Microarray DEG analysis visualisation

Hi,

I have found that my selected gene is differentially expressed between WDLPS and DDLPS tumour tissue samples after performing microarray DEG analysis.

Instead of just a p value in a table format, does anyone know if there is another way I can visualise this data?

Probe I.D "201667_at"
logFC 10.8205874181535
AveExpr 10.6925705768407
t 82.8808890739766
P.Value 3.10189446528995e-88 adj.P Val
3.10189446528995e-88
"B"
191.589248589131

Thankyou

differentially expressed genes microarray

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written 3.9 years ago by bioinformatics •

2 answers

You could make a volcano plot of all the genes in your DEG and highlight the one that you are interested in. Although you will be only using the pvalue and logFC from your data.

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written 3.9 years ago by antmantras •

You probably typed

tab <- topTable(fit, coef=YourCoef)

to get the table. To see the same results displayed in a mean-difference plot with the significant results highlighted, just type

plotMD(fit, coef=YourCoef)

where the coef is the same as the one specified for topTable.

Mean-difference (or MA) plots are recommended in limma because they display the expression level of each gene as well the logFCs and significance.

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written 3.9 years ago by Gordon Smyth

Thanks for your help. I have decided to present the data as a scatter plot/plot MDS (with an error bar) using expression values of the specific gene between the two tumour types (40 vs 52 samples) to show that it is differentially expressed. So 92 dots/points in total.

Do you know how I might do this, if I used these commands for microarray differential expression analysis.

library("arrayQualityMetrics")
library(GEOquery)
library(oligo)
library(Biobase)
library(affy)
library("splitstackshape")
library("tidyr")
library("dplyr")


celFiles <- list.celfiles()
affyRaw <- oligo::rma(affyraw)
eset <- oligo::rma(affyRaw)
library(limma)
pData(eset)
Groups <- c("DDLPS", "DDLPS", "WDLPS", "WDLPS")
design <- model.matrix(~factor(Groups))
colnames(design) <- c("DDLPS", "DDLPSvsWDLPS")
fit <- lmFit(eset, design)
fit <- eBayes(fit)
option (digits =2)
res <- topTable (fit, number = Inf, adjust.method = "none", coef = 1)
write.table(res, "diff_exp.txt", sep= "\t”)
require(hgu133a.db)
annotLookup <- select(hgu133a.db, keys = probes,
  columns = c('PROBEID', 'ENSEMBL', 'SYMBOL'))

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written 3.9 years ago by bioinformatics •