single-cell-multiomics 开源项目推荐（32 个）

OmicsML /awesome-foundation-model-single-cell-papers

该项目在 GitHub 上没有填写描述

gao-lab /GLUE

Graph-linked unified embedding for single-cell multi-omics data integration

pinellolab /dictys

Context specific and dynamic gene regulatory network reconstruction and analysis

NygenAnalytics /scarf

Memory-efficient analysis of single-cell data.

powellgenomicslab /Nebulosa

R package to visualize gene expression data based on weighted kernel density estimation

quadbio /scMultiome_analysis_vignette

Tutorial of single-cell RNA-ATAC multiomic sequencing data analysis

labomics /midas

PyTorch 实现了 MIDAS 算法，用于单细胞多模态数据集成（Nature Biotechnology 2024）。

genecell /COSG

Accurate and fast cell marker gene identification with COSG

pyrovelocity /pyrovelocity

𝒫robabilistic modeling of RNA velocity ⬱

sqjin /scAI

An unsupervised approach for the integrative analysis of single-cell multi-omics data

AI4SCR /MatchCLOT

Python package implementing our method MatchCLOT for multimodal single-cell data integration

LiHongCSBLab /scMagnify

scMagnify is a versatile Python toolkit for inferring and analyzing gene regulatory networks (GRNs) from single-cell multi-omic datasets

jeongdo801 /scNOVA

scNOVA : Single-Cell Nucleosome Occupancy and genetic Variation Analysis

xichensf /magical

R and MATLAB scripts for MAGICAL

grnlab /airqtl

Single-cell eQTL mapping and causal gene regulatory network inference at high efficiency and accuracy

NygenAnalytics /CyteArc

Fast, out-of-core single-cell analysis in Python for RNA, ATAC, protein, and multi-omic data, scaling to 100M+ cells. Mount remote datasets, work within a memory budget, and trace every result through built-in provenance.

chooliu /snmCTseq_Pipeline

Luo Lab snmCT-seq bioinformatics pipeline for joint single-nucleus methylation & transcriptome profiles.

deepomicslab /SCOIT

SCOIT is an implementation of a probabilistic tensor decomposition framework for single-cell multi-omics data integration.

Hollfelder-Lab /crecerelle

Source code, notebooks, and documentation of Crecerelle, a probabilistic deep learning framework for learning cell embeddings from single-cell gene expression and alternative splicing-induced transcript usage. Please read our accompanying paper for details.

cellgeni /nf-atac

Pipeline to process sc-ATAC data with pyCistopic