- read.csv(paste0(params$datadir, "/CanadianEpiData.csv"))
epidataCANall$date <- as.Date(epidataCANall$date)
epidataCANall$prname <- gsub('_', ' ', epidataCANall$prname)
epidate <- tail(epidataCANall,1)$date #download date
epidataCANall$previousvalue <- 0
#small loop to get the numtoday column from previous versions of this file from the cumulative cases
for(row in 1:nrow(epidataCANall)) {
p <- epidataCANall[row, "prname"]
subdf <- epidataCANall[which(
(epidataCANall$date > epidataCANall[row, "date"] & epidataCANall$prname==p)
), ]
if(nrow(subdf) != 0) {
nextrow <- which( (epidataCANall$date == min(subdf$date) & epidataCANall$prname==p))
epidataCANall[nextrow, "previousvalue"] <- epidataCANall[row, "totalcases"]
}
}
epidataCANall$numtoday <- epidataCANall$totalcases - epidataCANall$previousvalue
```
## SARS-CoV-2 in Canada
## Current Situation
**[NOTE: Barplots for lineage frequencies are now interactive, and new tables were added. All tables are now searchable, and the data downloadable.]**
XBB.1.5, combined with its subvariants and other variants with the mutation S:F486P, are still collectively growing in all regions of Canada. This collective set includes XBB.1.9.1, and XBF subvariants. Some BQ.1 subvariants are increasing in some regions of Canada along with CH.1.1. Differences between some provinces continue - see the evolving plots below of “Fastest growing lineages in Canada”, and “case count trends by variant”, per province.
**Variants of current interest**, due to their current/potential growth advantage, mutations of potential functional significance, or spread in other countries (note that some of these are not yet detected in Canada):
* BM.1.1.1
* BM.2 (high predicted affinity for the human ACE2 receptor)
* BN.1* subvariants due to high predicted immune evasion
* BQ.1.8 with S:F486P and S:L452R (seen recently in Ontario)
* BQ.1* select new descendants, particularly BQ.1.1.5 with S:Y453F
* BW.1, BW.1.1, BW.1.1.2 (descendants of BA.5.6, with S:F486A)
* CH.1.1 (descendant of BM which is BA.2.75.3 with S:L452R) and sublineages
* DN.1/DN.1.1 (BQ.1.1.5 descendants with S:K147N) - that also has S:Y453F (and so is actually DN.1.1.1 but this isn’t in databases yet)
* DN.1.1.1 (highest predicted affinity for the human ACE2 receptor)
* DS.1 (highest predicted immune evasion)
* EF.1 (BQ.1.1.13 subvariant)
* EG.1 (a XBB.1.9.1 subvariant)
* EK variants (subvariants ofXBB.1.5.13)
* XBB.1, XBB.1.5, XBB.1.5.13, XBB.1.9.1, XBB.1.10, XBB.1.12, XBB.6.1 and subvariants with S:F486P (or other mutations of note like S:Y453F)
* * * XBF subvariants (BA.5.2/CJ.1 recombinant; have S:F486P and immune evasion like XBB.1.5)
* XBK and XBL (have S:F486P and immune evasion like XBB.1.5)
* …and other variants with mutations associated with immune evasion or higher binding affinity to the human ACE2 receptor (examples: S:K147N, S:R346T, S:R403K, S:L452R, S:Y453F, S:F486P, S:490P, S:Q613H), particularly those variants with sets of immune evasive mutations of interest.
# Methodology {.tabset}
Genome data and metadata are sourced from the [Canadian VirusSeq Data Portal](https://virusseq-dataportal.ca/).
Pango lineage assignments are generated using the [pangoLEARN](https://github.com/cov-lineages/pangoLEARN) algorithm.
Source code for generating this RMarkdown notebook can be found in [https://github.com/CoVaRR-NET/duotang].
## Trees
### Phylogenetic trees
Canadian genomes were obtained from the VirusSeq data on the `r VirusSeq_release` and down-sampled to two genomes per lineage, province and month before October 2021, and five genomes per lineage, province and month after October 2021 (about 10,000 genomes in total).
We used a Python wrapper of [minimap2](https://github.com/lh3/minimap2) (version 2.17) to generate multiple sequence alignments for these genome samples.
A maximum likelihood (ML) tree was reconstructed from each alignment using the COVID-19 release of [IQ-TREE](http://www.iqtree.org/) (version 2.2.0).
Outliers were identified in by root-to-tip regression using the R package [ape](https://cran.r-project.org/web/packages/ape/) and removed from the dataset.
[TreeTime](https://github.com/neherlab/treetime) was used to reconstruct a time-scaled tree under a strict molecular clock model.
The resulting trees were plotted with [ggtree](https://github.com/YuLab-SMU/ggtree).
## Mutational composition
### Mutation composition graph
We extracted mutation frequencies from unaligned genomes using a custom Python wrapper of [minimap2](https://github.com/lh3/minimap2) (version 2.17).
These data were supplemented with genomic data and metadata from the [NCBI GenNank](https://www.ncbi.nlm.nih.gov/genbank/) database, curated by the [Nextstrain](https://nextstrain.org/ncov/open/global) development team.
We used these outputs to generate mutational graphs reporting mutations seen in at least 75% of sequences in the respective variants of concern in Canada.
Bars are colored by substitution type, and the corresponding amino acid changes are shown.
Genomic position annotations were generated in Python using [SnpEFF](http://pcingola.github.io/SnpEff/).
## Selection
### Selection Coefficents
To estimate selection, we used standard likelihood techniques.
In brief, sublineages of interest were prespecified (e.g., BA.1, BA.1.1, BA.2) and counts by day tracked over time.
If selection were constant over time, the frequency of sub-type $i$ at time $t$ would be expected to rise according to $$p_i(t) = \frac{p_i(0) \exp(s_i t)}{\sum_j p_j(0) \exp(s_j t)},$$ where $s_i$ is the selection coefficient favouring sub-type $i$.
A selection coefficient of $s_i=0.1$ implies that sub-type $i$ is expected to rise from 10% to 90% frequency in 44 days (in $4.4./s_i$ days for other values of $s_i$).
At any given time $t$, the probability of observing $n_i$ sequences of sublineage $i$ is multinomially distributed, given the total number of sequences from that day and the frequency of each $p_i(t)$.
Consequently, the likelihood of seeing the observed sequence data over all times $t$ and over all sublineages $j$ is proportional to $$L = \prod_t \prod_j p_i(t)^{n_i(t)}.$$
The [BBMLE](https://cran.r-project.org/web/packages/bbmle/bbmle.pdf) package in R was used to maximize the likelihood of the observed data (using the default optimization method, optim).
For each selection coefficient, 95% confidence intervals were obtained by profile likelihood (using uniroot).
Graphs illustrating the rise in frequency of a variant over time are shown (left panels), with the area of each dot proportional to the number of sequences.
95% confidence bands were obtained by randomly drawing 10,000 sets of parameters ($p_i$ and $s_i$ for each sub-type) using `RandomFromHessianOrMCMC`, assuming a multi-normal distribution around the maximum likelihood point (estimated from the Hessian matrix, [Pawitan 2001](https://books.google.ca/books?hl=en&lr=&id=WHsSDAAAQBAJ&oi=fnd&pg=PP1&dq=Pawitan+2001+likelihood&ots=v9sM5DuFrf&sig=vnRb--i2zu0jox_KnBSVxtG2aPg#v=onepage&q=Pawitan%202001%20likelihood&f=false)).
At each point in time, the 2.5%-97.5% range of values for $p_i(t)$ are then shown in the confidence bands.
Logit plots (right panels) show $$ln(\frac{p_i(t)}{p_{ref}(t)})$$ relative to a given reference genotype (here BA.1), which gives a line whose slope is the strength of selection $s_i$.
Changes in slope indicate changes in selection on a variant (*e.g.*, see [Otto et al.](https://mast.queensu.ca/~tday/pdf/Otto2021.pdf)).
These estimates of selection ignore heterogeneity within provinces and may be biased by the arrival of travel-related cases while frequencies are very low.
Sampling strategies that oversample clustered cases (*e.g.*, sequencing outbreaks) will introduce additional variation beyond the multinomial expectation, but these should lead to one-time shifts in frequency rather than trends over time.
Provinces with sampling strategies that are variant specific are removed, unless explicit information about the variant frequencies is available.
### Rates
#### Root-to-tip estimates of substitution rate
Maximum likelihood tree ([IQ-TREE](http://www.iqtree.org/)) processed with [root-to-tip regression](https://search.r-project.org/CRAN/refmans/ape/html/rtt.html) and plotting in R.
# Data notes by province {.tabset}
All analyses draw on the most recent publicly available viral sequence data on ViralSeq and should be interpreted with caution due to lags in reporting and sequencing priorities that can differ across provinces or territories. Note that the NCCID provides a timeline of Canadian events related to each variant: https://nccid.ca/covid-19-variants/.
## BC
### British Columbia
Provincial sequencing strategy includes a subset of representative positive samples and prioritized cases (outbreaks, long-term care, travel-related, vaccine escape, hospitalized).
Additional up-to-date covid data for this province can be found here:
http://www.bccdc.ca/health-info/diseases-conditions/covid-19/data-trends
## AB
### Alberta
Additional up-to-date COVID data for this province can be found here:
https://www.alberta.ca/stats/covid-19-alberta-statistics.htm#variants-of-concern
## SK
### Saskatchewan
Additional up-to-date COVID data for this province can be found here:
https://www.saskatchewan.ca/government/health-care-administration-and-provider-resources/treatment-procedures-and-guidelines/emerging-public-health-issues/2019-novel-coronavirus/cases-and-risk-of-covid-19-in-saskatchewan
## MB
### Manitoba
Additional up-to-date COVID data for this province can be found here:
https://geoportal.gov.mb.ca/apps/manitoba-covid-19/explore
## ON
### Ontario
Additional up-to-date COVID data for this province can be found here:
https://www.publichealthontario.ca/en/diseases-and-conditions/infectious-diseases/respiratory-diseases/novel-coronavirus/variants
## QC
### Quebec
Provincial random sequencing has been temporarily suspended as of Feb 8th, 2021. Quebec provides a list of updates on changes to screening and sequencing strategies, found here (in French): https://www.inspq.qc.ca/covid-19/donnees/variants#methodologie.
Additiona up-to-date COVID data for this province can be found here:
https://www.inspq.qc.ca/covid-19/donnees/variants
## NS
### Nova Scotia
Additional up-to-date COVID data for this province can be found here:
https://experience.arcgis.com/experience/204d6ed723244dfbb763ca3f913c5cad
## NB
### New Brunswick
Additional up-to-date COVID data for this province can be found here:
https://experience.arcgis.com/experience/8eeb9a2052d641c996dba5de8f25a8aa (NB dashboard)
## NL
### Newfoundland and Labrador
Additional up-to-date COVID data for this province can be found here:
https://covid-19-newfoundland-and-labrador-gnl.hub.arcgis.com/
# List of useful tools
Collect a list of bioinformatics, phylogenetic, and modelling tools that are useful for SARS-CoV-2 analyses:
* UShER: Ultrafast Sample placement on Existing tRee - for placing a small-ish dataset into the global GISAID phylogenetic tree [web-version: https://genome.ucsc.edu/cgi-bin/hgPhyloPlace, local-version: https://shusher.gi.ucsc.edu/]
* List of (mostly) modelling tools by CANMOD: [https://canmod.net/tools], includes RECON, outbreak tools for both modelling and genomic epi [https://github.com/reconhub]
* List of homoplaises in SARS-CoV-2: https://github.com/corneliusroemer/ncov-simplest/blob/main/data/exclude_sites_light.txt
* Erin Gill's COVID-19 dashboard [https://github.com/eringill/COVID_dashboard_reboot]
* The Epi Graph Network: training platform. Programming tools for health data analysis, African/European network of researchers and WHO Afro. [https://thegraphnetwork.training/]
* Nybbler tool for subsampling SARS-CoV-2 genome ensembles [https://github.com/nodrogluap/nybbler]
* Pokay tool for checking and reporitng mismatches [https://github.com/nodrogluap/pokay]
* IRIDA Canada's ID analysis platform for genomic epi [https://github.com/pvanheus/irida]
* cov-lineages (summaries of Pango lineages) [https://cov-lineages.org/lineage_list.html]
* CoVizu (analysis and visualization of the global diversity of SARS-CoV-2 genomes in real time) [https://github.com/PoonLab/covizu/]
* COVID-MVP (mutation tracker and visualization in real-time from Centre for Infectious Disease Genomics and One Health, CIDGOH) [https://covidmvp.cidgoh.ca/]
* Outbreak Info (SARS2 data explorer: lineage comparison, mutation tracker, etc) [https://outbreak.info/situation-reports]
# Acknowledgements and sources
We thank all the authors, developers, and contributors to the VirusSeq database for making their SARS-CoV-2 sequences publicly available. We especially thank the Canadian Public Health Laboratory Network, academic sequencing partners, diagnostic hospital labs, and other sequencing partners for the provision of the Canadian sequence data used in this work. Genome sequencing in Canada was supported by a Genome Canada grant to the Canadian COVID-19 Genomic Network (CanCOGeN).
We gratefully acknowledge all the Authors, the Originating laboratories responsible for obtaining the specimens, and the Submitting laboratories for generating the genetic sequence and metadata and sharing via the VirusSeq database, on which this research is based.
* The Canadian VirusSeq Data Portal (https://virusseq-dataportal.ca)
We wish to acknowledge the following organisations/laboratories for contributing data to the Portal: Canadian Public Health Laboratory Network (CPHLN), CanCOGGeN VirusSeq, Saskatchewan - Roy Romanow Provincial Laboratory (RRPL), Nova Scotia Health Authority, Alberta ProvLab North (APLN), Queen's University / Kingston Health Sciences Centre, National Microbiology Laboratory (NML), Institut National de Sante Publique du Quebec (INSPQ), BCCDC Public Health Laboratory, Public Health Ontario (PHO), Newfoundland and Labrador - Eastern Health, Unity Health Toronto, Ontario Institute for Cancer Research (OICR), Provincial Public Health Laboratory Network of Nova Scotia, Centre Hospitalier Universitaire Georges L. Dumont - New Brunswick, and Manitoba Cadham Provincial Laboratory. Please see the complete list of laboratories included in this repository.
* Public Health Agency of Canada (PHAC) / National Microbiology Laboratory (NML) - (https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html)
* Various provincial public health websites (e.g. INSPQ https://www.inspq.qc.ca/covid-19/donnees/)
# Session info {.tabset}
The version numbers of all packages in the current environment as well as information about the R install is reported below.
## Hide
## Show
```{r session_info}
sessionInfo()
```