# Version info: R 4.2.2, Biobase 2.58.0, GEOquery 2.66.0, limma 3.54.0
################################################################
# Differential expression analysis with limma
library(GEOquery)
## Loading required package: Biobase
## Loading required package: BiocGenerics
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## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs
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## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
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## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min
## Welcome to Bioconductor
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## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
## Setting options('download.file.method.GEOquery'='auto')
## Setting options('GEOquery.inmemory.gpl'=FALSE)
library(limma)
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## Attaching package: 'limma'
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## plotMA
library(umap)
# load series and platform data from GEO
gset <- getGEO("GSE18388", GSEMatrix =TRUE, AnnotGPL=TRUE)
## Found 1 file(s)
## GSE18388_series_matrix.txt.gz
if (length(gset) > 1) idx <- grep("GPL6246", attr(gset, "names")) else idx <- 1
gset <- gset[[idx]]
# make proper column names to match toptable
fvarLabels(gset) <- make.names(fvarLabels(gset))
# group membership for all samples
gsms <- "00001111"
sml <- strsplit(gsms, split="")[[1]]
# log2 transformation
ex <- exprs(gset)
qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
LogC <- (qx[5] > 100) ||
(qx[6]-qx[1] > 50 && qx[2] > 0)
if (LogC) { ex[which(ex <= 0)] <- NaN
exprs(gset) <- log2(ex) }
exprs(gset) <- normalizeBetweenArrays(exprs(gset)) # normalize data
# assign samples to groups and set up design matrix
gs <- factor(sml)
groups <- make.names(c("flown","control"))
levels(gs) <- groups
gset$group <- gs
design <- model.matrix(~group + 0, gset)
colnames(design) <- levels(gs)
gset <- gset[complete.cases(exprs(gset)), ] # skip missing values
fit <- lmFit(gset, design) # fit linear model
# set up contrasts of interest and recalculate model coefficients
cts <- paste(groups[1], groups[2], sep="-")
cont.matrix <- makeContrasts(contrasts=cts, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
# compute statistics and table of top significant genes
fit2 <- eBayes(fit2, 0.01)
## Warning: Zero sample variances detected, have been offset away from zero
tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
DT::datatable(tT, rownames=F)
# Visualize and quality control test results.
# Build histogram of P-values for all genes. Normal test
# assumption is that most genes are not differentially expressed.
tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
hist(tT2$adj.P.Val, col = "grey", border = "white", xlab = "P-adj",
ylab = "Number of genes", main = "P-adj value distribution")
# summarize test results as "up", "down" or "not expressed"
dT <- decideTests(fit2, adjust.method="fdr", p.value=0.05, lfc=0)
# Venn diagram of results
vennDiagram(dT, circle.col=palette())
# create Q-Q plot for t-statistic
t.good <- which(!is.na(fit2$F)) # filter out bad probes
qqt(fit2$t[t.good], fit2$df.total[t.good], main="Moderated t statistic")
# volcano plot (log P-value vs log fold change)
colnames(fit2) # list contrast names
## [1] "flown-control"
ct <- 1 # choose contrast of interest
# Please note that the code provided to generate graphs serves as a guidance to
# the users. It does not replicate the exact GEO2R web display due to multitude
# of graphical options.
#
# The following will produce basic volcano plot using limma function:
volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
# MD plot (log fold change vs mean log expression)
# highlight statistically significant (p-adj < 0.05) probes
plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
abline(h=0)
################################################################
# General expression data analysis
ex <- exprs(gset)
# box-and-whisker plot
ord <- order(gs) # order samples by group
palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
"#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
par(mar=c(7,4,2,1))
title <- paste ("GSE18388", "/", annotation(gset), sep ="")
boxplot(ex[,ord], boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=gs[ord])
legend("topleft", groups, fill=palette(), bty="n")
# expression value distribution
par(mar=c(4,4,2,1))
title <- paste ("GSE18388", "/", annotation(gset), " value distribution", sep ="")
plotDensities(ex, group=gs, main=title, legend ="topright")
# UMAP plot (dimensionality reduction)
ex <- na.omit(ex) # eliminate rows with NAs
ex <- ex[!duplicated(ex), ] # remove duplicates
ump <- umap(t(ex), n_neighbors = 4, random_state = 123)
par(mar=c(3,3,2,6), xpd=TRUE)
plot(ump$layout, main="UMAP plot, nbrs=4", xlab="", ylab="", col=gs, pch=20, cex=1.5)
legend("topright", inset=c(-0.15,0), legend=levels(gs), pch=20,
col=1:nlevels(gs), title="Group", pt.cex=1.5)
library("maptools") # point labels without overlaps
## Loading required package: sp
## Please note that 'maptools' will be retired during October 2023,
## plan transition at your earliest convenience (see
## https://r-spatial.org/r/2023/05/15/evolution4.html and earlier blogs
## for guidance);some functionality will be moved to 'sp'.
## Checking rgeos availability: FALSE
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## Attaching package: 'maptools'
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## sp2Mondrian
pointLabel(ump$layout, labels = rownames(ump$layout), method="SANN", cex=0.6)
## Warning: Function moved to the car package because maptools is retiring in 2023
# mean-variance trend, helps to see if precision weights are needed
plotSA(fit2, main="Mean variance trend, GSE18388")
