Comparing GSEA mutant data to wild type
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25 days ago
conmul • 0

Hi all,

I am working on a project in which we are trying to see how the biology surrounding specific mutant alleles differs. The source data is from DepMap, so I filtered by mutant type and did a t-test to isolate the significant genes in mutant cell lines. I have been using Enrichr to find the scores of the mutant groups and this methodology has been working as intended.

We are wanting to compare these scores to the wild-type to see how much change occurs, if any. My question is how should I go about comparing these data to the wild type? Running GSEA on the wild type cell lines hasn't been effective because I haven't thought of a way to filter significant genes in the WT data, and the p-adj ends up being sky high. If anyone has any recommendations on how to compare these data, it would be much appreciated.

Thanks

python gene GSEA R • 323 views
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What do you mean by "significant gene"? How did you perform GSEA and on how did you rank your genes for it? From guesswork I might initially suggest looking into methods for differential expression (for example DESeq2, limma, or edgeR). But this is assuming you have expression data which is not explicit from your post by any means.

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Sorry that it was unclear in the post. The data is all CRISPR gene effect scores from DepMap. The 'significant genes' as mentioned in the post were found by doing a t-test over the dataset and using those p-values to define what is "significant". I used Enrichr to perform the GSEA and ranked the significant genes as described earlier using the mean CRISPR gene effect score. Hope this clears up any confusion.

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