Identifying potentially associated gene set to drug response using GSVA
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Kent • 0
@5f50f52d
Last seen 4 hours ago
United Kingdom

Hi all,

I have done some drug response profiling. There are some drugs that are only effective in one or two samples (around 20 screened). I also have some RNA-Seq data from these samples (plus 50 more without screening results) and I calculated the GSVA score for them with rlog output from DESeq2.

I am just wondering if it is a legit way of finding activated pathway for a SINGLE sample by calculating the Z-score of GSVA normalised across samples per gene set and pick those with Z-score higher than 1.96.

I appreciate it if there is a more legit way of doing it and you can let me know. Thanks!

drug RNASeq GSVA • 182 views
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Robert Castelo ★ 3.4k
@rcastelo
Last seen 2 hours ago
Barcelona/Universitat Pompeu Fabra

Hi, if you want to do some formal inference about the effect of a drug at RNA level, then I would recommend you doing a differential expression analysis on the GSVA scores derived from RNA-seq data, between the group of samples that were exposed to the drug and the group of samples that were not exposed to the drug. I would recommend running gsvaParam() and gsva() with default parameters. Please consult the subsection on "Differential expression at pathway level" from the GSVA vignette.

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Hi Robert Castelo thanks for the reply. I should have explained to you the context first. I am testing over a hundred drugs on patient samples (so all of them are unique there is no biological replicate) ex vivo and the RNA-Seq data we have are from the diagnostic samples. There is no post-drug test RNA-Seq data.

I have used your package before for analysing gene set enrichment between groups with biological replicates (e.g. patients with the same subtype of cancer) so I am familiar with the session you referred to. My question is if it is an acceptable way to convert GSVA into z-score across samples so I can find out the outlier of a single sample, which hopefully would be an interesting one (e.g. the only sample that response to drug A). I am not good at statistics so I am just wondering if this z-score transformation is legit for identifying outliers.

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So, if I understand correctly, you derived GSVA scores from gene sets corresponding somehow to drugs in your RNA-seq data. Since GSVA scores are approximately normal across samples, z-scores should be fine to select outliers. However, I would double check the selected candidates with some visual aid, such as a boxplot, and/or you may want to follow a more principled approach to detect outliers, such as one of the tests implemented in the CRAN package outliers, or in the Bioconductor package parody, or going back to your original RNA-seq data and try the Bioconductor package OUTRIDER. I'm not an expert in outlier detection, so you might want to explore a bit more the subject on your own, or consult a local statistician. Others in this forum may also have alternative suggestions.

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