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2_de-analyses_v3.Rmd
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2_de-analyses_v3.Rmd
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---
title: "Section 2: DESeq2 analysis"
date: "`r Sys.Date()`"
author:
- name: Arun Seetharam
- name: Ha Vu
affiliation: Tuteja Lab
affiliation_url: https://www.tutejalab.org
output:
rmdformats::readthedown:
self_contained: true
thumbnails: false
lightbox: true
gallery: true
highlight: tango
---
```{r setup, include=FALSE}
options(max.print = "75")
knitr::opts_chunk$set(
echo = TRUE,
collapse = TRUE,
comment = "#>",
fig.path = "assets/",
fig.width = 8,
prompt = FALSE,
tidy = FALSE,
message = FALSE,
warning = TRUE
)
knitr::opts_knit$set(width = 75)
```
## DESeq2 analyses steps
This section uses the count data (for selected datasets) generated in Section 1 to do differential expression (DE) analyses using `DESeq2`. Briefly, the count data are imported in R, batch corrected using `ComBat_seq`, then DE analyses were performed for various contrasts using `DESeq2`. Results were visualized as volcano plots, and cell enrichment performed using `PlacentaCellEnrich` (`PCE`).
## Prerequisites
R packages required for this section are loaded
```{r, warnings=TRUE, message=FALSE}
setwd("C:/Users/arun/OneDrive - Iowa State University/OrganizedDocuments/github/BAPvsTrophoblast_Amnion")
# load the modules
library(sva)
library(tidyverse)
library(DESeq2)
library(vsn)
library(pheatmap)
library(ggrepel)
library(RColorBrewer)
library(reshape2)
require(biomaRt)
library(EnhancedVolcano)
library(TissueEnrich)
library(plotly)
library(DT)
library(cowplot)
library(biomaRt)
library(tidytext)
library(ggpubr)
library(scales)
```
## Import datasets
The `counts` data and its associated metadata (`coldata`) are imported for analyses.
```{r dataset, warnings=TRUE, message=FALSE}
counts = 'assets/counts-subset-v5.txt'
groupFile = 'assets/batch-subset-v5.txt'
coldata <-
read.csv(
groupFile,
row.names = 1,
sep = "\t",
stringsAsFactors = TRUE
)
cts <- as.matrix(read.csv(counts, sep = "\t", row.names = "gene.ids"))
```
Inspect the `coldata`.
```{r coldata}
DT::datatable(coldata)
```
Reorder columns of `cts` according to `coldata` rows. Check if samples in both files match.
```{r order, warnings=TRUE, message=FALSE}
colnames(cts)
all(rownames(coldata) %in% colnames(cts))
cts <- cts[, rownames(coldata)]
```
## Batch correction
Using ComBat_seq (SVA package) to run batch correction - using bioproject IDs as variable (dataset origin).
```{r batchcorrect, warnings=TRUE, message=FALSE}
cov1 <- as.factor(coldata$BioProject)
adjusted_counts <- ComBat_seq(cts, batch = cov1, group = NULL)
all(rownames(coldata) %in% colnames(cts))
cts <- cts[, rownames(coldata)]
```
## DESeq2
The batch corrected read counts are then used for running DESeq2 analyses
```{r deseq2, warnings=TRUE, message=FALSE}
dds <- DESeqDataSetFromMatrix(countData = adjusted_counts,
colData = coldata,
design = ~ condition)
vsd <- vst(dds, blind = FALSE)
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep, ]
dds <- DESeq(dds)
dds
```
Various contrasts are set up as follows (a total of 8 combinations)
```{r cotnrasts, warnings=TRUE, message=FALSE}
res.PH9vsBAP <-
results(dds,
contrast = c(
"condition",
"Primed_H9_hESCs",
"pBAP_D3.Primed_H9_hESCs"))
res.K00vsK72 <-
results(dds,
contrast = c(
"condition",
"hESC_H9_untr_0h",
"hESC_H9_BMP4_72h"))
res.UNDvsSTB <-
results(dds,
contrast = c(
"condition",
"hESC_H1_D8_MEF.CM.and.FGF2",
"hESC_H1_STB_gt70um_D8_BAP"))
res.K72vsSTB <-
results(dds,
contrast = c(
"condition",
"hESC_H9_BMP4_72h",
"hESC_H1_STB_gt70um_D8_BAP"))
res.BAPvsSTB <-
results(
dds,
contrast = c(
"condition",
"pBAP_D3.Primed_H9_hESCs",
"hESC_H1_STB_gt70um_D8_BAP"))
res.BAPvsK72 <-
results(dds,
contrast = c(
"condition",
"pBAP_D3.Primed_H9_hESCs",
"hESC_H9_BMP4_72h"))
res.K72vsL40 <-
results(dds,
contrast = c(
"condition",
"hESC_H9_BMP4_72h",
"hESC_H1_STB_lt40um_D8_BAP"))
res.BAPvsL40 <-
results(
dds,
contrast = c(
"condition",
"pBAP_D3.Primed_H9_hESCs",
"hESC_H1_STB_lt40um_D8_BAP"
)
)
```
The following function is to save DESeq2 results as well as generate variables to hold the gene lists for running PCE later on.
```{r deseq2funx, warnings=TRUE, message=FALSE}
processDE <- function(res.se, string) {
res.se <- res.se[order(res.se$padj),]
res.data <-
merge(as.data.frame(res.se),
as.data.frame(counts(dds, normalized = TRUE)),
by = "row.names",
sort = FALSE)
names(res.data)[1] <- "Gene"
write_delim(res.data,
file = paste0("DESeq2results-", string, "_fc.tsv"),
delim = "\t")
res.up <-
res.data %>%
filter(log2FoldChange >= 1) %>%
filter(padj <= 0.05) %>%
arrange(desc(log2FoldChange)) %>%
dplyr::select(Gene)
res.dw <-
res.data %>%
filter(log2FoldChange <= -1) %>%
filter(padj <= 0.05) %>%
arrange(desc(log2FoldChange)) %>%
dplyr::select(Gene)
res.up.new <-
annot[annot$ensembl_gene_id_version %in% res.up$Gene, ]
res.dw.new <-
annot[annot$ensembl_gene_id_version %in% res.dw$Gene, ]
pce.up1 <- paste0(string, ".up.pce", 1)
pce.dw1 <- paste0(string, ".dw.pce", 1)
pce.up2 <- paste0(string, ".up.pce", 2)
pce.dw2 <- paste0(string, ".dw.pce", 2)
assign(pce.up1, res.up.new$ensembl_gene_id, envir = .GlobalEnv)
assign(pce.dw1, res.dw.new$ensembl_gene_id, envir = .GlobalEnv)
assign(pce.up2, res.up.new$external_gene_name, envir = .GlobalEnv)
assign(pce.dw2, res.dw.new$external_gene_name, envir = .GlobalEnv)
}
```
## Creating gene lists
The gene lists have Ensembl gene-ID-version. We need them as gene-IDs. We also need other metadata later for these lists.
From Ensembl we will download metadata and attach to these lists.
```{r annotations, warnings=TRUE, message=FALSE}
ensembl = useMart("ENSEMBL_MART_ENSEMBL")
listDatasets(ensembl) %>%
filter(str_detect(description, "Human"))
ensembl = useDataset("hsapiens_gene_ensembl", mart = ensembl)
listFilters(ensembl) %>%
filter(str_detect(name, "ensembl"))
filterType <- "ensembl_gene_id_version"
filterValues <- rownames(cts)
listAttributes(ensembl) %>%
head(20)
attributeNames <- c('ensembl_gene_id_version',
'ensembl_gene_id',
'external_gene_name')
annot <- getBM(
attributes = attributeNames,
filters = filterType,
values = filterValues,
mart = ensembl
)
isDup <- duplicated(annot$ensembl_gene_id)
dup <- annot$ensembl_gene_id[isDup]
annot <- annot[!annot$ensembl_gene_id %in% dup, ] #this object will be saved and used later
```
The results are saved as tsv files.
```{r deseq2save, warnings=TRUE, message=FALSE}
processDE(res.PH9vsBAP, "PH9vsBAP")
processDE(res.K00vsK72, "K00vsK72")
processDE(res.UNDvsSTB, "UNDvsSTB")
processDE(res.K72vsSTB, "K72vsSTB")
processDE(res.BAPvsSTB, "BAPvsSTB")
processDE(res.BAPvsK72, "BAPvsK72")
processDE(res.K72vsL40, "K72vsL40")
processDE(res.BAPvsL40, "BAPvsL40")
```
```{r martdata}
mart <-
read.csv(
"assets/mart-genes.tsv",
sep = "\t",
stringsAsFactors = TRUE,
header = TRUE
) #this object was obtained from Ensembl as we illustrated in "Creating gene lists"
```
```{r volcano}
volcanoPlots <-
function(res.se,
string,
first,
second,
color1,
color2,
color3,
ChartTitle) {
res.se <- res.se[order(res.se$padj), ]
res.se <-
rownames_to_column(as.data.frame(res.se[order(res.se$padj), ]))
names(res.se)[1] <- "Gene"
res.data <-
merge(res.se,
mart,
by.x = "Gene",
by.y = "ensembl_gene_id_version")
res.data <- res.data %>% mutate_all(na_if, "")
res.data <- res.data %>% mutate_all(na_if, " ")
res.data <-
res.data %>% mutate(gene_symbol = coalesce(gene_symbol, Gene))
res.data$diffexpressed <- "other.genes"
res.data$diffexpressed[res.data$log2FoldChange >= 1 &
res.data$padj <= 0.05] <-
paste("Higher expression in", first)
res.data$diffexpressed[res.data$log2FoldChange <= -1 &
res.data$padj <= 0.05] <-
paste("Higher expression in", second)
res.data$delabel <- ""
res.data$delabel[res.data$log2FoldChange >= 1
& res.data$padj <= 0.05
&
!is.na(res.data$padj)] <-
res.data$gene_symbol[res.data$log2FoldChange >= 1
&
res.data$padj <= 0.05
&
!is.na(res.data$padj)]
res.data$delabel[res.data$log2FoldChange <= -1
& res.data$padj <= 0.05
&
!is.na(res.data$padj)] <-
res.data$gene_symbol[res.data$log2FoldChange <= -1
&
res.data$padj <= 0.05
&
!is.na(res.data$padj)]
outpath <- "interactive/"
gg <-
ggplot(res.data,
aes(
x = log2FoldChange,
y = -log10(padj),
col = diffexpressed,
label = delabel
)) +
geom_point(alpha = 0.5) +
xlim(-20, 20) +
theme_classic() +
scale_color_manual(name = "Expression", values = c(color1, color2, color3)) +
ggtitle(ChartTitle) +
xlab(paste("log2 fold change")) +
ylab("-log10 pvalue (adjusted)") +
theme(legend.text.align = 0)
saveWidget(ggplotly(gg), file = paste0(outpath, "/Figure_volcano_", string, ".html"))
}
```
## Volcano Plots (interactive)
Running Volcano plots for each comparison are shown below.
```{r vol_interactive_all, warnings=TRUE, message=FALSE}
volcanoPlots(
res.PH9vsBAP,
"PH9vsBAP",
"pH9_Io",
"H9_pBAP_D3_Io",
"#0571B0",
"#483D8B",
"#4d4d4d",
ChartTitle = "pH9_Io vs. H9_pBAP_D3_Io"
)
volcanoPlots(
res.K00vsK72,
"K00vsK72",
"H9_BMP4.0h_Krendl",
"H9_BMP4.72h_Krendl",
"#FF1493",
"#EE82EE",
"#4d4d4d",
ChartTitle = "H9_BMP4.0h_Krendl vs. H9_BMP4.72h_Krendl"
)
volcanoPlots(
res.UNDvsSTB,
"UNDvsSTB",
"H1_Yabe",
"H1_BAP_D8_>70_Yabe",
"#598234",
"#006400",
"#4d4d4d",
ChartTitle = "H1_Yabe vs. H1_BAP_D8_>70_Yabe"
)
volcanoPlots(
res.K72vsSTB,
"K72vsSTB",
"H9_BMP4.72h_Krendl",
"H1_BAP_D8_>70_Yabe",
"#598234",
"#EE82EE",
"#4d4d4d",
ChartTitle = "H9_BMP4.72h_Krendl vs. H1_BAP_D8_>70_Yabe"
)
volcanoPlots(
res.BAPvsSTB,
"BAPvsSTB",
"H9_pBAP_D3_Io",
"H1_BAP_D8_>70_Yabe",
"#598234",
"#0571B0",
"#4d4d4d",
ChartTitle = "H9_pBAP_D3_Io vs. H1_BAP_D8_>70_Yabe"
)
volcanoPlots(
res.BAPvsK72,
"BAPvsK72",
"H9_pBAP_D3_Io",
"H9_BMP4.72h_Krendl",
"#EE82EE",
"#0571B0",
"#4d4d4d",
ChartTitle = "H9_pBAP_D3_Io vs. H9_BMP4.72h_Krendl"
)
volcanoPlots(
res.K72vsL40,
"K72vsL40",
"H9_BMP4.72h_Krendl",
"H1_BAP_D8_<40_Yabe",
"#AEBD38",
"#EE82EE",
"#4d4d4d",
ChartTitle = "H9_BMP4.72h_Krendl vs. H1_BAP_D8_<40_Yabe"
)
volcanoPlots(
res.BAPvsL40,
"BAPvsL40",
"H9_pBAP_D3_Io",
"H1_BAP_D8_<40_Yabe",
"#AEBD38",
"#0571B0",
"#4d4d4d",
ChartTitle = "H9_pBAP_D3_Io vs. H1_BAP_D8_<40_Yabe"
)
```
### Interactive Volcano Plots:
1. [pH9_Io vs. H9_pBAP_D3_Io](interactive/Figure_volcano_ PH9vsBAP.html){target="_blank"}
2. [H9_BMP4.0h_Krendl vs. H9_BMP4.72h_Krendl](interactive/Figure_volcano_ K00vsK72.html){target="_blank"}
3. [H1_Yabe vs. H1_BAP_D8_>70_Yabe](interactive/Figure_volcano_ UNDvsSTB.html){target="_blank"}
4. [H9_BMP4.72h_Krendl vs. H1_BAP_D8_>70_Yabe](interactive/Figure_volcano_K72vsSTB.html){target="_blank"}
5. [H9_pBAP_D3_Io vs. H1_BAP_D8_>70_Yabe](interactive/Figure_volcano_ BAPvsSTB.html){target="_blank"}
6. [H9_pBAP_D3_Io vs. H9_BMP4.72h_Krendl](interactive/Figure_volcano_ BAPvsK72.html){target="_blank"}
7. [H9_BMP4.72h_Krendl vs. H1_BAP_D8_<40_Yabe](interactive/Figure_volcano_K72vsL40.html){target="_blank"}
8. [H9_pBAP_D3_Io vs. H1_BAP_D8_<40_Yabe](interactive/Figure_volcano_ BAPvsL40.html){target="_blank"}
### Static Volcano Plots:
```{r volcano2}
volcanoPlots2 <-
function(res.se,
string,
first,
second,
color1,
color2,
color3,
ChartTitle) {
res.se <- res.se[order(res.se$padj), ]
res.se <-
rownames_to_column(as.data.frame(res.se[order(res.se$padj), ]))
names(res.se)[1] <- "Gene"
res.data <-
merge(res.se,
mart,
by.x = "Gene",
by.y = "ensembl_gene_id_version")
res.data <- res.data %>% mutate_all(na_if, "")
res.data <- res.data %>% mutate_all(na_if, " ")
res.data <-
res.data %>% mutate(gene_symbol = coalesce(gene_symbol, Gene))
res.data$diffexpressed <- "other.genes"
res.data$diffexpressed[res.data$log2FoldChange >= 1 &
res.data$padj <= 0.05] <-
paste("Higher expression in", first)
res.data$diffexpressed[res.data$log2FoldChange <= -1 &
res.data$padj <= 0.05] <-
paste("Higher expression in", second)
res.data$delabel <- ""
res.data$delabel[res.data$log2FoldChange >= 1
& res.data$padj <= 0.05
&
!is.na(res.data$padj)] <-
res.data$gene_symbol[res.data$log2FoldChange >= 1
&
res.data$padj <= 0.05
&
!is.na(res.data$padj)]
res.data$delabel[res.data$log2FoldChange <= -1
& res.data$padj <= 0.05
&
!is.na(res.data$padj)] <-
res.data$gene_symbol[res.data$log2FoldChange <= -1
&
res.data$padj <= 0.05
&
!is.na(res.data$padj)]
ggplot(res.data,
aes(
x = log2FoldChange,
y = -log10(padj),
col = diffexpressed,
label = delabel
)) +
geom_point(alpha = 0.5) +
xlim(-20, 20) +
theme_classic() +
scale_color_manual(name = "Expression", values = c(color1, color2, color3)) +
geom_text_repel(
data = subset(res.data, padj <= 0.05),
max.overlaps = 15,
show.legend = F,
min.segment.length = Inf,
seed = 42,
box.padding = 0.5
) +
ggtitle(ChartTitle) +
xlab(paste("log2 fold change")) +
ylab("-log10 pvalue (adjusted)") +
theme(legend.text.align = 0)
}
```
```{r vol1, fig.cap="Fig 2.1: pH9_Io vs. H9_pBAP_D3_Io", fig.width=8, fig.height=5}
volcanoPlots2(
res.PH9vsBAP,
"PH9vsBAP",
"pH9_Io",
"H9_pBAP_D3_Io",
"#0571B0",
"#483D8B",
"#4d4d4d",
ChartTitle = "pH9_Io vs. H9_pBAP_D3_Io"
)
```
```{r vol2, fig.cap="Fig 2.2: H9_BMP4.0h_Krendl vs. H9_BMP4.72h_Krendl", fig.width=8, fig.height=5}
volcanoPlots2(
res.K00vsK72,
"K00vsK72",
"H9_BMP4.0h_Krendl",
"H9_BMP4.72h_Krendl",
"#FF1493",
"#EE82EE",
"#4d4d4d",
ChartTitle = "H9_BMP4.0h_Krendl vs. H9_BMP4.72h_Krendl"
)
```
```{r vol3, fig.cap="Fig 2.3: H1_Yabe vs. H1_BAP_D8_>70_Yabe", fig.width=8, fig.height=5}
volcanoPlots2(
res.UNDvsSTB,
"UNDvsSTB",
"H1_Yabe",
"H1_BAP_D8_>70_Yabe",
"#598234",
"#006400",
"#4d4d4d",
ChartTitle = "H1_Yabe vs. H1_BAP_D8_>70_Yabe"
)
```
```{r vol4, fig.cap="Fig 2.4: H9_BMP4.72h_Krendl vs. H1_BAP_D8_>70_Yabe", fig.width=8, fig.height=5}
volcanoPlots2(
res.K72vsSTB,
"K72vsSTB",
"H9_BMP4.72h_Krendl",
"H1_BAP_D8_>70_Yabe",
"#598234",
"#EE82EE",
"#4d4d4d",
ChartTitle = "H9_BMP4.72h_Krendl vs. H1_BAP_D8_>70_Yabe"
)
```
```{r vol5, fig.cap="Fig 2.5: H9_pBAP_D3_Io vs. H1_BAP_D8_>70_Yabe", fig.width=8, fig.height=5}
volcanoPlots2(
res.BAPvsSTB,
"BAPvsSTB",
"H9_pBAP_D3_Io",
"H1_BAP_D8_>70_Yabe",
"#598234",
"#0571B0",
"#4d4d4d",
ChartTitle = "H9_pBAP_D3_Io vs. H1_BAP_D8_>70_Yabe"
)
```
```{r vol6, fig.cap="Fig 2.6: H9_pBAP_D3_Io vs. H9_BMP4.72h_Krendl", fig.width=8, fig.height=5}
volcanoPlots2(
res.BAPvsK72,
"BAPvsK72",
"H9_pBAP_D3_Io",
"H9_BMP4.72h_Krendl",
"#EE82EE",
"#0571B0",
"#4d4d4d",
ChartTitle = "H9_pBAP_D3_Io vs. H9_BMP4.72h_Krendl"
)
```
```{r vol7, fig.cap="Fig 2.7: H9_BMP4.72h_Krendl vs. H1_BAP_D8_<40_Yabe", fig.width=8, fig.height=5}
volcanoPlots2(
res.K72vsL40,
"K72vsL40",
"H9_BMP4.72h_Krendl",
"H1_BAP_D8_<40_Yabe",
"#AEBD38",
"#EE82EE",
"#4d4d4d",
ChartTitle = "H9_BMP4.72h_Krendl vs. H1_BAP_D8_<40_Yabe"
)
```
```{r vol8, fig.cap="Fig 2.8: H9_pBAP_D3_Io vs. H1_BAP_D8_<40_Yabe", fig.width=8, fig.height=5}
volcanoPlots2(
res.BAPvsL40,
"BAPvsL40",
"H9_pBAP_D3_Io",
"H1_BAP_D8_<40_Yabe",
"#AEBD38",
"#0571B0",
"#4d4d4d",
ChartTitle = "H9_pBAP_D3_Io vs. H1_BAP_D8_<40_Yabe"
)
```
## PlacentaCellEnrich (PCE) analyses
The above gene lists are used for running PCE. The function used for running PCE is below.
```{r pceimport}
# Vento-Tormo et al., dataset
input = "C:/Users/arun/OneDrive - Iowa State University/OrganizedDocuments/TutejaLab/PlacentaEnrich/"
l <-
load(file = paste0(input, "combine-test-expression1.Rdata"))
humanGeneMapping <- dataset$GRCH38$humanGeneMapping
d <- dataset$PlacentaDeciduaBloodData
data <- d$expressionData
cellDetails <- d$cellDetails
# Xiang et al., dataset
te.dataset.xiang <- readRDS(paste0(input, "te.dataset.xiang.rds"))
# Castel et al., dataset
te.dataset.castel <- readRDS(paste0(input, "te.dataset.castel.rds"))
# Ro dataset
te.dataset.ro <- readRDS(paste0(input, "te.Rostovskaya.exp0.01.fc1.rds"))
# full names for cell types
xi.md <-
read.csv(
paste0(input, "/md-xi.tsv"),
sep = "\t",
header = TRUE,
row.names = 1
)
vt.md <-
read.csv(
paste0(input, "md-vt.tsv"),
sep = "\t",
header = TRUE,
row.names = 1
)
zp.md <-
read.csv(
paste0(input, "md-zp.tsv"),
sep = "\t",
header = TRUE,
row.names = 1
)
ro.md <-
read.csv(
paste0(input, "md-ro.tsv"),
sep = "\t",
header = TRUE,
row.names = 1
)
```
```{r themeclean}
theme_clean <- function() {
font.1 <- 'sans' #assign font family up front
font.2 <- 'sans'
font.3 <- 'sans'
theme_classic() %+replace% #replace elements we want to change
theme(
#grid elements
panel.grid.major = element_blank(),
#strip major gridlines
panel.grid.minor = element_blank(),
#strip minor gridlines
#panel.grid.major.x = element_blank(),
#panel.grid.minor.x = element_blank(),
#panel.grid.minor.y = element_blank(),
#panel.grid.major.y = element_blank(),
#panel.border = element_blank()
#axis.ticks = element_blank(), #strip axis ticks
#since theme_minimal() already strips axis lines, we don't need to do that again
# Plot Title
plot.title = element_text(
#title
family = font.1,
#set font family
size = 14,
#set font size
face = 'bold',
#bold typeface
hjust = 0,
#left align
vjust = 2
),
#raise slightly
# Plot SubTitle
plot.subtitle = element_text(#subtitle
family = font.1, #font family
size = 12),
#font size
# Caption (bottom -right)
plot.caption = element_text(
#caption
family = font.1,
#font family
size = 12,
#font size
hjust = 1
),
#right align
plot.margin = margin(
t = 1,
r = 1,
b = 1,
l = 2,
unit = "cm"
),
# Axis Title
axis.title = element_text(
family = font.1,
size = 12,
face = 'bold'
),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
axis.text = element_text(family = font.1,
size = 12),
axis.text.x = element_text(#margin for axis text
margin = margin(5, b = 10),
size = 12),
# FacetGrid title
strip.text = element_text(
family = font.1,
face = "bold",
color = "gray35",
hjust = 0,
size = 10
),
# FacetGrid background
strip.background = element_rect(fill = "white",
linetype = "blank"),
# Legend title
legend.title = element_text(
family = font.1,
size = 12,
color = "black",
face = "bold"
),
# Legend box contents
legend.text = element_text(
family = font.1,
size = 12,
color = "black",
face = "plain"
),
#legend.background = element_blank(),
legend.background = element_rect(
color = "black",
fill = "transparent",
size = 2,
linetype = "blank"
),
legend.key = element_blank(),
#legend.position=c(0.05, 0.95),
#legend.position="none",
#legend.justification=c(0,1),
#legend.key = element_blank(),
)
}
```
```{r pcefunction}
run.all.PCE <- function(geneList1, geneList2, filename, ChartTitle, barcolor) {
inputGenes <- unique(toupper(geneList2))
gs1 <- GeneSet(geneIds = inputGenes)
humanGene <-
humanGeneMapping[humanGeneMapping$Gene.name %in% inputGenes,]
inputGenes <- humanGene$Gene
expressionData <-
data[intersect(row.names(data), humanGeneMapping$Gene),]
se <-
SummarizedExperiment(
assays = SimpleList(as.matrix(expressionData)),
rowData = row.names(expressionData),
colData = colnames(expressionData)
)
cellSpecificGenesExp <-
teGeneRetrieval(se, expressedGeneThreshold = 1)
gs <- GeneSet(geneIds = toupper(inputGenes))
output.vt <- teEnrichmentCustom(gs, cellSpecificGenesExp)
en.output.vt <-
setNames(data.frame(assay(output.vt[[1]]), row.names = rowData(output.vt[[1]])[, 1]),
colData(output.vt[[1]])[, 1])
row.names(cellDetails) <- cellDetails$RName
en.output.vt$Tissue <-
cellDetails[row.names(en.output.vt), "CellName"]
en.output.vt$source <- "VT"
en.output.vt <- en.output.vt[order(-en.output.vt$Log10PValue), ]
en.output.vt <-
merge(en.output.vt, vt.md, by = "row.names", all.x = TRUE)
en.output.vt <- rownames_to_column(en.output.vt, var = "Name")
output.xi <- teEnrichmentCustom(gs1, te.dataset.xiang)
en.output.xi <-
setNames(data.frame(assay(output.xi[[1]]), row.names = rowData(output.xi[[1]])[, 1]),
colData(output.xi[[1]])[, 1])
en.output.xi$Tissue <- rownames(en.output.xi)
en.output.xi$source <- "Xi"
en.output.xi <- en.output.xi[order(-en.output.xi$Log10PValue), ]
en.output.xi <-
merge(en.output.xi, xi.md, by = "row.names", all.x = TRUE)
en.output.xi <- rownames_to_column(en.output.xi, var = "Name")
output.zp <- teEnrichmentCustom(gs1, te.dataset.castel)
en.output.zp <-
setNames(data.frame(assay(output.zp[[1]]), row.names = rowData(output.zp[[1]])[, 1]),
colData(output.zp[[1]])[, 1])
en.output.zp$Tissue <- rownames(en.output.zp)
en.output.zp$source <- "ZP"
en.output.zp <- en.output.zp[order(-en.output.zp$Log10PValue), ]
en.output.zp <-
merge(en.output.zp, zp.md, by = "row.names", all.x = TRUE)
en.output.zp <- rownames_to_column(en.output.zp, var = "Name")
output.ro <- teEnrichmentCustom(gs1, te.dataset.ro)
en.output.ro <-
setNames(data.frame(assay(output.ro[[1]]), row.names = rowData(output.ro[[1]])[, 1]),
colData(output.ro[[1]])[, 1])
en.output.ro$Tissue <- rownames(en.output.ro)
en.output.ro$source <- "Ro"
en.output.ro <- en.output.ro[order(-en.output.ro$Log10PValue), ]
en.output.ro <-
merge(en.output.ro, ro.md, by = "row.names", all.x = TRUE)
# colnames(en.output.ro) <- c("Name", "Log10PValue", "Tissue.Specific.Genes", "fold.change", "Tissue", "source", "CellNames")
en.output.ro <- rownames_to_column(en.output.ro, var = "Name")
en.conbined <- rbind(en.output.vt, en.output.xi, en.output.zp, en.output.ro)
# p <- 0.05
# logp <- -log10(p)
en.conbined <- en.conbined %>%
# mutate(Log10PValue = replace(Log10PValue, Log10PValue < logp, 0))
# en.conbined %>%
group_by(source) %>%
arrange(source, desc(Log10PValue)) %>% dplyr::slice(1:7) %>%
ungroup %>%
mutate(
source = as.factor(source),
CellNames = tidytext::reorder_within(CellNames, Log10PValue, source, sep = ":")
)
# write.table(en.conbined,
# file = paste0("cluster_", filename, ".tsv"))
g <-
ggplot(en.conbined, aes(CellNames, Log10PValue)) +
geom_bar(stat = 'identity', fill = barcolor) +
labs(x = "", y = "-log10(adj. p value)") +
theme_clean() +
ggtitle(ChartTitle) +
theme(legend.position = "none",
axis.title.y = element_blank()
) +
scale_y_continuous(expand = expansion(mult = c(0, .1)), breaks = pretty_breaks()) +
facet_wrap( ~ source, scales = "free", ncol = 2) +
coord_flip()
ggsave(paste0("PCE_", filename, ".png"),
g,
dpi = 900,
width = 12,
height = 8
)
return(g)
}
```
The PCE is run on each of the gene lists as follows (up and down pairs are displayed together).
## PCE plots
```{r pce1, fig.cap="Fig 2.9: PCE results for pH9_Io vs. H9_pBAP_D3_Io", fig.width=12, fig.height=12}
a <-
run.all.PCE(
PH9vsBAP.dw.pce1,
PH9vsBAP.dw.pce2,
"PH9vsBAP.down_allPCE.v2",
"Overexpressed in H9_pBAP_D3_Io",
"#0571B0"
)
b <-
run.all.PCE(
PH9vsBAP.up.pce1,
PH9vsBAP.up.pce2,
"PH9vsBAP.up_allPCE.v2",
"Overexpressed in pH9_Io",
"#483F8E"
)
panel_plot <-
plot_grid(a,
b,
labels = c("A", "B"),
ncol = 1,
nrow = 2)
panel_plot
```
```{r pce2, fig.cap="Fig 2.10: PCE results for H9_BMP4.0h_Krendl vs. H9_BMP4.72h_Krendl", fig.width=12, fig.height=12}
a <-
run.all.PCE(
K00vsK72.dw.pce1,
K00vsK72.dw.pce2,
"K00vsK72.down_allPCE.v2",
"Overexpressed in H9_BMP4.72h_Krendl",
"#EE82EE"
)
b <-
run.all.PCE(
K00vsK72.up.pce1,
K00vsK72.up.pce2,
"K00vsK72.up_allPCE.v2",
"Overexpressed in H9_BMP4.0h_Krendl",
"#FF1493"
)
panel_plot <-
plot_grid(a,
b,
labels = c("A", "B"),
ncol = 1,
nrow = 2)
panel_plot
```
```{r pce3, fig.cap="Fig 2.11: PCE results for H1_Yabe vs. H1_BAP_D8_>70_Yabe", fig.width=12, fig.height=12}
a <-
run.all.PCE(
UNDvsSTB.dw.pce1,
UNDvsSTB.dw.pce2,
"UNDvsSTB.down_allPCE.v2",
"Overexpressed in H1_BAP_D8_>70_Yabe",
"#598234"
)
b <-
run.all.PCE(
UNDvsSTB.up.pce1,
UNDvsSTB.up.pce2,
"UNDvsSTB.up_allPCE.v2",
"Overexpressed in H1_Yabe",
"#006400"
)
panel_plot <-
plot_grid(a,
b,
labels = c("A", "B"),
ncol = 1,
nrow = 2)
panel_plot
```
```{r pce4, fig.cap="Fig 2.12: PCE results for H9_BMP4.72h_Krendl vs. H1_BAP_D8_>70_Yabe", fig.width=12, fig.height=12}
a <-
run.all.PCE(
K72vsSTB.dw.pce1,
K72vsSTB.dw.pce2,
"K72vsSTB.down_allPCE.v2",
"Overexpressed in H1_BAP_D8_>70_Yabe",
"#598234"
)
b <-
run.all.PCE(
K72vsSTB.up.pce1,
K72vsSTB.up.pce2,
"K72vsSTB.up_allPCE.v2",
"Overexpressed in H9_BMP4.72h_Krendl",
"#EE82EE"
)
panel_plot <-
plot_grid(a,
b,
labels = c("A", "B"),