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Hello Xihao!
I would suggest two types of information that would be very informative in the summary and annotation of all steps.
For quantitative phenotype, a simple mean/median of the trait for each enriched gene, gene region or variant would be awesome.
For binary phenotypes, the number of samples on each condition for each enriched gene, gene region or variant would be good also.
This is because I got a lot of results from my binary trait analysis, but I have to manually look through my VCF/GDS files to summarize the number of affected indivuals on each trait, to select the most interesting targets. Sometimes, after p-value filtering, filtering for genes/variants/regions that have none or almost no samples in the Control groups is more interesting then only looking at the lowest p value.
Kind Regards
The text was updated successfully, but these errors were encountered:
Thank you for your suggestions. We are happy to work with you and invite you to contribute to STAARpipeline development. It seems like your specific requests fit better into creating some custom summary and annotation functions. If you have them ready, I can invite you to commit to the STAARpipeline-Tutorial repo, similar to @LVMEHINOVIC's commit.
Thanks and let us know how do you want to proceed.
Hello Xihao!
I would suggest two types of information that would be very informative in the summary and annotation of all steps.
For quantitative phenotype, a simple mean/median of the trait for each enriched gene, gene region or variant would be awesome.
For binary phenotypes, the number of samples on each condition for each enriched gene, gene region or variant would be good also.
This is because I got a lot of results from my binary trait analysis, but I have to manually look through my VCF/GDS files to summarize the number of affected indivuals on each trait, to select the most interesting targets. Sometimes, after p-value filtering, filtering for genes/variants/regions that have none or almost no samples in the Control groups is more interesting then only looking at the lowest p value.
Kind Regards
The text was updated successfully, but these errors were encountered: