Plotting gene expression values from microarray data

Question

[Natasha]

I'm trying to plot a distribution graph of the gene expression values,after and before normalization, from microarray data.

Here is my code to obtain a plot of the normalized values,

library(Biobase)
library(GEOquery)
library(magrittr)
library(rJava)
library("xlsx")
library(stringr)
library(ggplot2)
eset <- getGEO('GSE20966')[[1]] 
boxplot(exprs(eset), outline=FALSE)
edata <- data.frame(exprs(eset))
ggplot(eset[,1])

I expected to obtain a plot similar to the distribution plot shown at the end of the page in this tutorial

Unfortunately, I couldn't succeed in doing this. Could someone suggest if there are alternate ways of plotting the logged gene expression values of each sample?(I expect a normally distributed plot)

Answer 1

[Devon Ryan]

Please have a read through a ggplot2 tutorial. ggplot(eset[,1]) just sets things up for plotting, it won't actually plot anything itself.

[Natasha]

I replaced the last line with ggplot(data = edata,aes(x=colnames(edata)[1]))+geom_density(alpha=.2) .I couldn't succeed in obtaining a distribution though.

Is it appropriate to use geom_density?

[Devon Ryan]

You'll want to use something like x=GSMsomething rather than x=colnames(edata)[1]. If there are multiple samples then you'll want to make it a long-form table first and then use something like x=sample, y=value.