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Question: arrayExpress differential gene express analysis
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14 months ago by

I have a dataset that looks like below:

 Gene ID Gene Name p-value1 log2foldchange1 p-value2 log2foldchange2 p-value3 ACEGIKM00000000001 AABR07013255.1 NA 0 NA 0 NA ACEGIKM00000000007 Gad1 NA 0 NA 0 NA ACEGIKM00000000008 Alx4 NA 0 NA 0 NA ACEGIKM00000000009 Tmco5b NA 0 NA 0 NA ACEGIKM00000000010 Cbln1 NA 0 NA 0 NA ACEGIKM00000000012 Tcf15 NA 0 NA 0 NA ACEGIKM00000000017 Steap1 0.293657 0.2 0.176462 0.2 0.08213 ACEGIKM00000000021 AABR07061902.1 0.058899 -0.3 0.919169 0 0.95051 ACEGIKM00000000024 Hebp1 0.904233 0 0.589132 0.1 0.637529 ACEGIKM00000000033 Tmcc2 NA 0 NA -0.1 NA ACEGIKM00000000034 Nuak2 0.580938 -0.1 0.882088 0 0.800909

I want to Identify differentially expressed genes (for example, using p-value or fold change or both) for each treatment that includes direction of change, then I want to Identify the most important pathways impacted by the treatment. I also want to do visualizations that show the changes in gene expression as a function of treatment.

I also have a counts file, but since I already have this file with fold change and pvalues, I was hoping I could get my answers from this.

Any suggestions are appreciated.

modified 14 months ago by Aaron Lun19k • written 14 months ago by jadepinket0
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14 months ago by
Aaron Lun19k
Cambridge, United Kingdom
Aaron Lun19k wrote:

If the p-values are already BH-adjusted, you can just define DE genes as those with p-values below a desired threshold, e.g., 5%. If not, you'll have to adjust them yourself using p.adjust with method="BH". After you identify DE genes, you can identify enriched pathways/processes using goana or kegga from the limma package. Check whether your organism is supported, though; the IDs aren't familiar to me.

What you mean by "visualizations" is vague. If the log-fold changes describe the effect of treatment, you can just plot them. They look a bit weird, though, usually there's more than 1 decimal point.