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figure_5ef_deg.Rmd
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figure_5ef_deg.Rmd
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---
title: "Figure 5E and 5F: Deg and Volcanos"
author: "Matthew Angel"
date: "12/13/2021"
output:
rmarkdown::html_document:
toc: true
code_folding: hide
vignette: >
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
%\usepackage[utf8]{inputenc}
---
<style type="text/css">
body{ /* Normal */
font-size: 12px;
}
td { /* Table */
font-size: 8px;
}
h1.title {
font-size: 38px;
color: DarkRed;
}
h1 { /* Header 1 */
font-size: 28px;
color: DarkBlue;
}
h2 { /* Header 2 */
font-size: 22px;
color: DarkBlue;
}
h3 { /* Header 3 */
font-size: 18px;
font-family: "Times New Roman", Times, serif;
color: DarkBlue;
}
code.r{ /* Code block */
font-size: 12px;
}
pre { /* Code block - determines code spacing between lines */
font-size: 14px;
}
</style>
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```
# DEG analysis
```{r deg_analysis}
suppressMessages(library(limma))
suppressMessages(library(tidyverse))
suppressMessages(library(edgeR))
suppressMessages(library(stringr))
df <- read.csv("output/f60_formatted_data.csv")
annot <- read.csv("input/f60_metadata.csv")
annot$combined <- paste(annot$vaccination,annot$timepoint, sep="_")
df.m <- df[,-1]
targetfile <- annot[match(colnames(df.m),annot$sample_id),]
targetfile <- targetfile[rowSums(is.na(targetfile)) != ncol(targetfile), ]
df.m <- df.m[,match(targetfile$sample_id,colnames(df.m))]
#x <- 2^df.m
ordered_covariates=c("combined","animal_id")
ordered_covariates=ordered_covariates[order(ordered_covariates!="combined")]
targetfile <- targetfile %>% select(sample_id,one_of(ordered_covariates)) %>% as.data.frame()
row.names(targetfile) <- targetfile$sample_id
dm.formula <- as.formula(paste("~0 +", paste(ordered_covariates, sep="+", collapse="+")))
design=model.matrix(dm.formula, targetfile)
colnames(design) <- str_replace_all(colnames(design), "combined", "")
v <- new("EList", list(E = df.m, design = design))
rownames(v$E) <- df[["Gene"]]
as.data.frame(v$E) %>% rownames_to_column("Gene") -> df.voom
fit <- lmFit(v, design)
contrasts_of_interest <- c( "V1_d1-V1_d0",
"V2_d1-V2_d0",
"V3_d1-V3_d0",
"V4_d1-V4_d0")
cm <- makeContrasts(contrasts = contrasts_of_interest, levels=design)
fit2 <- contrasts.fit(fit, cm)
fit2 <- eBayes(fit2)
logFC = fit2$coefficients
colnames(logFC)=paste(colnames(logFC),"logFC",sep="_")
tstat = fit2$t
colnames(tstat)=paste(colnames(tstat),"tstat",sep="_")
FC = 2^fit2$coefficients
FC = ifelse(FC<1,-1/FC,FC)
colnames(FC)=paste(colnames(FC),"FC",sep="_")
pvalall=fit2$p.value
colnames(pvalall)=paste(colnames(pvalall),"pval",sep="_")
pvaladjall=apply(pvalall,2,function(x) p.adjust(x,"BH"))
colnames(pvaladjall)=paste(colnames(fit2$coefficients),"adjpval",sep="_")
finalres=as.data.frame(cbind(v$E,FC, logFC, tstat, pvalall, pvaladjall))
finalres %>% rownames_to_column("Gene") -> finalres
colnames(finalres)<-gsub("\\(|\\)","",colnames(finalres))
write.table(finalres,file="output/f60_deg_results.csv", sep = ",", row.names = FALSE, quote = FALSE)
```
# Volcano Plots Figures 5 and S6
```{r plot contrasts}
suppressMessages(library("EnhancedVolcano"))
suppressMessages(library("gridExtra"))
Plots <- list()
for(i in seq_along(contrasts_of_interest)){
contrast <- contrasts_of_interest[i]
lfccol <- paste0(contrast,"_logFC")
pvalcol <- paste0(contrast,"_adjpval")
select_columns <- c("Gene",as.name(lfccol),as.name(pvalcol))
finalres %>% dplyr::arrange(finalres[,pvalcol]) %>% dplyr::select_(.dots = select_columns) -> plt.df
# Set Y-max
negative_log10_p_values <- -log10(plt.df[,pvalcol])
ymax <- ceiling(max(negative_log10_p_values[is.finite(negative_log10_p_values)])) + 1
plt.df[,pvalcol][negative_log10_p_values > ymax] <- 10**(-ymax)
shapeCustom <- ifelse(negative_log10_p_values > ymax, 17, 19)
names(shapeCustom)<- rep("Exact",length(shapeCustom))
names(shapeCustom)[shapeCustom == 17] <- "Adjusted"
xmax = ceiling(max(plt.df[,lfccol])) + 1
xmin = floor(min(plt.df[,lfccol])) - 1
genes_to_label <- unique(plt.df$Gene[1:10])
plt.df[,"neglogpval"] <- -log10(plt.df[,pvalcol])
filtered_genes = plt.df$Gene[plt.df[,pvalcol] <= 0.05 & abs(plt.df[,lfccol]) >= 1]
Plots[[i]] <- EnhancedVolcano(toptable=plt.df,
x=lfccol,
y=pvalcol,
lab=plt.df[,"Gene"],
selectLab = genes_to_label,
title=contrast,
subtitle = paste0("Significant=",length(filtered_genes)),
xlab=lfccol,
ylab=bquote(~-Log[10]~.("(adjpval)")),
xlim=c(xmin,xmax),
ylim=c(0, ymax),
pCutoff=0.05,
FCcutoff=1,
axisLabSize=24,
labSize=4,
pointSize=2,
shapeCustom=shapeCustom,
drawConnectors = TRUE,
labCol = "black")
}
nplots=length(Plots)
nrows=ceiling(nplots/ceiling(sqrt(nplots)))
imageWidth = 3000*ceiling(nplots/nrows)
imageHeight = 3000*nrows
dpi = 300
png(
filename="plots/figure_5_and_S6_volcano_plots.png",
width=imageWidth,
height=imageHeight,
units="px",
pointsize=4,
bg="white",
res=dpi,
type="cairo")
do.call("grid.arrange", c(Plots, nrow=nrows))
null_var <- dev.off()
knitr::include_graphics("plots/figure_5_and_S6_volcano_plots.png")
```
# Session Info
```{r session_info}
sessionInfo()
```