Question: Absence of significant values in the analyzes with GEO2R

0

RafaelMP •

**110**wrote:Hi!

I intend to use different array experiments, stored in GEO, to conduct a reanalysis. As a starting point, I used the GEO2R code, but I noticed that countless sets do not show significant adj-pvalue in healthy vs. sick comparisons (for example, GSE20163). I don't know if there are no differences between the groups or if the code does is lacking something. I saw that many people suspect the results of GEO2R, but I have not found a clear answer on the subject. Other datasets, using the same methodology, resulted in significant values (for example, GSE7753). I appreciate the suggestions.

Thank you!

```
#Version info: R 3.2.3, Biobase 2.30.0, GEOquery 2.40.0, limma 3.26.8
################################################################
#Differential expression analysis with limma
library(GEOquery)
library(limma)
library(umap)
# load series and platform data from GEO
gset <- getGEO("GSE20163", GSEMatrix =TRUE, AnnotGPL=TRUE)
if (length(gset) > 1) idx <- grep("GPL96", 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 <- "00000010111001111"
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) }
#assign samples to groups and set up design matrix
gs <- factor(sml)
groups <- make.names(c("Normal","Disease"))
levels(gs) <- groups
gset$group <- gs
design <- model.matrix(~group + 0, gset)
colnames(design) <- levels(gs)
fit <- lmFit(gset, design) # fit linear model
#set up contrasts of interest and recalculate model coefficients
cts <- paste(groups[1], groups[2], sep="-")
cont.matrix <- makeContrasts(contrasts=cts, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
#compute statistics and table of top significant genes
fit2 <- eBayes(fit2, 0.01)#top 250
tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
write.table(tT, file=stdout(), row.names=F, sep="\t")
#Visualize and quality control test results.
#Build histogram of P-values for all genes. Normal test
#assumption is that most genes are not differentially expressed.
tT2 <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
hist(tT2$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)
#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$df.total[t.good], main="Moderated t statistic")
#volcano plot (log P-value vs log fold change)
colnames(fit2) # list contrast names
ct <- 1 # choose contrast of interest
volcanoplot(fit2, coef=ct, main=colnames(fit2)[ct], pch=20,
highlight=length(which(dT[,ct]!=0)), names=rep('+', nrow(fit2)))
#MD plot (log fold change vs mean log expression)
#highlight statistically significant (p-adj < 0.05) probes
plotMD(fit2, column=ct, status=dT[,ct], legend=F, pch=20, cex=1)
abline(h=0)
################################################################
# General expression data analysis
ex <- exprs(gset)
# box-and-whisker plot
ord <- order(gs) # order samples by group
palette(c("#1B9E77", "#7570B3", "#E7298A", "#E6AB02", "#D95F02",
"#66A61E", "#A6761D", "#B32424", "#B324B3", "#666666"))
par(mar=c(7,4,2,1))
title <- paste ("GSE20163", "/", annotation(gset), sep ="")
boxplot(ex[,ord], boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=gs[ord])
legend("topleft", groups, fill=palette(), bty="n")
#expression value distribution
par(mar=c(4,4,2,1))
title <- paste ("GSE20163", "/", annotation(gset), " value distribution", sep ="")
plotDensities(ex, group=gs, main=title, legend ="topright")
#UMAP plot (dimensionality reduction)
ex <- na.omit(ex) # eliminate rows with NAs
ex <- ex[!duplicated(ex), ] # remove duplicates
ump <- umap(t(ex), n_neighbors = 7, random_state = 123)
par(mar=c(3,3,2,6), xpd=TRUE)
plot(ump$layout, main="UMAP plot, nbrs=7", 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
pointLabel(ump$layout, labels = rownames(ump$layout), method="SANN", cex=0.6)
#mean-variance trend, helps to see if precision weights are needed
plotSA(fit2, main="Mean variance trend, GSE20163")
```