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stargeo's Introduction

Differential expression signatures for disease using STARGEO

DOI: 10.5281/zenodo.46866.svg (corresponds to v1.0)

STARGEO is a webapp which allows users to identify differentially expressed genes between samples of their choosing. Users annotate studies in GEO to indicate which samples belong to which conditions. We've annotated many samples for their membership to specific disease or control classes. Then for a specific query (case versus control specification), STARGEO meta-analyzes across all the studies with relevant samples.

Here, we perform STARGEO analyses for diseases in our drug repurposing hetnet. See the Thinklab discussion for more information.

Execution

This repository depends on the starapi package. See environment.yml for the other installed packages in the environment.

The notebooks are executed in the following order:

  1. retrieve-tags.ipynb retrieves the current tags from the STARGEO database. The connection details are stored in dsn.txt (private).
  2. prepare_queries.ipynb prepares the STARGEO queries based off of manual Disease Ontology to STARGEO tag mappings (data/DO-tag-mapping.tsv). The queries specifics are stored in data/queries.tsv.
  3. querier.ipynb performs the STARGEO analyses. The output for each disease is stored in data/doslim.
  4. combine.ipynb aggregates the differential expression results for all diseases. data/diffex.tsv contains the significantly differential expressions. data/summary.tsv shows the number of up and down-regulated genes per disease.

License

All original content in this repository is released under CC0 1.0.

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stargeo's Issues

Meta-analyses to combine significant genes

I was looking into your code at https://github.com/dhimmel/stargeo/blob/master/combine.ipynb.

Could you tell me where the file 'balanced_permutation.tsv.gz' comes from /is downloaded from?

And just to make sure, this code uses only the p-values in the "multipletests" method, right?

So, it first gets all the significant genes from each independent study, then creates corrected p-values and after selects the genes that have "up" direction through the fold-change? I'm not sure how the process goes. I would really appreciate if you could help me, please.

And I read in this paper https://doi.org/10.1093/bib/bbaa019, that random effects models are used when the gene is significant in all studies. So it is not used for when it's not in some of the studies, right?

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