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Generate a bar plot in R

Hi, How could I generate a bar plot in R like that I found in GEO2R profile graph to show the expression values of the gene across Samples ( comparing between two cell lines ) ?

My code for example ( I plane to use this data which I loaded it from GEO datasets) :-

library(Biobase)
library(GEOquery)
library(limma)

# load series and platform data from GEO

gset <- getGEO("GSE7700", GSEMatrix =TRUE, AnnotGPL=TRUE)
if (length(gset) > 1) idx <- grep("GPL570", attr(gset, "names")) else idx <- 1
gset <- gset[[idx]]

# make proper column names to match toptable 
fvarLabels(gset) <- make.names(fvarLabels(gset))

# group names for all samples
gsms <- "1111000000"
sml <- c()
for (i in 1:nchar(gsms)) { sml[i] <- substr(gsms,i,i) }

# log2 transform
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) ||
          (qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2)
if (LogC) { ex[which(ex <= 0)] <- NaN
  exprs(gset) <- log2(ex) }

# set up the data and proceed with analysis
sml <- paste("G", sml, sep="")    # set group names
fl <- as.factor(sml)
gset$description <- fl
design <- model.matrix(~ description + 0, gset)
colnames(design) <- levels(fl)
fit <- lmFit(gset, design)
cont.matrix <- makeContrasts(G1-G0, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2, 0.01)
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")

I want to generate a bar plot for each gene individual like the image that I attached here. Profile Graph

r gene
• 4,082 views • link
updated by Alex Reynolds Alex Reynolds • written by libya.tahani • libya.tahani

Please use the formatting bar (especially the code option) to present your post better. I've done it for you this time.
code_formatting

• 0 views • link
written by Ram Ram

1 answer

Starting with a data frame called d.

Make a column called sample that assigns each row/sample to a sample name GSM1234, etc.

Make a column called treatment, which is a categorical factor that assigns each row's sample to normal or cancer.

Make a column called expression, which contains the expression value (log-transformed, etc.) for the row/sample (or for its gene, if that's the correct interpretation of what you're plotting).

#!/usr/bin/env Rscript

library(ggplot2)

# set up `d` per your code
# add `sample`, `treatment`, and `expression` columns per this answer

out_fn <- 'figure.pdf'

pdf(out_fn, onefile=F, width=7, height=5)
p <- ggplot(d, aes(group=sample)) + 
  geom_bar(aes(x=sample, y=expression, fill=treatment), width=0.75, position="dodge2", stat="identity") + 
  facet_grid(~treatment) +
  scale_colour_brewer(palette="Set1") + 
  scale_fill_brewer(palette="Set1") +
  ggtitle('Gene expression vs Sample') +
  xlab('Samples') +
  ylab('Expression') +
  theme(plot.title = element_text(size = 12, hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
print(p)
dev.off()
• 0 views • link
written by Alex Reynolds Alex Reynolds

If you're using Rscript, you're better off using ggsave(p, device='pdf') than pdf(); print(p); dev.off(); IMO.

• 0 views • link
written by Ram Ram

I'm editing the answer to change gene to sample (or cell line), which on a second read seems more appropriate for answering the question.

• 0 views • link
written by Alex Reynolds Alex Reynolds

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