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Question: Plotting gene expression values from microarray data
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gravatar for Natasha
2.4 years ago by
Natasha • 40
Natasha • 40 wrote:

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)
microarray ggplot2 bioconductor R gene expression • 1.4k views
ADD COMMENT • link •
modified 2.4 years ago by Devon Ryan ♦ 98k • written 2.4 years ago by Natasha • 40
1
gravatar for Devon Ryan
2.4 years ago by
Devon Ryan ♦ 98k
Freiburg, Germany
Devon Ryan ♦ 98k wrote:

Please have a read through a ggplot2 tutorial. ggplot(eset[,1]) just sets things up for plotting, it won't actually plot anything itself.
ADD COMMENT • link written 2.4 years ago by Devon Ryan ♦ 98k

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.https://image.ibb.co/bVijN9/im.png

Is it appropriate to use geom_density?

ADD REPLY • link written 2.4 years ago by Natasha • 40

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.

ADD REPLY • link written 2.4 years ago by Devon Ryan ♦ 98k
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