https://github.com/databio/renin_atac
https://github.com/databio/renin_atac
Last synced: 6 days ago
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- Host: GitHub
- URL: https://github.com/databio/renin_atac
- Owner: databio
- Created: 2025-09-16T00:10:48.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2026-01-28T19:16:33.000Z (5 months ago)
- Last Synced: 2026-03-01T17:11:21.696Z (4 months ago)
- Language: Jupyter Notebook
- Size: 4.66 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Renin cell identity
Here we include a step-by-step breakdown of the analyses to reproduce the data presented in "The reninness score: integrative analysis of multi-omic data to define renin cell identity".
The goal is to identify a unique epigenetic landscape that defines renin cell identity; and develop a computational tool to use that unique epigenetic landscape to identify renin-expressing cells, and quantify the renin program of unknown cell samples.
## Overview of the experimental design
## Read preprocessing
We used the PEPATAC pipeline to process the raw ATAC-seq reads, including alignment, peak-calling, and quality control. The input files to run PEPATAC for this study are stored in the [metadata](metadata) sub-folder. For more information on how to use PEPATAC, see: http://pepatac.databio.org/
## Consensus peak set generation
We used the genomic interval machine learning (geniml) Python package to construct a consensus region set, or the “universe”, using maximum likelihood approach. For more information on how to use `genimal`, see: https://docs.bedbase.org/geniml/tutorials/create-consensus-peaks/
## Differential accessibility analysis and differential accessibile region annotation
All code used for differential accessibility analysis and differential accessibile region annotation are stored in the [src](src) sub-folder.
## Reninness score calculation and model performace evaluation
All code used for model training and reninness score calulation can be found [here](https://github.com/databio/reninness_score/tree/master)). All code used for model performace evaluation are stored in the [src](src) sub-folder.