1671 lines (1277 with data), 59.7 kB
---
title: "COVID19: DGE visualisation"
output: html_notebook
---
This is to visualise the output from DE testing run on each T cell subset, either comparing across linear and quadratic trends.
```{r, warning=FALSE, message=FALSE}
library(ggplot2)
library(ggthemes)
library(ggsci)
library(scales)
library(enrichR)
library(ggrepel)
library(cowplot)
library(RColorBrewer)
```
# DGE testing across COVID19 severity - linear trends
Testing for a linear trend across disease severity, from healthy through to critical.
```{r}
dge.files <- list.files("~/Dropbox/COVID19/Data/Updated/DEResults.dir/", pattern="\\.L\\.csv")
linear.dge.list <- list()
for(x in seq_along(dge.files)){
x.csv <- read.csv(paste0("~/Dropbox/COVID19/Data/Updated/DEResults.dir/", dge.files[x]),
header=TRUE, stringsAsFactors=FALSE)
x.annot <- gsub(dge.files[x], pattern="DE_(\\S*)_Severity\\.L\\.csv", replacement="\\1")
x.csv$Sub.Annotation <- x.annot
linear.dge.list[[dge.files[x]]] <- x.csv
}
linear.dge.df <- do.call(rbind.data.frame, linear.dge.list)
linear.dge.df$Diff <- sign(linear.dge.df$logFC)
linear.dge.df$Diff[linear.dge.df$FDR >= 0.01] <- 0
table(linear.dge.df$Diff, linear.dge.df$Sub.Annotation)
```
There are variable numbers of gene DE across categories, but generally quite a small number.
```{r}
# collect all de genes for plotting
de.genes.all <- unique(linear.dge.df$X[linear.dge.df$FDR < 0.01])
write.table(de.genes.all,
file="~/Dropbox/COVID19/Data/Updated/DEResults.dir/ALL_linear_DEgenes.tsv",
quote=FALSE, row.names=FALSE, sep="\t")
```
## CD4.CM
```{r}
enrich.dbs <- c("Transcription_Factor_PPIs", "MSigDB_Computational", "MSigDB_Hallmark_2020",
"KEGG_2019_Human", "GO_Biological_Process_2018")
```
```{r, fig.height=3.95, fig.width=4.95}
cd4.cm.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.CM"), ]$X
cd4.cm.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.CM"), ]$FDR > 1e-3] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.CM"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd4.cm.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.CM-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd4.cm.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.CM") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd4.cm.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.CM") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd4.msigdb.up <- cd4.cm.enriched.up[["MSigDB_Hallmark_2020"]]
cd4.msigdb.up$Dir <- "Up"
cd4.msigdb.down <- cd4.cm.enriched.down[["MSigDB_Hallmark_2020"]]
cd4.msigdb.down$Dir <- "Down"
cd4.msigdb <- do.call(rbind.data.frame, list(cd4.msigdb.up, cd4.msigdb.down))
cd4.msigdb$logP <- -log10(cd4.msigdb$P.value)
cd4.msigdb$logP[cd4.msigdb$Dir %in% c("Down")] <- -1 * cd4.msigdb$logP[cd4.msigdb$Dir %in% c("Down")]
ggplot(cd4.msigdb[cd4.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.CM-linear_MSigDB_enriched.pdf",
height=4.15, width=4.15, useDingbats=FALSE) +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd4.tfppi.up <- cd4.cm.enriched.up[["Transcription_Factor_PPIs"]]
cd4.tfppi.up$Dir <- "Up"
cd4.tfppi.down <- cd4.cm.enriched.down[["Transcription_Factor_PPIs"]]
cd4.tfppi.down$Dir <- "Down"
cd4.tfppi <- do.call(rbind.data.frame, list(cd4.tfppi.up, cd4.tfppi.down))
cd4.tfppi$logP <- -log10(cd4.tfppi$P.value)
cd4.tfppi$logP[cd4.tfppi$Dir %in% c("Down")] <- -1 * cd4.tfppi$logP[cd4.tfppi$Dir %in% c("Down")]
ggplot(cd4.tfppi[cd4.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD4.Naive
```{r, fig.height=3.95, fig.width=4.95}
cd4.naive.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Naive"), ]$X
cd4.naive.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Naive"), ]$FDR > 1e-3] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Naive"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd4.naive.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.Naive-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd4.naive.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Naive") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd4.naive.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Naive") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd4.naive.msigdb.up <- cd4.naive.enriched.up[["MSigDB_Hallmark_2020"]]
cd4.naive.msigdb.up$Dir <- "Up"
cd4.naive.msigdb.down <- cd4.naive.enriched.down[["MSigDB_Hallmark_2020"]]
cd4.naive.msigdb.down$Dir <- "Down"
cd4.naive.msigdb <- do.call(rbind.data.frame, list(cd4.naive.msigdb.up, cd4.naive.msigdb.down))
cd4.naive.msigdb$logP <- -log10(cd4.naive.msigdb$P.value)
cd4.naive.msigdb$logP[cd4.naive.msigdb$Dir %in% c("Down")] <- -1 * cd4.naive.msigdb$logP[cd4.naive.msigdb$Dir %in% c("Down")]
ggplot(cd4.naive.msigdb[cd4.naive.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.Naive-linear_MSigDB_enriched.pdf",
height=4.15, width=4.15, useDingbats=FALSE) +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd4.naive.tfppi.up <- cd4.naive.enriched.up[["Transcription_Factor_PPIs"]]
cd4.naive.tfppi.up$Dir <- "Up"
cd4.naive.tfppi.down <- cd4.naive.enriched.down[["Transcription_Factor_PPIs"]]
cd4.naive.tfppi.down$Dir <- "Down"
cd4.naive.tfppi <- do.call(rbind.data.frame, list(cd4.naive.tfppi.up, cd4.naive.tfppi.down))
cd4.naive.tfppi$logP <- -log10(cd4.naive.tfppi$P.value)
cd4.naive.tfppi$logP[cd4.naive.tfppi$Dir %in% c("Down")] <- -1 * cd4.naive.tfppi$logP[cd4.naive.tfppi$Dir %in% c("Down")]
ggplot(cd4.naive.tfppi[cd4.naive.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD8.Naive
```{r, fig.height=3.95, fig.width=4.95}
cd8.naive.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.Naive"), ]$X
cd8.naive.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.Naive"), ]$FDR > 1e-3] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.Naive"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd8.naive.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD8.Naive-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd8.naive.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.Naive") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd8.naive.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.Naive") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd8.naive.msigdb.up <- cd8.naive.enriched.up[["MSigDB_Hallmark_2020"]]
cd8.naive.msigdb.up$Dir <- "Up"
cd8.naive.msigdb.down <- cd8.naive.enriched.down[["MSigDB_Hallmark_2020"]]
cd8.naive.msigdb.down$Dir <- "Down"
cd8.naive.msigdb <- do.call(rbind.data.frame, list(cd8.naive.msigdb.up, cd8.naive.msigdb.down))
cd8.naive.msigdb$logP <- -log10(cd8.naive.msigdb$P.value)
cd8.naive.msigdb$logP[cd8.naive.msigdb$Dir %in% c("Down")] <- -1 * cd8.naive.msigdb$logP[cd8.naive.msigdb$Dir %in% c("Down")]
ggplot(cd8.naive.msigdb[cd8.naive.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd8.naive.tfppi.up <- cd8.naive.enriched.up[["Transcription_Factor_PPIs"]]
cd8.naive.tfppi.up$Dir <- "Up"
cd8.naive.tfppi.down <- cd8.naive.enriched.down[["Transcription_Factor_PPIs"]]
cd8.naive.tfppi.down$Dir <- "Down"
cd8.naive.tfppi <- do.call(rbind.data.frame, list(cd8.naive.tfppi.up, cd8.naive.tfppi.down))
cd8.naive.tfppi$logP <- -log10(cd8.naive.tfppi$P.value)
cd8.naive.tfppi$logP[cd8.naive.tfppi$Dir %in% c("Down")] <- -1 * cd8.naive.tfppi$logP[cd8.naive.tfppi$Dir %in% c("Down")]
ggplot(cd8.naive.tfppi[cd8.naive.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD8.EM
```{r, fig.height=3.95, fig.width=4.95}
cd8.em.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.EM"), ]$X
cd8.em.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.EM"), ]$FDR > 1e-2] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.EM"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd8.em.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD8.EM-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd8.em.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.EM") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd8.em.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.EM") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd8.em.msigdb.up <- cd8.em.enriched.up[["MSigDB_Hallmark_2020"]]
cd8.em.msigdb.up$Dir <- "Up"
cd8.em.msigdb.down <- cd8.em.enriched.down[["MSigDB_Hallmark_2020"]]
if(nrow(cd8.em.msigdb.down) > 0){
cd8.em.msigdb.down$Dir <- "Down"
cd8.em.msigdb <- do.call(rbind.data.frame, list(cd8.em.msigdb.up, cd8.em.msigdb.down))
} else{
cd8.em.msigdb <- do.call(rbind.data.frame, list(cd8.em.msigdb.up))
}
cd8.em.msigdb$logP <- -log10(cd8.em.msigdb$P.value)
cd8.em.msigdb$logP[cd8.em.msigdb$Dir %in% c("Down")] <- -1 * cd8.em.msigdb$logP[cd8.em.msigdb$Dir %in% c("Down")]
ggplot(cd8.em.msigdb[cd8.em.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd8.em.tfppi.up <- cd8.em.enriched.up[["Transcription_Factor_PPIs"]]
cd8.em.tfppi.up$Dir <- "Up"
cd8.em.tfppi.down <- cd8.em.enriched.down[["Transcription_Factor_PPIs"]]
if(nrow(cd8.em.tfppi.down) > 0){
cd8.em.tfppi.down$Dir <- "Down"
cd8.em.tfppi <- do.call(rbind.data.frame, list(cd8.em.tfppi.up, cd8.em.tfppi.down))
} else{
cd8.em.tfppi <- do.call(rbind.data.frame, list(cd8.em.tfppi.up))
}
cd8.em.tfppi$logP <- -log10(cd8.em.tfppi$P.value)
cd8.em.tfppi$logP[cd8.em.tfppi$Dir %in% c("Down")] <- -1 * cd8.em.tfppi$logP[cd8.em.tfppi$Dir %in% c("Down")]
ggplot(cd8.em.tfppi[cd8.em.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD8.TE
```{r, fig.height=3.95, fig.width=4.95}
cd8.te.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.TE"), ]$X
cd8.te.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.TE"), ]$FDR > 1e-2] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.TE"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd8.te.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD8.TE-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd8.te.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.TE") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd8.te.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD8.TE") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd8.te.msigdb.up <- cd8.te.enriched.up[["MSigDB_Hallmark_2020"]]
cd8.te.msigdb.up$Dir <- "Up"
cd8.te.msigdb.down <- cd8.te.enriched.down[["MSigDB_Hallmark_2020"]]
if(nrow(cd8.te.msigdb.down) > 0){
cd8.te.msigdb.down$Dir <- "Down"
}
cd8.te.msigdb <- do.call(rbind.data.frame, list(cd8.te.msigdb.up, cd8.te.msigdb.down))
cd8.te.msigdb$logP <- -log10(cd8.te.msigdb$P.value)
cd8.te.msigdb$logP[cd8.te.msigdb$Dir %in% c("Down")] <- -1 * cd8.te.msigdb$logP[cd8.te.msigdb$Dir %in% c("Down")]
ggplot(cd8.te.msigdb[cd8.te.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd8.te.tfppi.up <- cd8.te.enriched.up[["Transcription_Factor_PPIs"]]
if(nrow(cd8.te.tfppi.up) > 0){
cd8.te.tfppi.up$Dir <- "Up"
}
cd8.te.tfppi.down <- cd8.te.enriched.down[["Transcription_Factor_PPIs"]]
if(nrow(cd8.te.tfppi.down) > 0){
cd8.te.tfppi.down$Dir <- "Down"
}
cd8.te.tfppi <- do.call(rbind.data.frame, list(cd8.te.tfppi.up, cd8.te.tfppi.down))
cd8.te.tfppi$logP <- -log10(cd8.te.tfppi$P.value)
cd8.te.tfppi$logP[cd8.te.tfppi$Dir %in% c("Down")] <- -1 * cd8.te.tfppi$logP[cd8.te.tfppi$Dir %in% c("Down")]
cd8.te.tfppi$Sub
ggplot(cd8.te.tfppi[cd8.te.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD4.Tfh
```{r, fig.height=3.95, fig.width=4.95}
tfh.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Tfh"), ]$X
tfh.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Tfh"), ]$FDR > 1e-3] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Tfh"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=tfh.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.Tfh-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
tfh.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Tfh") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
tfh.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("CD4.Tfh") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
tfh.msigdb.up <- tfh.enriched.up[["MSigDB_Hallmark_2020"]]
tfh.msigdb.up$Dir <- "Up"
tfh.msigdb.down <- tfh.enriched.down[["MSigDB_Hallmark_2020"]]
if(!is.null(tfh.msigdb.down)){
tfh.msigdb.down$Dir <- "Down"
tfh.msigdb <- do.call(rbind.data.frame, list(tfh.msigdb.up, tfh.msigdb.down))
} else{
tfh.msigdb <- do.call(rbind.data.frame, list(tfh.msigdb.up))
}
tfh.msigdb$logP <- -log10(tfh.msigdb$P.value)
tfh.msigdb$logP[tfh.msigdb$Dir %in% c("Down")] <- -1 * tfh.msigdb$logP[tfh.msigdb$Dir %in% c("Down")]
ggplot(tfh.msigdb[tfh.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
tfh.tfppi.up <- tfh.enriched.up[["Transcription_Factor_PPIs"]]
tfh.tfppi.up$Dir <- "Up"
tfh.tfppi.down <- tfh.enriched.down[["Transcription_Factor_PPIs"]]
if(!is.null(tfh.tfppi.down)){
tfh.tfppi.down$Dir <- "Down"
tfh.tfppi <- do.call(rbind.data.frame, list(tfh.tfppi.up, tfh.tfppi.down))
} else{
tfh.tfppi <- do.call(rbind.data.frame, list(tfh.tfppi.up))
}
tfh.tfppi <- do.call(rbind.data.frame, list(tfh.tfppi.up, tfh.tfppi.down))
tfh.tfppi$logP <- -log10(tfh.tfppi$P.value)
tfh.tfppi$logP[tfh.tfppi$Dir %in% c("Down")] <- -1 * tfh.tfppi$logP[tfh.tfppi$Dir %in% c("Down")]
ggplot(tfh.tfppi[tfh.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## gdT
```{r, fig.height=3.95, fig.width=4.95}
gdt.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("gdT"), ]$X
gdt.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("gdT"), ]$FDR > 1e-3] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("gdT"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=gdt.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/gdT-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
gdt.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("gdT") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
gdt.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("gdT") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
gdt.msigdb.up <- gdt.enriched.up[["MSigDB_Hallmark_2020"]]
if(!is.null(gdt.msigdb.up)){
gdt.msigdb.up$Dir <- "Up"
}
gdt.msigdb.down <- gdt.enriched.down[["MSigDB_Hallmark_2020"]]
if(!is.null(gdt.msigdb.down)){
gdt.msigdb.down$Dir <- "Down"
}
if(!is.null(gdt.msigdb.up) & !is.null(gdt.msigdb.down)){
gdt.msigdb <- do.call(rbind.data.frame, list(gdt.msigdb.up, gdt.msigdb.down))
gdt.msigdb$logP <- -log10(gdt.msigdb$P.value)
gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")] <- -1 * gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")]
} else if(!is.null(gdt.msigdb.up) & is.null(gdt.msigdb.down)){
gdt.msigdb <- do.call(rbind.data.frame, list(gdt.msigdb.up))
gdt.msigdb$logP <- -log10(gdt.msigdb$P.value)
gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")] <- -1 * gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")]
} else if(is.null(gdt.msigdb.up) & !is.null(gdt.msigdb.down)){
gdt.msigdb <- do.call(rbind.data.frame, list(gdt.msigdb.down))
gdt.msigdb$logP <- -log10(gdt.msigdb$P.value)
gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")] <- -1 * gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")]
} else{
gdt.msigdb <- NULL
}
if(!is.null(gdt.msigdb)){
ggplot(gdt.msigdb[gdt.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
}
```
```{r, fig.height=4.15, fig.width=4.15}
gdt.tfppi.up <- gdt.enriched.up[["Transcription_Factor_PPIs"]]
gdt.tfppi.up$Dir <- "Up"
gdt.tfppi.down <- gdt.enriched.down[["Transcription_Factor_PPIs"]]
if(!is.null(gdt.tfppi.down)){
gdt.tfppi.down$Dir <- "Down"
gdt.tfppi <- do.call(rbind.data.frame, list(gdt.tfppi.up, gdt.tfppi.down))
gdt.tfppi$logP <- -log10(gdt.tfppi$P.value)
gdt.tfppi$logP[gdt.tfppi$Dir %in% c("Down")] <- -1 * gdt.tfppi$logP[gdt.tfppi$Dir %in% c("Down")]
} else{
gdt.tfppi <- do.call(rbind.data.frame, list(gdt.tfppi.up))
gdt.tfppi$logP <- -log10(gdt.tfppi$P.value)
gdt.tfppi$logP[gdt.tfppi$Dir %in% c("Down")] <- -1 * gdt.tfppi$logP[gdt.tfppi$Dir %in% c("Down")]
}
ggplot(gdt.tfppi[gdt.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## MAIT
```{r, fig.height=3.95, fig.width=4.95}
mait.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("MAIT"), ]$X
mait.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("MAIT"), ]$FDR > 1e-3] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("MAIT"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=mait.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/MAIT-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
mait.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("MAIT") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
mait.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("MAIT") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
mait.msigdb.up <- mait.enriched.up[["MSigDB_Hallmark_2020"]]
if(!is.null(mait.msigdb.up)){
mait.msigdb.up$Dir <- "Up"
}
mait.msigdb.down <- mait.enriched.down[["MSigDB_Hallmark_2020"]]
if(!is.null(mait.msigdb.down)){
mait.msigdb.down$Dir <- "Down"
}
if(!is.null(mait.msigdb.down) & !is.null(mait.msigdb.up)){
mait.msigdb <- do.call(rbind.data.frame, list(mait.msigdb.up, mait.msigdb.down))
} else if(is.null(mait.msigdb.down) & !is.null(mait.msigdb.up)){
mait.msigdb <- do.call(rbind.data.frame, list(mait.msigdb.up))
} else if(!is.null(mait.msigdb.down) & is.null(mait.msigdb.up)){
mait.msigdb <- do.call(rbind.data.frame, list(mait.msigdb.down))
}
mait.msigdb$logP <- -log10(mait.msigdb$P.value)
mait.msigdb$logP[mait.msigdb$Dir %in% c("Down")] <- -1 * mait.msigdb$logP[mait.msigdb$Dir %in% c("Down")]
ggplot(mait.msigdb[mait.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
mait.tfppi.up <- mait.enriched.up[["Transcription_Factor_PPIs"]]
if(!is.null(mait.tfppi.up)){
mait.tfppi.up$Dir <- "Up"
}
mait.tfppi.down <- mait.enriched.down[["Transcription_Factor_PPIs"]]
if(!is.null(mait.tfppi.down)){
mait.tfppi.down$Dir <- "Down"
}
if(!is.null(mait.tfppi.up) & !is.null(mait.tfppi.down)){
mait.tfppi <- do.call(rbind.data.frame, list(mait.tfppi.up, mait.tfppi.down))
} else if(!is.null(mait.tfppi.up) & is.null(mait.tfppi.down)){
mait.tfppi <- do.call(rbind.data.frame, list(mait.tfppi.up))
} else if(is.null(mait.tfppi.up) & !is.null(mait.tfppi.down)){
mait.tfppi <- do.call(rbind.data.frame, list(mait.tfppi.down))
}
mait.tfppi$logP <- -log10(mait.tfppi$P.value)
mait.tfppi$logP[mait.tfppi$Dir %in% c("Down")] <- -1 * mait.tfppi$logP[mait.tfppi$Dir %in% c("Down")]
ggplot(mait.tfppi[mait.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## Treg
```{r, fig.height=3.95, fig.width=4.95}
treg.labels <- linear.dge.df[linear.dge.df$Sub.Annotation %in% c("Treg"), ]$X
treg.labels[linear.dge.df[linear.dge.df$Sub.Annotation %in% c("Treg"), ]$FDR > 1e-3] <- ""
ggplot(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("Treg"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=treg.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/Treg-linear_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
treg.enriched.up <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("Treg") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC > 0, ]$X,
enrich.dbs)
treg.enriched.down <- enrichr(linear.dge.df[linear.dge.df$Sub.Annotation %in% c("Treg") &
linear.dge.df$FDR < 0.01 &
linear.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
treg.msigdb.up <- treg.enriched.up[["MSigDB_Hallmark_2020"]]
treg.msigdb.up$Dir <- "Up"
treg.msigdb.down <- treg.enriched.down[["MSigDB_Hallmark_2020"]]
if(!is.null(treg.msigdb.down)){
treg.msigdb.down$Dir <- "Down"
}
treg.msigdb <- do.call(rbind.data.frame, list(treg.msigdb.up, treg.msigdb.down))
treg.msigdb$logP <- -log10(treg.msigdb$P.value)
treg.msigdb$logP[treg.msigdb$Dir %in% c("Down")] <- -1 * treg.msigdb$logP[treg.msigdb$Dir %in% c("Down")]
ggplot(treg.msigdb[treg.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
treg.tfppi.up <- treg.enriched.up[["Transcription_Factor_PPIs"]]
treg.tfppi.up$Dir <- "Up"
treg.tfppi.down <- treg.enriched.down[["Transcription_Factor_PPIs"]]
if(!is.null(treg.tfppi.down)){
treg.tfppi.down$Dir <- "Down"
}
treg.tfppi <- do.call(rbind.data.frame, list(treg.tfppi.up, treg.tfppi.down))
treg.tfppi$logP <- -log10(treg.tfppi$P.value)
treg.tfppi$logP[treg.tfppi$Dir %in% c("Down")] <- -1 * treg.tfppi$logP[treg.tfppi$Dir %in% c("Down")]
ggplot(treg.tfppi[treg.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
Aggregate all results from the linear trend analysis.
```{r}
cd4.msigdb$Sub.Annotation <- "CD4.CM"
cd4.naive.msigdb$Sub.Annotation <- "CD4.Naive"
cd8.naive.msigdb$Sub.Annotation <- "CD8.Naive"
cd8.em.msigdb$Sub.Annotation <- "CD8.EM"
cd8.te.msigdb$Sub.Annotation <- "CD8.TE"
tfh.msigdb$Sub.Annotation <- "CD4.Tfh"
# treg.msigdb$Sub.Annotation <- "Treg"
mait.msigdb$Sub.Annotation <- "MAIT"
# gdt.msigdb$Sub.Annotation <- "gdT"
msigdb.linear.enrichments <- list(cd4.msigdb, cd4.naive.msigdb, cd8.naive.msigdb, cd8.em.msigdb, cd8.te.msigdb,
tfh.msigdb, mait.msigdb)
msigdb.linear.enrich.df <- do.call(rbind.data.frame,
msigdb.linear.enrichments)
```
```{r}
cell.order <- c("CD4.Naive", "CD4.CM", "CD4.EM", "CD4.IL22", "CD4.Prolif", "CD4.Th1", "CD4.Th2", "CD4.Th17", "CD4.Tfh",
"Treg", "CD8.Naive", "CD8.Activated", "CD8.Prolif", "CD8.CM", "CD8.TE", "CD8.EM", "gdT", "MAIT", "NKT")
all.qual.cols <- brewer.pal.info[brewer.pal.info$category %in% c("qual") & brewer.pal.info$colorblind, ]
col_vector = unlist(mapply(brewer.pal, all.qual.cols$maxcolors, rownames(all.qual.cols)))
# cell.cols <- col_vector[c(1:7, 9:19)]
cell.cols <- col_vector[c(1:17, 19, 20)]
names(cell.cols) <- cell.order
```
```{r, fig.height=5.95, fig.width=6.95}
ggplot(msigdb.linear.enrich.df[msigdb.linear.enrich.df$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP, colour=Sub.Annotation)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point(size=3.5, shape=18) +
scale_colour_manual(values=cell.cols) +
#facet_wrap(~Term, scales="free_y", ncol=1) +
labs(x="Pathway", y="Odds Rato") +
guides(colour=guide_legend(title="Cell Type", override.aes=list(size=5, shape=18))) +
theme_cowplot() +
coord_flip() +
theme(panel.spacing=unit(0, "lines"),
strip.text=element_blank(),
strip.background=element_blank()) +
ggsave("~/Dropbox/COVID19/Updated_plots/Tcell-linear_MSigDB_enriched.pdf",
height=5.95, width=6.95, useDingbats=FALSE) +
NULL
```
And for the enriched TFs. Aggregate all results from the linear trend analysis.
```{r}
cd4.tfppi$Sub.Annotation <- "CD4.CM"
cd4.naive.tfppi$Sub.Annotation <- "CD4.Naive"
cd8.naive.tfppi$Sub.Annotation <- "CD8.Naive"
cd8.em.tfppi$Sub.Annotation <- "CD8.EM"
cd8.te.tfppi$Sub.Annotation <- "CD8.TE"
tfh.tfppi$Sub.Annotation <- "CD4.Tfh"
# treg.tfppi$Sub.Annotation <- "Treg"
mait.tfppi$Sub.Annotation <- "MAIT"
# gdt.tfppi$Sub.Annotation <- "gdT"
tfppi.linear.enrichments <- list(cd4.tfppi, cd4.naive.tfppi, cd8.naive.tfppi, cd8.em.tfppi, cd8.te.tfppi,
tfh.tfppi, mait.tfppi)
tfppi.linear.enrich.df <- do.call(rbind.data.frame,
tfppi.linear.enrichments)
```
```{r, fig.height=8.95, fig.width=6.95}
ggplot(tfppi.linear.enrich.df[tfppi.linear.enrich.df$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP, colour=Sub.Annotation)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point(size=3.5, shape=18) +
scale_colour_manual(values=cell.cols) +
#facet_wrap(~Term, scales="free_y", ncol=1) +
labs(x="Pathway", y="Odds Rato") +
guides(colour=guide_legend(title="Cell Type", override.aes=list(size=5, shape=18))) +
theme_cowplot() +
coord_flip() +
theme(panel.spacing=unit(0, "lines"),
strip.text=element_blank(),
strip.background=element_blank()) +
ggsave("~/Dropbox/COVID19/Updated_plots/Tcell-linear_TFPPI_enriched.pdf",
height=5.95, width=6.95, useDingbats=FALSE) +
NULL
```
# DGE testing across COVID19 severity - quadratic trends
As the model above, but testing for a linear trend across disease severity, from healthy through to critical.
```{r}
dge.files <- list.files("~/Dropbox/COVID19/Data/Updated/DEResults.dir/", pattern="\\.Q\\.csv")
quadratic.dge.list <- list()
for(x in seq_along(dge.files)){
x.csv <- read.csv(paste0("~/Dropbox/COVID19/Data/Updated/DEResults.dir/", dge.files[x]),
header=TRUE, stringsAsFactors=FALSE)
x.annot <- gsub(dge.files[x], pattern="DE_(\\S*)_Severity\\.Q\\.csv", replacement="\\1")
x.csv$Sub.Annotation <- x.annot
quadratic.dge.list[[dge.files[x]]] <- x.csv
}
quadratic.dge.df <- do.call(rbind.data.frame, quadratic.dge.list)
quadratic.dge.df$Diff <- sign(quadratic.dge.df$logFC)
quadratic.dge.df$Diff[quadratic.dge.df$FDR >= 0.01] <- 0
table(quadratic.dge.df$Diff, quadratic.dge.df$Sub.Annotation)
```
There are variable numbers of gene DE across categories, but generally quite a small number. Somehow this doesn't feel right...
## CD4.CM
```{r}
enrich.dbs <- c("Transcription_Factor_PPIs", "MSigDB_Computational", "MSigDB_Hallmark_2020",
"UK_Biobank_GWAS_v1", "KEGG_2019_Human", "GO_Biological_Process_2018")
```
```{r, fig.height=3.95, fig.width=4.95}
cd4.cm.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.CM"), ]$X
cd4.cm.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.CM"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.CM"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd4.cm.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.CM-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd4.cm.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.CM") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd4.cm.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.CM") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd4.msigdb.up <- cd4.cm.enriched.up[["MSigDB_Hallmark_2020"]]
cd4.msigdb.up$Dir <- "Up"
cd4.msigdb.down <- cd4.cm.enriched.down[["MSigDB_Hallmark_2020"]]
cd4.msigdb.down$Dir <- "Down"
cd4.msigdb <- do.call(rbind.data.frame, list(cd4.msigdb.up, cd4.msigdb.down))
cd4.msigdb$logP <- -log10(cd4.msigdb$P.value)
cd4.msigdb$logP[cd4.msigdb$Dir %in% c("Down")] <- -1 * cd4.msigdb$logP[cd4.msigdb$Dir %in% c("Down")]
ggplot(cd4.msigdb[cd4.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd4.tfppi.up <- cd4.cm.enriched.up[["Transcription_Factor_PPIs"]]
cd4.tfppi.up$Dir <- "Up"
cd4.tfppi.down <- cd4.cm.enriched.down[["Transcription_Factor_PPIs"]]
cd4.tfppi.down$Dir <- "Down"
cd4.tfppi <- do.call(rbind.data.frame, list(cd4.tfppi.up, cd4.tfppi.down))
cd4.tfppi$logP <- -log10(cd4.tfppi$P.value)
cd4.tfppi$logP[cd4.tfppi$Dir %in% c("Down")] <- -1 * cd4.tfppi$logP[cd4.tfppi$Dir %in% c("Down")]
ggplot(cd4.tfppi[cd4.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD4.Naive
```{r, fig.height=3.95, fig.width=4.95}
cd4.naive.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Naive"), ]$X
cd4.naive.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Naive"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Naive"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd4.naive.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.Naive-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbat=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd4.naive.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Naive") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd4.naive.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Naive") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd4.naive.msigdb.up <- cd4.naive.enriched.up[["MSigDB_Hallmark_2020"]]
cd4.naive.msigdb.up$Dir <- "Up"
cd4.naive.msigdb.down <- cd4.naive.enriched.down[["MSigDB_Hallmark_2020"]]
cd4.naive.msigdb.down$Dir <- "Down"
cd4.naive.msigdb <- do.call(rbind.data.frame, list(cd4.naive.msigdb.up, cd4.naive.msigdb.down))
cd4.naive.msigdb$logP <- -log10(cd4.naive.msigdb$P.value)
cd4.naive.msigdb$logP[cd4.naive.msigdb$Dir %in% c("Down")] <- -1 * cd4.naive.msigdb$logP[cd4.naive.msigdb$Dir %in% c("Down")]
ggplot(cd4.naive.msigdb[cd4.naive.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd4.naive.tfppi.up <- cd4.naive.enriched.up[["Transcription_Factor_PPIs"]]
cd4.naive.tfppi.up$Dir <- "Up"
cd4.naive.tfppi.down <- cd4.naive.enriched.down[["Transcription_Factor_PPIs"]]
cd4.naive.tfppi.down$Dir <- "Down"
cd4.naive.tfppi <- do.call(rbind.data.frame, list(cd4.naive.tfppi.up, cd4.naive.tfppi.down))
cd4.naive.tfppi$logP <- -log10(cd4.naive.tfppi$P.value)
cd4.naive.tfppi$logP[cd4.naive.tfppi$Dir %in% c("Down")] <- -1 * cd4.naive.tfppi$logP[cd4.naive.tfppi$Dir %in% c("Down")]
ggplot(cd4.naive.tfppi[cd4.naive.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD8.Naive
```{r, fig.height=3.95, fig.width=4.95}
cd8.naive.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.Naive"), ]$X
cd8.naive.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.Naive"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.Naive"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd8.naive.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD8.Naive-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbat=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd8.naive.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.Naive") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd8.naive.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.Naive") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd8.naive.msigdb.up <- cd8.naive.enriched.up[["MSigDB_Hallmark_2020"]]
cd8.naive.msigdb.up$Dir <- "Up"
cd8.naive.msigdb.down <- cd8.naive.enriched.down[["MSigDB_Hallmark_2020"]]
cd8.naive.msigdb.down$Dir <- "Down"
cd8.naive.msigdb <- do.call(rbind.data.frame, list(cd8.naive.msigdb.up, cd8.naive.msigdb.down))
cd8.naive.msigdb$logP <- -log10(cd8.naive.msigdb$P.value)
cd8.naive.msigdb$logP[cd8.naive.msigdb$Dir %in% c("Down")] <- -1 * cd8.naive.msigdb$logP[cd8.naive.msigdb$Dir %in% c("Down")]
ggplot(cd8.naive.msigdb[cd8.naive.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd8.naive.tfppi.up <- cd8.naive.enriched.up[["Transcription_Factor_PPIs"]]
cd8.naive.tfppi.up$Dir <- "Up"
cd8.naive.tfppi.down <- cd8.naive.enriched.down[["Transcription_Factor_PPIs"]]
cd8.naive.tfppi.down$Dir <- "Down"
cd8.naive.tfppi <- do.call(rbind.data.frame, list(cd8.naive.tfppi.up, cd8.naive.tfppi.down))
cd8.naive.tfppi$logP <- -log10(cd8.naive.tfppi$P.value)
cd8.naive.tfppi$logP[cd8.naive.tfppi$Dir %in% c("Down")] <- -1 * cd8.naive.tfppi$logP[cd8.naive.tfppi$Dir %in% c("Down")]
ggplot(cd8.naive.tfppi[cd8.naive.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD8.EM
```{r, fig.height=3.95, fig.width=4.95}
cd8.em.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.EM"), ]$X
cd8.em.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.EM"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.EM"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd8.em.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD8.CEMquadratic_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd8.em.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.EM") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd8.em.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.EM") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd8.em.msigdb.up <- cd8.em.enriched.up[["MSigDB_Hallmark_2020"]]
if(!is.null(cd8.em.msigdb.up)){
cd8.em.msigdb.up$Dir <- "Up"
cd8.em.msigdb.down <- cd8.em.enriched.down[["MSigDB_Hallmark_2020"]]
cd8.em.msigdb.down$Dir <- "Down"
cd8.em.msigdb <- do.call(rbind.data.frame, list(cd8.em.msigdb.up, cd8.em.msigdb.down))
} else{
cd8.em.msigdb.down <- cd8.em.enriched.down[["MSigDB_Hallmark_2020"]]
cd8.em.msigdb.down$Dir <- "Down"
cd8.em.msigdb <- do.call(rbind.data.frame, list(cd8.em.msigdb.up, cd8.em.msigdb.down))
}
cd8.em.msigdb$logP <- -log10(cd8.em.msigdb$P.value)
cd8.em.msigdb$logP[cd8.em.msigdb$Dir %in% c("Down")] <- -1 * cd8.em.msigdb$logP[cd8.em.msigdb$Dir %in% c("Down")]
ggplot(cd8.em.msigdb[cd8.em.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd8.em.tfppi.up <- cd8.em.enriched.up[["Transcription_Factor_PPIs"]]
if(!is.null(cd8.em.tfppi.up)){
cd8.em.tfppi.up$Dir <- "Up"
cd8.em.tfppi.down <- cd8.em.enriched.down[["Transcription_Factor_PPIs"]]
cd8.em.tfppi.down$Dir <- "Down"
cd8.em.tfppi <- do.call(rbind.data.frame, list(cd8.em.tfppi.up, cd8.em.tfppi.down))
} else{
cd8.em.tfppi.down <- cd8.em.enriched.down[["Transcription_Factor_PPIs"]]
cd8.em.tfppi.down$Dir <- "Down"
cd8.em.tfppi <- do.call(rbind.data.frame, list(cd8.em.tfppi.down))
}
cd8.em.tfppi$logP <- -log10(cd8.em.tfppi$P.value)
cd8.em.tfppi$logP[cd8.em.tfppi$Dir %in% c("Down")] <- -1 * cd8.em.tfppi$logP[cd8.em.tfppi$Dir %in% c("Down")]
ggplot(cd8.em.tfppi[cd8.em.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD8.TE
```{r, fig.height=3.95, fig.width=4.95}
cd8.te.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.TE"), ]$X
cd8.te.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.TE"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.TE"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=cd8.te.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD8.TE-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
cd8.te.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.TE") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
cd8.te.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD8.TE") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
cd8.te.msigdb.up <- cd8.te.enriched.up[["MSigDB_Hallmark_2020"]]
cd8.te.msigdb.up$Dir <- "Up"
cd8.te.msigdb.down <- cd8.te.enriched.down[["MSigDB_Hallmark_2020"]]
if(nrow(cd8.te.msigdb.down) > 0){
cd8.te.msigdb.down$Dir <- "Down"
}
cd8.te.msigdb <- do.call(rbind.data.frame, list(cd8.te.msigdb.up, cd8.te.msigdb.down))
cd8.te.msigdb$logP <- -log10(cd8.te.msigdb$P.value)
cd8.te.msigdb$logP[cd8.te.msigdb$Dir %in% c("Down")] <- -1 * cd8.te.msigdb$logP[cd8.te.msigdb$Dir %in% c("Down")]
ggplot(cd8.te.msigdb[cd8.te.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
cd8.te.tfppi.up <- cd8.te.enriched.up[["Transcription_Factor_PPIs"]]
if(nrow(cd8.te.tfppi.up) > 0){
cd8.te.tfppi.up$Dir <- "Up"
}
cd8.te.tfppi.down <- cd8.te.enriched.down[["Transcription_Factor_PPIs"]]
if(nrow(cd8.te.tfppi.down) > 0){
cd8.te.tfppi.down$Dir <- "Down"
}
cd8.te.tfppi <- do.call(rbind.data.frame, list(cd8.te.tfppi.up, cd8.te.tfppi.down))
cd8.te.tfppi$logP <- -log10(cd8.te.tfppi$P.value)
cd8.te.tfppi$logP[cd8.te.tfppi$Dir %in% c("Down")] <- -1 * cd8.te.tfppi$logP[cd8.te.tfppi$Dir %in% c("Down")]
ggplot(cd8.te.tfppi[cd8.te.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## CD4.Tfh
```{r, fig.height=3.95, fig.width=4.95}
tfh.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Tfh"), ]$X
tfh.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Tfh"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Tfh"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=tfh.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/CD4.Tfh-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
tfh.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Tfh") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
tfh.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("CD4.Tfh") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
tfh.msigdb.up <- tfh.enriched.up[["MSigDB_Hallmark_2020"]]
if(!is.null(tfh.msigdb.up)){
tfh.msigdb.up$Dir <- "Up"
}
tfh.msigdb.down <- tfh.enriched.down[["MSigDB_Hallmark_2020"]]
if(!is.null(tfh.msigdb.down)){
tfh.msigdb.down$Dir <- "Down"
}
if(!is.null(tfh.msigdb.down) & !is.null(tfh.msigdb.up)){
tfh.msigdb <- do.call(rbind.data.frame, list(tfh.msigdb.up, tfh.msigdb.down))
if(nrow(tfh.msigdb) > 0){
tfh.msigdb$logP <- -log10(tfh.msigdb$P.value)
tfh.msigdb$logP[tfh.msigdb$Dir %in% c("Down")] <- -1 * tfh.msigdb$logP[tfh.msigdb$Dir %in% c("Down")]
ggplot(tfh.msigdb[tfh.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
}
}
```
```{r, fig.height=4.15, fig.width=4.15}
tfh.tfppi.up <- tfh.enriched.up[["Transcription_Factor_PPIs"]]
if(!is.null(tfh.tfppi.up)){
tfh.tfppi.up$Dir <- "Up"
}
tfh.tfppi.down <- tfh.enriched.down[["Transcription_Factor_PPIs"]]
if(!is.null(tfh.tfppi.down)){
tfh.tfppi.down$Dir <- "Down"
}
tfh.tfppi <- do.call(rbind.data.frame, list(tfh.tfppi.up, tfh.tfppi.down))
if(nrow(tfh.tfppi) > 0){
tfh.tfppi$logP <- -log10(tfh.tfppi$P.value)
tfh.tfppi$logP[tfh.tfppi$Dir %in% c("Down")] <- -1 * tfh.tfppi$logP[tfh.tfppi$Dir %in% c("Down")]
ggplot(tfh.tfppi[tfh.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
}
```
## gdT
```{r, fig.height=3.95, fig.width=4.95}
gdt.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("gdT"), ]$X
gdt.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("gdT"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("gdT"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=gdt.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/gdT-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
gdt.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("gdT") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
gdt.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("gdT") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
gdt.msigdb.up <- gdt.enriched.up[["MSigDB_Hallmark_2020"]]
gdt.msigdb.up$Dir <- "Up"
gdt.msigdb.down <- gdt.enriched.down[["MSigDB_Hallmark_2020"]]
gdt.msigdb.down$Dir <- "Down"
gdt.msigdb <- do.call(rbind.data.frame, list(gdt.msigdb.up, gdt.msigdb.down))
gdt.msigdb$logP <- -log10(gdt.msigdb$P.value)
gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")] <- -1 * gdt.msigdb$logP[gdt.msigdb$Dir %in% c("Down")]
ggplot(gdt.msigdb[gdt.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
gdt.tfppi.up <- gdt.enriched.up[["Transcription_Factor_PPIs"]]
gdt.tfppi.up$Dir <- "Up"
gdt.tfppi.down <- gdt.enriched.down[["Transcription_Factor_PPIs"]]
gdt.tfppi.down$Dir <- "Down"
gdt.tfppi <- do.call(rbind.data.frame, list(gdt.tfppi.up, gdt.tfppi.down))
gdt.tfppi$logP <- -log10(gdt.tfppi$P.value)
gdt.tfppi$logP[gdt.tfppi$Dir %in% c("Down")] <- -1 * gdt.tfppi$logP[gdt.tfppi$Dir %in% c("Down")]
ggplot(gdt.tfppi[gdt.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## MAIT
```{r, fig.height=3.95, fig.width=4.95}
mait.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("MAIT"), ]$X
mait.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("MAIT"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("MAIT"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=mait.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/MAIT-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
mait.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("MAIT") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
mait.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("MAIT") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
mait.msigdb.up <- mait.enriched.up[["MSigDB_Hallmark_2020"]]
mait.msigdb.up$Dir <- "Up"
mait.msigdb.down <- mait.enriched.down[["MSigDB_Hallmark_2020"]]
mait.msigdb.down$Dir <- "Down"
mait.msigdb <- do.call(rbind.data.frame, list(mait.msigdb.up, mait.msigdb.down))
mait.msigdb$logP <- -log10(mait.msigdb$P.value)
mait.msigdb$logP[mait.msigdb$Dir %in% c("Down")] <- -1 * mait.msigdb$logP[mait.msigdb$Dir %in% c("Down")]
ggplot(mait.msigdb[mait.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
mait.tfppi.up <- mait.enriched.up[["Transcription_Factor_PPIs"]]
mait.tfppi.up$Dir <- "Up"
mait.tfppi.down <- mait.enriched.down[["Transcription_Factor_PPIs"]]
mait.tfppi.down$Dir <- "Down"
mait.tfppi <- do.call(rbind.data.frame, list(mait.tfppi.up, mait.tfppi.down))
mait.tfppi$logP <- -log10(mait.tfppi$P.value)
mait.tfppi$logP[mait.tfppi$Dir %in% c("Down")] <- -1 * mait.tfppi$logP[mait.tfppi$Dir %in% c("Down")]
ggplot(mait.tfppi[mait.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
## Treg
```{r, fig.height=3.95, fig.width=4.95}
treg.labels <- quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("Treg"), ]$X
treg.labels[quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("Treg"), ]$FDR > 1e-3] <- ""
ggplot(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("Treg"), ],
aes(x=logFC, y=-log10(FDR))) +
geom_point() +
theme_cowplot() +
geom_text_repel(aes(label=treg.labels)) +
ggsave("~/Dropbox/COVID19/Updated_plots/Treg-quadratic_volcano.pdf",
height=3.95, width=4.95, useDingbats=FALSE) +
NULL
```
What are the enriched pathways amongst these genes?
```{r}
treg.enriched.up <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("Treg") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC > 0, ]$X,
enrich.dbs)
treg.enriched.down <- enrichr(quadratic.dge.df[quadratic.dge.df$Sub.Annotation %in% c("Treg") &
quadratic.dge.df$FDR < 0.01 &
quadratic.dge.df$logFC < 0, ]$X,
enrich.dbs)
```
## MSigDB 2020 enrichments
```{r, fig.height=4.15, fig.width=4.15}
treg.msigdb.up <- treg.enriched.up[["MSigDB_Hallmark_2020"]]
treg.msigdb.up$Dir <- "Up"
treg.msigdb.down <- treg.enriched.down[["MSigDB_Hallmark_2020"]]
treg.msigdb.down$Dir <- "Down"
treg.msigdb <- do.call(rbind.data.frame, list(treg.msigdb.up, treg.msigdb.down))
treg.msigdb$logP <- -log10(treg.msigdb$P.value)
treg.msigdb$logP[treg.msigdb$Dir %in% c("Down")] <- -1 * treg.msigdb$logP[treg.msigdb$Dir %in% c("Down")]
ggplot(treg.msigdb[treg.msigdb$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
```{r, fig.height=4.15, fig.width=4.15}
treg.tfppi.up <- treg.enriched.up[["Transcription_Factor_PPIs"]]
treg.tfppi.up$Dir <- "Up"
treg.tfppi.down <- treg.enriched.down[["Transcription_Factor_PPIs"]]
treg.tfppi.down$Dir <- "Down"
treg.tfppi <- do.call(rbind.data.frame, list(treg.tfppi.up, treg.tfppi.down))
treg.tfppi$logP <- -log10(treg.tfppi$P.value)
treg.tfppi$logP[treg.tfppi$Dir %in% c("Down")] <- -1 * treg.tfppi$logP[treg.tfppi$Dir %in% c("Down")]
ggplot(treg.tfppi[treg.tfppi$P.value < 0.01, ],
aes(x=reorder(Term, logP), y=logP)) +
geom_hline(yintercept=0, lty=2, colour='grey80') +
geom_point() +
coord_flip() +
labs(x="Pathway", y="Odds Rato") +
theme_cowplot()
```
Datasets
Models
