Data analysis step 5: Differential analysis of RNA-seq
So far in this RNA-seq analysis series of posts, we've done a whole bunch of primary analysis on GSE55125 and now we are at the stage where we can now perform a statistical analysis of the count matrix we generated in the last post and look at the genes expression differences caused by Azacitidine.
For this type of analysis we could load our data into R and perform the analysis ourselves, but for a simple experiment design with 2 sample groups in triplicate without batch effects or sample pairing I want to share with you an easy solution. DEB is a online service provided by the Interdisciplinary Center for Biotechnology Research (ICBR) University of Florida that will analyse the count matrix for you with either DESeq, edgeR or baySeq. Their Bioinformation paper is also worth a look.
As with all aspects of bioinformatics, format is critical. You need to follow the specified format exactly. Here is what the head of my count matrix looks like:
geneUNTR1UNTR2UNTR3AZA1AZA2AZA3
ENSG000…
For this type of analysis we could load our data into R and perform the analysis ourselves, but for a simple experiment design with 2 sample groups in triplicate without batch effects or sample pairing I want to share with you an easy solution. DEB is a online service provided by the Interdisciplinary Center for Biotechnology Research (ICBR) University of Florida that will analyse the count matrix for you with either DESeq, edgeR or baySeq. Their Bioinformation paper is also worth a look.
As with all aspects of bioinformatics, format is critical. You need to follow the specified format exactly. Here is what the head of my count matrix looks like:
geneUNTR1UNTR2UNTR3AZA1AZA2AZA3
ENSG000…