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MvGM_atb.Rmd
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MvGM_atb.Rmd
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---
title: "Human Macrophages: M-CSF v GM-CSF: LPS, LPS+Adenosine, LPS+PGE2"
author: "Kajal Hamidzadeh and atb"
date: "`r Sys.Date()`"
output:
html_document:
code_download: true
code_folding: show
fig_caption: true
fig_height: 7
fig_width: 7
highlight: tango
keep_md: false
mode: selfcontained
number_sections: true
self_contained: true
theme: readable
toc: true
toc_float:
collapsed: false
smooth_scroll: false
rmdformats::readthedown:
code_download: true
code_folding: show
df_print: paged
fig_caption: true
fig_height: 7
fig_width: 7
highlight: tango
width: 300
keep_md: false
mode: selfcontained
toc_float: true
BiocStyle::html_document:
code_download: true
code_folding: show
fig_caption: true
fig_height: 7
fig_width: 7
highlight: tango
keep_md: false
mode: selfcontained
toc_float: true
---
<style type="text/css">
body, td {
font-size: 16px;
}
code.r{
font-size: 16px;
}
pre {
font-size: 16px
}
</style>
```{r options, include=FALSE}
library("hpgltools")
tt <- devtools::load_all("/data/hpgltools")
knitr::opts_knit$set(width=120,
progress=TRUE,
verbose=TRUE,
echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
dpi=96)
old_options <- options(digits=4,
stringsAsFactors=FALSE,
knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
rundate <- format(Sys.Date(), format="%Y%m%d")
previous_file <- ""
ver <- "20200330"
## tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
## rmd_file <- "03_expression_infection_20180822.Rmd"
```
# A version of Kajal's R markdown document with some more minor changes
## Libraries
```{r libraries, warning=FALSE, message=FALSE, include=FALSE}
library(devtools)
library(tximport)
library(biomaRt)
library(hpgltools)
library(DESeq2)
library(gplots)
library(ggplot2)
library(cbcbSEQ)
library(RColorBrewer)
library(Vennerable)
library(edgeR)
library(calibrate)
library(scales)
```
In this version of Kajal's document, I will repeat each step with some changes
to see if I can make things a little easier for future modification.
I will therefore leave Kajal's work unchanged and follow each block with an alternative.
## Set working directory, import metadata and abundance files
```{r read_data}
design <- read.table("sample_sheets/MetaData only 4 hour.txt", header=TRUE, sep="\t")
design[["Patient"]] <- as.factor(design[["Patient"]])
design[["Stimulation"]] <- as.factor(design[["Stimulation"]])
design[["Batch"]] <- as.factor(design[["Batch"]])
design[["Growth"]] <- as.factor(design[["Growth"]])
files <- file.path("kallisto abundance files/", design$HPGL.Identifier, "abundance.tsv")
names(files) <- paste0("HPGL09", c(12:31, 42:60))
rownames(design) <- design[[1]]
stim_design_idx <- design[["Stimulation"]] != "NS"
stim_design <- design[stim_design_idx, ]
```
## Convert transcript ID to gene ID
```{r ensembl}
ensembl <- useMart("ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl",
host="useast.ensembl.org")
tx2gene <- getBM(attributes=c("ensembl_transcript_id", "ensembl_gene_id", "chromosome_name"),
mart=ensembl)
good_idx <- grepl(x=tx2gene[["chromosome_name"]], pattern="^[[:alnum:]]{1,2}$")
good_ones <- tx2gene[good_idx, -3]
```
## Create counts table
```{r}
txi.kallisto.tsv <- tximport(files, type="kallisto", tx2gene=good_ones,
countsFromAbundance="lengthScaledTPM")
nrow(txi.kallisto.tsv$counts)
write.table(txi.kallisto.tsv$counts, "TPM_MvGM_CDS_20180711.txt",
col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(txi.kallisto.tsv$counts, "csv/TPM_MvGM_20180711.csv",
row.names=TRUE, quote=FALSE)
stim_counts <- txi.kallisto.tsv$counts
stim_counts_idx <- colnames(stim_counts) %in% rownames(stim_design)
stim_counts <- stim_counts[, stim_counts_idx]
```
This is a potentially important difference in how I and Kajal treated the data.
This is because I created the expressionset without using 'lengthScaledTPM',
but let tximport use the raw counts so that the various normalizations are not
affected. E.g. using these scaled numbers results in less variance in the data
and may lead to some confusion for the downstream tools
(edger/limma/deseq/etc). On the other hand, it may lead to cleaner plots, I am
not sure.
## Create DESeqDataSet
Not sure what this is actually doing
```{r deseq_dds}
df <- data.frame(condition=paste(design$HPGL.Identifier, design$Growth,
design$Stimulation, design$Patient, sep="_"))
rownames(df) <- colnames(txi.kallisto.tsv$counts)
dds <- DESeqDataSetFromTximport(txi.kallisto.tsv, df, ~condition)
nrow(dds)
dds_stim <- dds[, rownames(stim_design)]
```
## My version of the above
```{r atb_read_data}
my_design <- design
rownames(my_design) <- design[[1]]
my_design[["condition"]] <- my_design[["Stimulation"]]
my_design[["file"]] <- glue::glue("preprocessing/{rownames(my_design)}/abundance.tsv")
colnames(my_design) <- tolower(colnames(my_design))
gene_info <- load_biomart_annotations(host="useast.ensembl.org")$annotation
rownames(gene_info) <- make.names(gene_info[["ensembl_gene_id"]], unique=TRUE)
tx_gene_map <- gene_info[, c("ensembl_transcript_id", "ensembl_gene_id")]
hs_expt <- create_expt(metadata=my_design, gene_info=gene_info, tx_gene_map=tx_gene_map)
hs_expt <- set_expt_batches(hs_expt, fact="growth")
stim_expt <- subset_expt(hs_expt, subset="stimulation!='NS'")
```
## Bar Plot of Counts
```{r barplot}
par(mar=c(10, 4.5, 3.5, 1))
par(oma=c(0, 0, 0, 0))
barplot(colSums(txi.kallisto.tsv$counts), las=3, main="Raw Counts By Sample")
barplot(colSums(stim_counts), las=3, main="Raw Counts By Sample")
```
## My Barplot of counts
```{r my_barplot}
libsize <- plot_libsize(hs_expt)
libsize$plot
libsize$summary
plot_libsize(stim_expt)$plot
```
## Box Plot of Counts
```{r count_barplot}
dds <- estimateSizeFactors(dds)
ncts <- counts(dds, normalized=TRUE)
y <- log(ncts + 1)
par(mar=c(5, 5, 2, 1))
par(oma=c(0, 0, 0, 0))
boxplot(y, names=colnames(txi.kallisto.tsv$counts),
las=3, main="Per Sample Log of Size-factor Counts")
dds_stim <- estimateSizeFactors(dds_stim)
stim_ncts <- counts(dds_stim, normalized=TRUE)
stim_y <- log(stim_ncts + 1)
par(mar=c(5, 5, 2, 1))
par(oma=c(0, 0, 0, 0))
boxplot(stim_y, names=colnames(stim_counts), las=3,
main="Per Sample Log of Size-factor Counts")
```
```{r my_boxplot}
boxplot <- plot_boxplot(hs_expt)
boxplot
```
## Heatmap of Pearson Correlation
```{r corheat}
datCor <- cor(txi.kallisto.tsv$counts)
heatmap.2(datCor, Rowv=NA, Colv=NA, margins=c(10, 10),
labRow=df$condition, labCol=df$condition,
dendrogram="none", scale="none", trace="none",
srtCol=45, main="Pearson Correlation")
stim_datCor <- cor(stim_counts)
heatmap.2(stim_datCor, Rowv=NA, Colv=NA, margins=c(10, 10),
labRow=df$condition, labCol=df$condition,
dendrogram="none", scale="none", trace="none",
srtCol=45, main="Pearson Correlation")
```
### Try out ggstatsplot for this
```{r ggtesting}
library(ggstatsplot)
ggtest <- ggcorrmat(as.data.frame(stim_counts), type="robust",
sig.level=0.001, p.adjust.method="holm")
limits <- c(min(ggtest[["data"]][["coef"]]), 1)
ggtest <- ggtest + scale_fill_gradient(
low="black", high="red", limit=limits) +
theme(axis.text.y=element_text(size=6),
legend.position="top")
library(ggdendro)
dendro_mtrx <- dcast(data=ggtest[["data"]], formula=Var1 ~ Var2, value.var="coef")
rownames(dendro_mtrx) <- dendro_mtrx[["Var1"]]
dendro_mtrx[["Var1"]] <- NULL
dendro_mtrx <- as.matrix(dendro_mtrx)
dendro <- as.dendrogram(hclust(d=dist(x=dendro_mtrx)))
dendro_plot <- ggdendrogram(data=dendro, rotate=TRUE)
library(grid)
grid.newpage()
print(ggtest, vp = viewport(x = 0.4, y = 0.5, width = 0.8, height = 1.0))
print(dendro_plot, vp = viewport(x = 0.90, y = 0.445, width = 0.2, height = 1.0))
```
```{r my_corheat}
plot_corheat(hs_expt)$plot
plot_corheat(stim_expt)$plot
```
## Filter and Normalize Counts
```{r}
filterCounts <- function (counts, lib.size=NULL, thresh=1, minSamples=2) {
cpms <- 2 ^ log2CPM(counts, lib.size = lib.size)$y
keep <- rowSums(cpms > thresh) >= minSamples
counts <- counts[keep, ]
counts
}
x_table <- table(design$Stimulation)
dim(txi.kallisto.tsv$counts)
counts <- filterCounts(txi.kallisto.tsv$counts, thresh=1, minSamples=min(x_table))
dim(counts)
countsSubQ <- qNorm(counts)
x <- log2CPM(countsSubQ)
s <- makeSVD(x$y)
stim_table <- table(stim_design$Stimulation)
stim_counts <- filterCounts(stim_counts, thresh=1, minSamples=min(stim_table))
dim(stim_counts)
stim_countsSubQ <- qNorm(stim_counts)
stim_x <- log2CPM(stim_countsSubQ)
stim_s <- makeSVD(stim_x$y)
```
## Median Pairwise Correlation
```{r smc}
corM <- matrixStats::rowMedians(cor(txi.kallisto.tsv$counts))
qs <- quantile(corM, p=c(1, 3) / 4)
iqr <- diff(qs)
outLimit <- qs[1] - 1.5 * iqr
ylim <- c(pmin(min(corM), outLimit), max(corM))
cond <- paste(design$Growth, design$Stimulation, sep="_")
col <- ifelse(cond == "M_NS", "gray60",
ifelse(cond == "M_LPS", "deeppink3",
ifelse(cond == "M_LA", "darkseagreen2",
ifelse(cond == "M_LP", "lavender",
ifelse(cond == "GM_NS", "lightpink1",
ifelse(cond == "GM_LPS", "moccasin",
ifelse(cond == "GM_LA", "black",
ifelse(cond == "GM_LP", "springgreen4", "blue2"))))))))
par(mar=c(5, 4.5, 2, 1))
plot(corM, xaxt="n", ylim=ylim, ylab="Median Pairwise Correlation",
xlab="", main="", col=col, pch=16, cex=1.5)
axis(side=1, at=seq(along=corM), labels=colnames(txi.kallisto.tsv$counts), las=2)
abline(h=outLimit, lty=2)
abline(v=1:ncol(txi.kallisto.tsv$counts), lty=3, col="black")
stim_corM <- matrixStats::rowMedians(cor(stim_counts))
stim_qs <- quantile(stim_corM, p=c(1, 3) / 4)
stim_iqr <- diff(stim_qs)
stim_outLimit <- stim_qs[1] - (1.5 * iqr)
stim_ylim <- c(pmin(min(stim_corM), stim_outLimit), max(stim_corM))
stim_cond <- paste(stim_design$Growth, stim_design$Stimulation, sep="_")
col <- ifelse(cond=="M_NS", "gray60",
ifelse(cond=="M_LPS", "deeppink3",
ifelse(cond=="M_LA", "darkseagreen2",
ifelse(cond=="M_LP", "lavender",
ifelse(cond=="GM_NS", "lightpink1",
ifelse(cond=="GM_LPS", "moccasin",
ifelse(cond=="GM_LA", "black",
ifelse(cond=="GM_LP", "springgreen4", "blue2"))))))))
par(mar=c(5, 4.5, 2, 1))
plot(stim_corM, xaxt="n", ylim=ylim, ylab="Median Pairwise Correlation",
xlab="", main="", col=col, pch=16, cex=1.5)
axis(side=1, at=seq(along=stim_corM), labels=colnames(stim_counts), las=2)
abline(h=stim_outLimit, lty=2)
abline(v=1:ncol(stim_counts), lty=3, col="black")
```
```{r my_smc}
plot_sm(hs_expt)$plot
plot_sm(stim_expt)$plot
```
## PCA
```{r}
cbcbSEQ::pcRes(s$v, s$d, cond, design$Batch)[1:5, ]
cbcbSEQ::pcRes(s$v, s$d, cond, design$Patient)[1:5, ]
cbcbSEQ::pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Batch)[1:5, ]
cbcbSEQ::pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Patient)[1:5, ]
```
## Euclidian Distance Heat Map
```{r disheat}
dists <- dist(t(counts))
mat <- as.matrix(dists)
rownames(mat) <- colnames(mat) <- cond
hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
vec.patient <- rainbow(nlevels(design$Patient), start=0, end=0.8)
patient.color <- rep(0, length(design$Patient))
for (i in 1:length(design$Patient)) {
patient.color[i] <- vec.patient[design$Patient[i] == levels(design$Patient)]
}
vec.condition <- c("green", "lightblue", "pink", "purple")
condition.color <- rep(0, length(design$Stimulation))
for (i in 1:length(design$Stimulation)) {
condition.color[i] <- vec.condition[design$Stimulation[i] == levels(design$Stimulation)]
}
heatmap <- heatmap.2(mat, trace="none", col=rev(hmcol), margin=c(11, 11),
ColSideColors=condition.color,
RowSideColors=patient.color, key=FALSE, srtCol=45)
stim_dists <- dist(t(stim_counts))
stim_mat <- as.matrix(stim_dists)
rownames(stim_mat) <- colnames(stim_mat) <- stim_cond
stim_hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
stim_vec.patient <- rainbow(nlevels(stim_design$Patient), start=0, end=0.8)
stim_patient.color <- rep(0, length(stim_design$Patient))
for (i in 1:length(stim_design$Patient)) {
idx <- stim_design$Patient[i] == levels(stim_design$Patient)
stim_patient.color[i] <- stim_vec.patient[idx]
}
stim_vec.condition <- c("green", "lightblue", "pink", "purple")
stim_condition.color <- rep(0, length(stim_design$Stimulation))
for (i in 1:length(stim_design$Stimulation)) {
idx <- stim_design$Stimulation[i] == levels(stim_design$Stimulation)
stim_condition.color[i] <- stim_vec.condition[idx]
}
heatmap <- heatmap.2(stim_mat, trace="none", col=rev(hmcol), margin=c(11, 11),
ColSideColors=stim_condition.color,
RowSideColors=stim_patient.color, key=FALSE, srtCol=45)
```
```{r my_disheat}
my_disheat <- plot_disheat(hs_expt)
stim_disheat <- plot_disheat(stim_expt)
```
## Plot PC1 v PC2
```{r original_pca}
condnum <- as.numeric(as.factor(design$Stimulation))
condnum <- ifelse(condnum == 4, "green",
ifelse(condnum == 3, "lightblue",
ifelse(condnum == 2, "pink",
ifelse(condnum == 1, "purple", "black"))))
patnum <- as.numeric(as.factor(design$Patient))
patnum <- ifelse(patnum == 4, 21,
ifelse(patnum == 3, 22,
ifelse(patnum == 2, 23,
ifelse(patnum == 1, 24,
ifelse(patnum == 5, 25, 1)))))
cbcbSEQ::plotPC(s$v, s$d, col="black", pch=patnum, bg=condnum)
legend(x=0.2, y=0.2, legend=unique(design$Stimulation), pch=22, col=0,
pt.bg=c("green", "lightblue", "pink", "purple"), pt.cex=1.5, cex=0.5, bty="n")
legend(x=0.2, y=-0.1, legend=unique(design$Patient), pch=unique(patnum), col=0,
pt.bg="gray90", pt.cex=1.5, cex=0.5, bty="n")
stim_condnum <- as.numeric(as.factor(stim_design$Stimulation))
stim_condnum <- ifelse(stim_condnum == 4, "green",
ifelse(stim_condnum == 3, "lightblue",
ifelse(stim_condnum == 2, "pink",
ifelse(stim_condnum == 1, "purple", "black"))))
stim_patnum <- as.numeric(as.factor(stim_design$Patient))
stim_patnum <- ifelse(stim_patnum == 4, 21,
ifelse(stim_patnum == 3, 22,
ifelse(stim_patnum == 2, 23,
ifelse(stim_patnum == 1, 24,
ifelse(stim_patnum == 5, 25, 1)))))
cbcbSEQ::plotPC(stim_s$v, stim_s$d, col="black", pch=stim_patnum, bg=stim_condnum)
legend(x=0.2, y=0.2, legend=unique(stim_design$Stimulation), pch=22, col=0,
pt.bg=c("green","lightblue","pink","purple"), pt.cex=1.5, cex=0.5, bty="n")
legend(x=0.2, y=-0.1, legend=unique(stim_design$Patient), pch=unique(stim_patnum), col=0,
pt.bg="gray90", pt.cex=1.5, cex=0.5, bty="n")
```
```{r my_pca}
hs_norm <- normalize_expt(hs_expt, norm="quant", filter="cbcb", transform="log2")
my_pca <- plot_pca(hs_norm, plot_labels=FALSE, cis=NULL)
my_pca$plot
hsstim_norm <- normalize_expt(stim_expt, norm="quant", filter="cbcb", transform="log2")
stim_pca <- plot_pca(hsstim_norm, plot_labels=FALSE, cis=NULL)
stim_pca$plot
```
## Correct for Patient in Limma model
```{r}
mod <- model.matrix(~design$Patient)
v <- voom(countsSubQ, mod)
fit <- lmFit(v)
newData <- residuals(fit, v)
s <- makeSVD(newData)
pcRes(s$v, s$d, cond, design$Batch)[1:5, ]
pcRes(s$v, s$d, cond, design$Patient)[1:5, ]
stim_table <- table(stim_design$Stimulation)
stim_counts <- filterCounts(stim_counts, thresh=2, minSamples=min(stim_table))
dim(stim_counts)
stim_countsSubQ <- qNorm(stim_counts)
stim_mod <- model.matrix(~stim_design$Patient)
stim_v <- voom(stim_countsSubQ, stim_mod)
stim_fit <- lmFit(stim_v)
stim_newData <- residuals(stim_fit, stim_v)
stim_s <- makeSVD(stim_newData)
pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Batch)[1:5, ]
pcRes(stim_s$v, stim_s$d, stim_cond, stim_design$Patient)[1:5, ]
```
## Plot PC1 and PC2 with patient correction
```{r}
gronum <- as.numeric(as.factor(design$Growth))
gronum <- ifelse(gronum == 2, 19,
ifelse(gronum == 1, 15, 1))
patnum <- as.numeric(as.factor(design$Patient))
patnum <- ifelse(patnum == 1, "pink",
ifelse(patnum == 2, "green",
ifelse(patnum == 3, "blue",
ifelse(patnum == 4, "yellow",
ifelse(patnum == 5, "black", "orange")))))
samplenum <- as.numeric(as.factor(design$Stimulation))
samplenum <- ifelse(samplenum == 4, "green",
ifelse(samplenum == 3, "blue",
ifelse(samplenum == 1, "black",
ifelse(samplenum == 2, "yellow", "grey"))))
plotPC(s$v, s$d, pch=gronum, col=samplenum, cex=2)
legend(x=0.05, y=0.3, legend=c("NS", "LPS", "LP", "LA"), pch=22, col=0,
pt.bg=c("green", "blue", "black", "yellow"), pt.cex=1.5, cex=1, bty="n")
legend(x=0.05, y=0.1, legend=unique(design$Growth), pch=unique(gronum), col="black",
pt.bg="black", pt.cex=1.0, cex=1, bty="n")
stim_gronum <- as.numeric(as.factor(stim_design$Growth))
stim_gronum <- ifelse(stim_gronum == 2, 19,
ifelse(stim_gronum == 1, 15, 1))
stim_patnum <- as.numeric(as.factor(stim_design$Patient))
stim_patnum <- ifelse(stim_patnum == 1, "pink",
ifelse(stim_patnum == 2, "green",
ifelse(stim_patnum == 3, "blue",
ifelse(stim_patnum == 4, "yellow",
ifelse(stim_patnum == 5, "black", "orange")))))
stim_samplenum <- as.numeric(as.factor(stim_design$Stimulation))
stim_samplenum <- ifelse(stim_samplenum == 3, "green",
ifelse(stim_samplenum == 2, "blue",
ifelse(stim_samplenum == 1, "black", "grey")))
plotPC(stim_s$v, stim_s$d, pch=stim_gronum, col=stim_samplenum, cex=2)
legend(x=0.05, y=0.3, legend=c("LPS", "LP", "LA"), pch=22, col=0,
pt.bg=c("green", "blue", "black", "yellow"), pt.cex=1.5, cex=1, bty="n")
legend(x=0.05, y=0.1, legend=unique(stim_design$Growth), pch=unique(stim_gronum),
col="black", pt.bg="black", pt.cex=1.0, cex=1, bty="n")
```
## My Figure 3A
```{r my_pca2}
##stim_counts <- filterCounts(stim_counts, thresh=1, minSamples=min(x))
##dim(stim_counts)
##stim_countsSubQ <- qNorm(stim_counts)
##stim_x <- log2CPM(stim_countsSubQ)
##stim_s <- makeSVD(stim_x$y)
hss <- set_expt_batches(stim_expt, fact="patient")
hss <- sm(normalize_expt(hss, filter=TRUE))
hss <- sm(normalize_expt(hss, norm="quant"))
hss <- sm(normalize_expt(hss, convert="cpm"))
hss <- sm(normalize_expt(hss, transform="log2"))
hss <- sm(normalize_expt(hss, batch="limma"))
hss <- sm(set_expt_batches(hss, fact="growth"))
plot_pca(hss, plot_labels=FALSE)$plot
hss_varpart <- simple_varpart(expt=hss)
hss_varplot <- plot_variance_coefficients(hss)
pp(file="stimulation_coef.png", image=hss_varplot$plot)
hss_varplot <- plot_variance_coefficients(hss, x_axis="batch")
hss_varplot$plot
pp(file="mgm_coef.png", image=hss_varplot$plot)
tmp <- pData(hss)
tmp[["both"]] <- paste0(tmp[["condition"]], "_", tmp[["batch"]])
pData(hss[["expressionset"]]) <- tmp
hss_both_varplot <- plot_variance_coefficients(hss, x_axis="both")
pp(file="both_coef.png", image=hss_both_varplot$plot)
```
## Heatmap with patient correction??
```{r}
dists <- dist(t(newData))
mat <- as.matrix(dists)
rownames(mat) <- colnames(mat) <- cond
heatmap <- heatmap.2(mat, trace="none", col=rev(hmcol), margin=c(11, 11),
ColSideColors=condition.color,
RowSideColors=patient.color, key=FALSE, srtCol=45)
```
## DE analysis
```{r}
countsSubQ <- qNorm(counts)
patient <- design$Patient
mod <- model.matrix(~ 0 + cond + patient, data=design)
v <- voom(countsSubQ, mod, plot=TRUE)
fit <- lmFit(v)
```
## M_LPS v M_NS
eBayes finds an F-statistic from the set of t-statistics for that gene
```{r}
M_LPS.M_NS.contr.mat <- makeContrasts(M_LPSvM_NS=(condM_LPS - condM_NS), levels=v$design)
M_LPS.M_NS.fit <- contrasts.fit(fit, M_LPS.M_NS.contr.mat)
M_LPS.M_NS.eb <- eBayes(M_LPS.M_NS.fit)
M_LPS.M_NS.topTab <- topTable(M_LPS.M_NS.eb, coef="M_LPSvM_NS", number=nrow(v$E))
M_LPS.M_NS.topTab <- cbind(rownames(M_LPS.M_NS.topTab), M_LPS.M_NS.topTab)
colnames(M_LPS.M_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LPS.M_NS.topTab) <- c(1:nrow(M_LPS.M_NS.topTab))
lps_ns_table <- "csv/topTab_M_LPSvM_NS_CDS_limmabatchcorrection_20171120.csv"
write.csv(M_LPS.M_NS.topTab, file=lps_ns_table,
row.names=FALSE, quote=FALSE)
```
Limit list to genes with an adjusted p value < 0.05
```{r}
M_LPS.M_NS.sigGenes <- M_LPS.M_NS.topTab[M_LPS.M_NS.topTab$adj.P.Val < 0.05, ]
length(M_LPS.M_NS.sigGenes$ID)
```
Filter out rows with less than 2-fold change (log2 fold change of > 1)
```{r}
M_LPS.M_NS.sigGenesFold1 <- subset(M_LPS.M_NS.sigGenes, abs(logFC) > 1)
length(M_LPS.M_NS.sigGenesFold1$ID)
```
Filter out rows with less than 4-fold change (log2 fold change of > 2)
```{r}
M_LPS.M_NS.sigGenesFold2 <- subset(M_LPS.M_NS.sigGenes, abs(logFC) > 2)
length(M_LPS.M_NS.sigGenesFold2$ID)
```
Make an MA plot
```{r}
sel <- M_LPS.M_NS.topTab$adj.P.Val < 0.05
top <- M_LPS.M_NS.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E
plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
main="M_LPSvM_NS adjusted", ylab="log FC",
xlab="Average Expression", sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")
```
Annotate sigGenes list using Biomart
```{r}
M_LPS.M_NS.sigGenes <- M_LPS.M_NS.sigGenes[order(-M_LPS.M_NS.sigGenes$logFC), ]
sigGenes <- M_LPS.M_NS.sigGenes
ids <- sigGenes$ID
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
filters="ensembl_gene_id", values=ids, mart=ensembl)
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
desc$Description <- gsub(",", "", desc$Description)
DEG_LPS <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG_LPS <- subset(DEG_LPS,
select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type),
FS="/t")
DEG_LPS <- DEG_LPS[order(-DEG_LPS$logFC), ]
## Filter out Genes -1<FC<1
DEG_LPS <- subset(DEG_LPS, abs(DEG_LPS$logFC)>1)
## Save DE genes
write.table(DEG_LPS, "csv/DEG_M_LPSvM_NS_CDS_limmabatchcorrection_20171120.txt",
col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG_LPS, "csv/DEG_M_LPSvM_NS_CDS_limmabatchcorrection_20171120.csv",
row.names=FALSE, quote=FALSE)
```
## M_LPS v M_LA
```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
M_LPS.M_LA.contr.mat <- makeContrasts(M_LPSvM_LA=((condM_LA - condM_NS) - (condM_LPS - condM_NS)),
levels=v$design)
M_LPS.M_LA.fit <- contrasts.fit(fit, M_LPS.M_LA.contr.mat)
M_LPS.M_LA.eb <- eBayes(M_LPS.M_LA.fit)
M_LPS.M_LA.topTab <- topTable(M_LPS.M_LA.eb, coef="M_LPSvM_LA", number=nrow(v$E))
M_LPS.M_LA.topTab <- cbind(rownames(M_LPS.M_LA.topTab), M_LPS.M_LA.topTab)
colnames(M_LPS.M_LA.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LPS.M_LA.topTab) <- c(1:nrow(M_LPS.M_LA.topTab))
lps_la_table <- "csv/topTab_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.csv"
write.csv(M_LPS.M_LA.topTab, file=lps_la_table, row.names=FALSE, quote=FALSE)
##Limit list to genes with an adjusted p value < 0.05
M_LPS.M_LA.sigGenes <- M_LPS.M_LA.topTab[M_LPS.M_LA.topTab$adj.P.Val < 0.05, ]
length(M_LPS.M_LA.sigGenes$ID)
##[1] 617
##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LPS.M_LA.sigGenesFold1 <- subset(M_LPS.M_LA.sigGenes, abs(logFC) > 1)
length(M_LPS.M_LA.sigGenesFold1$ID)
##[1] 240
##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LPS.M_LA.sigGenesFold2 <- subset(M_LPS.M_LA.sigGenes, abs(logFC) > 2)
length(M_LPS.M_LA.sigGenesFold2$ID)
##[1] 69
##Make an MA plot
sel <- M_LPS.M_LA.topTab$adj.P.Val < 0.05
top <- M_LPS.M_LA.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E
plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
main="M_LPSvM_LA adjusted",
ylab="log FC", xlab="Average Expression",
sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")
dev.copy(pdf, "images/MAplot_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.pdf",
width=8, height=8)
dev.off()
dev.copy(png, "images/MAplot_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.png",
width=700, height=700)
dev.off()
M_LPS.M_LA.sigGenes <- M_LPS.M_LA.sigGenes[order(-M_LPS.M_LA.sigGenes$logFC), ]
##Annotate sigGenes list using Biomart
sigGenes <- M_LPS.M_LA.sigGenes
ids <- sigGenes$ID
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
filters="ensembl_gene_id",
values=ids, mart=ensembl)
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)
DEG <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG <- subset(DEG, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG <- DEG[order(-DEG$logFC), ]
##Filter out Genes -1<FC<1
DEG <- subset(DEG, abs(DEG$logFC) > 1)
##Save DE genes
write.table(DEG, "csv/DEG_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.txt",
col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG, "csv/DEG_M_LPSvM_LA_CDS_limmabatchcorrection_20171120rev.csv",
row.names=FALSE, quote=FALSE)
```
## M_LPS v M_LP
```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
M_LPS.M_LP.contr.mat <- makeContrasts(
M_LPSvM_LP=((condM_LP - condM_NS) - (condM_LPS - condM_NS)),
levels=v$design)
M_LPS.M_LP.fit <- contrasts.fit(fit, M_LPS.M_LP.contr.mat)
M_LPS.M_LP.eb <- eBayes(M_LPS.M_LP.fit)
M_LPS.M_LP.topTab <- topTable(M_LPS.M_LP.eb, coef="M_LPSvM_LP", number=nrow(v$E))
M_LPS.M_LP.topTab <- cbind(rownames(M_LPS.M_LP.topTab), M_LPS.M_LP.topTab)
colnames(M_LPS.M_LP.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LPS.M_LP.topTab) <- c(1:nrow(M_LPS.M_LP.topTab))
lps_lp_table <- "csv/topTab_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.csv"
write.csv(M_LPS.M_LP.topTab, file=lps_lp_table, row.names=FALSE, quote=FALSE)
##Limit list to genes with an adjusted p value < 0.05
M_LPS.M_LP.sigGenes <- M_LPS.M_LP.topTab[M_LPS.M_LP.topTab$adj.P.Val < 0.05, ]
length(M_LPS.M_LP.sigGenes$ID)
##[1] 1473
##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LPS.M_LP.sigGenesFold1 <- subset(M_LPS.M_LP.sigGenes, abs(logFC) > 1)
length(M_LPS.M_LP.sigGenesFold1$ID)
##[1] 467
##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LPS.M_LP.sigGenesFold2 <- subset(M_LPS.M_LP.sigGenes, abs(logFC) > 2)
length(M_LPS.M_LP.sigGenesFold2$ID)
##[1] 125
##Make an MA plot
sel <- M_LPS.M_LP.topTab$adj.P.Val < 0.05
top <- M_LPS.M_LP.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E
plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
main="M_LPSvM_LP adjusted",
ylab="log FC", xlab="Average Expression",
sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")
dev.copy(pdf, "images/MAplot_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.pdf",
width=8, height=8)
dev.off()
dev.copy(png, "images/MAplot_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.png",
width=700, height=700)
dev.off()
M_LPS.M_LP.sigGenes <- M_LPS.M_LP.sigGenes[order(-M_LPS.M_LP.sigGenes$logFC), ]
##Annotate sigGenes list using Biomart
sigGenes <- M_LPS.M_LP.sigGenes
ids <- sigGenes$ID
##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
filters="ensembl_gene_id",
values=ids, mart=ensembl)
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)
DEG <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG <- subset(DEG, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type), FS="/t")
DEG <- DEG[order(-DEG$logFC), ]
##Filter out Genes -1<FC<1
DEG <- subset(DEG, abs(DEG$logFC) > 1)
##Save DE genes
write.table(DEG, "csv/DEG_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.txt",
col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG, "csv/DEG_M_LPSvM_LP_CDS_limmabatchcorrection_20171120rev.csv",
row.names=FALSE, quote=FALSE)
```
## M_LP v M_NS
```{r}
##eBayes finds an F-statistic from the set of t-statistics for that gene
M_LP.M_NS.contr.mat <- makeContrasts(M_LPvM_NS=(condM_LP - condM_NS), levels=v$design)
M_LP.M_NS.fit <- contrasts.fit(fit, M_LP.M_NS.contr.mat)
M_LP.M_NS.eb <- eBayes(M_LP.M_NS.fit)
M_LP.M_NS.topTab <- topTable(M_LP.M_NS.eb, coef="M_LPvM_NS", number=nrow(v$E))
M_LP.M_NS.topTab <- cbind(rownames(M_LP.M_NS.topTab), M_LP.M_NS.topTab)
colnames(M_LP.M_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LP.M_NS.topTab) <- c(1:nrow(M_LP.M_NS.topTab))
lp_ns_table <- "csv/topTab_M_LPvM_NS_CDS_limmabatchcorrection_20171018.csv"
write.csv(M_LP.M_NS.topTab, file=lp_ns_table, row.names=FALSE, quote=FALSE)
##Limit list to genes with an adjusted p value < 0.05
M_LP.M_NS.sigGenes <- M_LP.M_NS.topTab[M_LP.M_NS.topTab$adj.P.Val < 0.05, ]
length(M_LP.M_NS.sigGenes$ID)
##[1] 4386
##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LP.M_NS.sigGenesFold1 <- subset(M_LP.M_NS.sigGenes, abs(logFC) > 1)
length(M_LP.M_NS.sigGenesFold1$ID)
##[1] 1455
##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LP.M_NS.sigGenesFold2 <- subset(M_LP.M_NS.sigGenes, abs(logFC) > 2)
length(M_LP.M_NS.sigGenesFold2$ID)
##[1] 519
##Make an MA plot
sel <- M_LP.M_NS.topTab$adj.P.Val < 0.05
top <- M_LP.M_NS.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E
plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
main="M_LPvM_NS adjusted",
ylab="log FC", xlab="Average Expression",
sub=sub)
points(rowMeans(cpm[top$ID,])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")
dev.copy(pdf, "images/MAplot_M_LPvM_NS_CDS_limmabatchcorrection_20171018.pdf",
width=8, height=8)
dev.off()
dev.copy(png, "images/MAplot_M_LPvM_NS_CDS_limmabatchcorrection_20171018.png",
width=700, height=700)
dev.off()
M_LP.M_NS.sigGenes <- M_LP.M_NS.sigGenes[order(-M_LP.M_NS.sigGenes$logFC), ]
##Annotate sigGenes list using Biomart
sigGenes <- M_LP.M_NS.sigGenes
ids <- sigGenes$ID
##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
filters="ensembl_gene_id",
values=ids, mart=ensembl)
colnames(desc)=c("ID", "Symbol", "Description", "Type")
##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)
DEG_LP4 <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG_LP4 <- subset(DEG_LP4, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type),
FS="/t")
DEG_LP4 <- DEG_LP4[order(-DEG_LP4$logFC), ]
##Filter out Genes -1<FC<1
DEG_LP4 <- subset(DEG_LP4, abs(DEG_LP4$logFC) > 1)
##Save DE genes
write.table(DEG_LP4, "csv/DEG_M_LPvM_NS_CDS_limmabatchcorrection_20171018.txt",
col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG_LP4, "csv/DEG_M_LPvM_NS_CDS_limmabatchcorrection_20171018.csv",
row.names=FALSE, quote=FALSE)
```
## M_LA v M_NS
```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
M_LA.M_NS.contr.mat <- makeContrasts(M_LAvM_NS=(condM_LA - condM_NS), levels=v$design)
M_LA.M_NS.fit <- contrasts.fit(fit, M_LA.M_NS.contr.mat)
M_LA.M_NS.eb <- eBayes(M_LA.M_NS.fit)
M_LA.M_NS.topTab <- topTable(M_LA.M_NS.eb, coef="M_LAvM_NS", number=nrow(v$E))
M_LA.M_NS.topTab <- cbind(rownames(M_LA.M_NS.topTab), M_LA.M_NS.topTab)
colnames(M_LA.M_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(M_LA.M_NS.topTab) <- c(1:nrow(M_LA.M_NS.topTab))
la_ns_table <- "csv/topTab_M_LAvM_NS_CDS_limmabatchcorrection_20171120.csv"
write.csv(M_LA.M_NS.topTab, file=la_ns_table, row.names=FALSE, quote=FALSE)
##Limit list to genes with an adjusted p value < 0.05
M_LA.M_NS.sigGenes <- M_LA.M_NS.topTab[M_LA.M_NS.topTab$adj.P.Val < 0.05, ]
length(M_LA.M_NS.sigGenes$ID)
##[1] 617
##Filter out rows with less than 2-fold change (log2 fold change of > 1)
M_LA.M_NS.sigGenesFold1 <- subset(M_LA.M_NS.sigGenes, abs(logFC) > 1)
length(M_LA.M_NS.sigGenesFold1$ID)
##[1] 240
##Filter out rows with less than 4-fold change (log2 fold change of > 2)
M_LA.M_NS.sigGenesFold2 <- subset(M_LA.M_NS.sigGenes, abs(logFC) > 2)
length(M_LA.M_NS.sigGenesFold2$ID)
##[1] 69
##Make an MA plot
sel <- M_LA.M_NS.topTab$adj.P.Val < 0.05
top <- M_LA.M_NS.topTab
sub <- paste("No. of sig. genes: ", sum(sel), "/", length(sel))
cpm <- v$E
plot(rowMeans(cpm[top$ID,]), top$logFC, pch=16, cex=0.5, col="darkgrey",
main="M_LAvM_NS adjusted",
ylab="log FC", xlab="Average Expression",
sub=sub)
points(rowMeans(cpm[top$ID, ])[sel], top$logFC[sel], col="red", cex=0.5)
abline(h=c(-1, 0, 1), col="red")
dev.copy(pdf, "images/MAplot_M_LAvM_NS_CDS_limmabatchcorrection_20171120.pdf", width=8, height=8)
dev.off()
dev.copy(png, "images/MAplot_M_LAvM_NS_CDS_limmabatchcorrection_20171120.png", width=700, height=700)
dev.off()
M_LA.M_NS.sigGenes <- M_LA.M_NS.sigGenes[order(-M_LA.M_NS.sigGenes$logFC), ]
##Annotate sigGenes list using Biomart
sigGenes <- M_LA.M_NS.sigGenes
ids <- sigGenes$ID
##To see possibilities for attributes, use head(listAttributes(ensembl), n=20L)
desc <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol", "description", "gene_biotype"),
filters="ensembl_gene_id",
values=ids, mart=ensembl)
colnames(desc) <- c("ID", "Symbol", "Description", "Type")
##Remove commas from description
desc$Description <- gsub(",", "", desc$Description)
DEG_LA4 <- merge(sigGenes, desc, by="ID", all=TRUE)
DEG_LA4 <- subset(DEG_LA4, select=c(ID, Symbol, Description, logFC, adj.P.Val, AveExpr, Type),
FS="/t")
DEG_LA4 <- DEG_LA4[order(-DEG_LA4$logFC), ]
##Filter out Genes -1<FC<1
DEG_LA4 <- subset(DEG_LA4, abs(DEG_LA4$logFC) > 1)
##Save DE genes
write.table(DEG_LA4, "csv/DEG_M_LAvM_NS_CDS_limmabatchcorrection_20171120.txt",
col.names=TRUE, row.names=FALSE, quote=FALSE)
write.csv(DEG_LA4, "csv/DEG_M_LAvM_NS_CDS_limmabatchcorrection_20171120.csv",
row.names=FALSE, quote=FALSE)
```
## GM_LPS v. GM_NS
```{r}
#eBayes finds an F-statistic from the set of t-statistics for that gene
GM_LPS.GM_NS.contr.mat <- makeContrasts(GM_LPSvGM_NS=(condGM_LPS-condGM_NS), levels=v$design)
GM_LPS.GM_NS.fit <- contrasts.fit(fit, GM_LPS.GM_NS.contr.mat)
GM_LPS.GM_NS.eb <- eBayes(GM_LPS.GM_NS.fit)
GM_LPS.GM_NS.topTab <- topTable(GM_LPS.GM_NS.eb, coef="GM_LPSvGM_NS", number=nrow(v$E))
GM_LPS.GM_NS.topTab <- cbind(rownames(GM_LPS.GM_NS.topTab), GM_LPS.GM_NS.topTab)
colnames(GM_LPS.GM_NS.topTab) <- c("ID", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B")
rownames(GM_LPS.GM_NS.topTab) <- c(1:nrow(GM_LPS.GM_NS.topTab))
gm_lps_ns_table <- "csv/topTab_GM_LPSvGM_NS_CDS_limmabatchcorrection_20171120.csv"
write.csv(GM_LPS.GM_NS.topTab, file= gm_lps_ns_table, row.names=FALSE, quote=FALSE)
##Limit list to genes with an adjusted p value < 0.05
GM_LPS.GM_NS.sigGenes <- GM_LPS.GM_NS.topTab[GM_LPS.GM_NS.topTab$adj.P.Val < 0.05, ]
length(GM_LPS.GM_NS.sigGenes$ID)