https://github.com/baderlab/flash-mm

Fast and scalable LMM estimation algorithm for differential expression (DE) analysis of scRNA-seq
https://github.com/baderlab/flash-mm

Last synced: 25 days ago
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Fast and scalable LMM estimation algorithm for differential expression (DE) analysis of scRNA-seq

• Host: GitHub
• URL: https://github.com/baderlab/flash-mm
• Owner: BaderLab
• License: mit
• Created: 2024-11-21T13:35:04.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-21T16:32:09.000Z (about 1 month ago)
• Last Synced: 2024-11-21T17:33:28.319Z (about 1 month ago)
• Language: R
• Size: 97.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# FLASH-MM: Fast and Scalable Single-Cell Differential Expression Analysis Using Linear Mixed-Effects Models

**FLASH-MM** is a fast and scalable algorithm for differential expression (DE) analysis in large-scale single-cell RNA-seq (scRNA-seq) datasets. It addresses challenges such as intra-subject correlation, inter-subject variability, and the computational demands of analyzing millions of cells.

## Key Features

- **Efficient and Scalable**: Precomputes summary statistics to handle large datasets while maintaining single-cell resolution.

- **Accurate DE Analysis**: Controls type-I error rates and maintains high statistical power.

- **Broad Applications**: Supports case-control comparisons, cell-type-specific analyses, and multi-subject studies.

- **Simulation Tool**: Includes `simuRNAseq` for generating realistic scRNA-seq datasets.

## Applications

FLASH-MM has been applied to:

- Case-control comparisons in tuberculosis immune atlases.

- Cell-type-specific sex comparisons in kidney datasets.

With its speed, accuracy, and flexibility, FLASH-MM enables robust DE analysis for large-scale single-cell studies across diverse biological contexts.