https://github.com/gersteinlab/psychencode_singlecell_integrative
Integrative Analyses across 388 human brain samples at single-cell resolution
https://github.com/gersteinlab/psychencode_singlecell_integrative
Last synced: 9 months ago
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Integrative Analyses across 388 human brain samples at single-cell resolution
- Host: GitHub
- URL: https://github.com/gersteinlab/psychencode_singlecell_integrative
- Owner: gersteinlab
- License: other
- Created: 2024-03-20T20:43:11.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-21T17:07:34.000Z (almost 2 years ago)
- Last Synced: 2025-03-26T04:34:35.777Z (9 months ago)
- Language: R
- Homepage:
- Size: 34.8 MB
- Stars: 14
- Watchers: 11
- Forks: 4
- Open Issues: 5
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PsychENCODE Single-cell Integrative Analysis
This repository contains the code generated as part of the PsychENCODE consortium's [Integrative Analysis paper](https://doi.org/10.1101/2024.03.18.585576).
## Overview
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
For a comprehensive understanding, the publication provides detailed insights into our methodologies, results, and discussions. We recommend reading the paper alongside exploring this repository.
## Publication
- **Title**: Single-cell genomics & regulatory networks for 388 human brains
- **Link**: [Biorxiv preprint](https://doi.org/10.1101/2024.03.18.585576)
## Key Highlights
- **Extensive Dataset**: More than 2.8 million nuclei from the prefrontal cortex across 388 individuals.
- **Cell-Type Specific Regulatory Elements**: Identification of over 550K unique regulatory elements.
- **Single-Cell eQTLs Exploration**: Unearthed more than 1.4 million eQTLs offering insights into cell-type regulatory networks and inter-cellular communication.
- **Aging and Neuropsychiatric Disorders**: Comprehensive network models highlighting expression changes associated with aging and various neuropsychiatric disorders.
- **Integrative Model Construction**: A model that accurately imputes cell-type gene expression, prioritizing potential disease-risk genes and drug targets, associating them with specific cell types.