Cycle Enrichr
Enrichment of gene sets with no gene annotations leveraging ARCHS4 and PrismExp gene function prediction.
Usage
Installation
pip3 install cycleenrichr
Download Prediction File
# download precomputed predictions file from PrismExp
import cycleenrichr as cycle
cycle.load.download("predictions.h5")
Run Set Enrichment for Gene Set Library
import cycleenrichr as cycle
# load gene set libary from Enrichr
library = cycle.enrichr.get_library("KEGG_2021_Human")
predictions_path = "predictions.h5"
result = cycle.enrichment.enrich(library, predictions_path)
1 maintainer
Metadata
1 maintainer
Release files for cycleenrichr 0.0.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cycleenrichr-0.0.9.tar.gz | 8.6 kB | Details |
Release files / cycleenrichr-0.0.9.tar.gz
| Download URL | cycleenrichr-0.0.9.tar.gz |
|---|---|
| Size | 8.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e23e30aff172d4ea681e00e489c03ddd67b7125c1cf011b8ff979b9507d0d471
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BLAKE2b-256 checksum How to use checksums |
3d7f07090af8cfaaedfd8cd0068cd7caa81e9e0fbc5a4b781eab132b302c2a96
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No |
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twine/4.0.2 CPython/3.9.6
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