Different DEG results using Ballgown and edgeR
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@iraiamaialen-19272
Last seen 5.8 years ago

Hi everybody! I have been trying to analyze some RNA-seq samples. On one hand I have used Ballgown, with and without adjustvars option, to see the DEG results, as from the PCA plot I can see a strong batch effect between replicates collected on the same days. Then on the other hand, I tryed to use edgeR with TMM method to do the same, with and without adjusting for batch effect. And the thing is that I have really different results. Ballgown always returns a lot of DEGs comparing with edgeR all of them with pval<0.05, but the most surprising thing is that if I compare ballgown and edger results, that there is no common gene name... When I compare edgeR results with and without adujustvar, I have same genes on both results, and some more genes in adjusted option. But with ballgown when I compare edgeR results with an without batcheffect adjust, the results are common in very few of genes.

I know that I haven't write any command, but I have follow pretty well the pipelines described in the protocols and manuals from both sites. So my general question is, are this results typical? I mean, it happens to have different results when comparing different type of normalization, and furthermore, when comparing with and without batch effect adjust?

I'm a little worried about this controversy.

Thanks in advance!

Iraia

edger ballgown rnaseq batch effect adjustvars • 1.8k views
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@gordon-smyth
Last seen 1 hour ago
WEHI, Melbourne, Australia

Over the years, a lot of people have posted questions on this forum asking why two or more well known Bioconductor packages give very different results. Almost always it turns out either that the results are not very different or the user has actually done different analyses in the two packages.

In the case of ballgown and edgeR, the two packages do completely different things. One does isoform-level analyses based on de novo assembly while the other does gene-level analyses based on gene annotation and a reference genome, so one wouldn't expect to be able to compare the results very closely.

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Ok, I understand. Thanks for your clear explanation. I didn't know that this two tools do such different things, I thought they both were to find differentially expressed genes between rna-seq samples. Thanks!

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