https://github.com/immunogenomics/sc-h2
https://github.com/immunogenomics/sc-h2
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/immunogenomics/sc-h2
- Owner: immunogenomics
- Created: 2023-03-04T22:27:39.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-12T15:56:28.000Z (over 2 years ago)
- Last Synced: 2025-07-07T21:39:00.780Z (11 months ago)
- Language: Jupyter Notebook
- Size: 31.5 MB
- Stars: 5
- Watchers: 11
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Mapping cell state heritability in autoimmunity with single-cell data
Code related to the manuscript "Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability" (Accepted in principle at Nature Genetics).
Preprint now available at https://www.medrxiv.org/content/10.1101/2023.02.24.23286364v1
All scripts required to reproduce the analysis in this paper can also be found at: https://doi.org/10.5281/zenodo.8436010
Contact: Anika Gupta anikagupta@g.harvard.edu
To follow along with the analyses described in the manuscript, we would recommend using the scripts in the following directory order:
1. RNA processing (preparing the counts data for the regressions; also includes a subdirectory with the QC and ATAC processing code)
2. regressions (underlying basis for this work and the key novelty)
3. dynamic_peaks_characterization (plots for Figure 2)
4. stat_gen (preparing the regression outputs for heritability analyses)
5. ldsc (running heritability enrichment analyses)
6. h2_post-hoc (making sense of and plotting the heritability outputs; for h2 plots in Figures 3-6)
7. benchmarking (trying out modified versions of this approach OR the approach as-is but on PBMCs)