Clustering scRNAseq by genotypes
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Updated
Apr 30, 2026 - Python
Clustering scRNAseq by genotypes
scGNN (single cell graph neural networks) for single cell clustering and imputation using graph neural networks
Memory-efficient single-cell analysis in Python. Stream RNA, ATAC, CITE-seq and multi-omics from local or remote Zarr stores, from laptop to atlas scale, with reusable fingerprinted results.
Quantifying experimental perturbations at single cell resolution
An unofficial demultiplexing strategy for SPLiT-seq RNA-Seq data
A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies
Single cell type annotation guided by cell atlases, with freedom to be queer
Perturbational analysis by causality-aware generative model for single-cell RNA-sequencing data
Single cell analysis using Low Resource
The simplest repo for scGPT inference, finetuning and training.
Single-cell eQTL mapping and causal gene regulatory network inference at high efficiency and accuracy
PHet: Heterogeneity-Preserving Discriminative Feature Selection for Subtype Discovery
Folded and map strategy and cell perturbation response prediction
orthomap is a python package to extract orthologous maps (in other words the evolutionary age of a given orthologous group) from OrthoFinder/eggNOG results. Orthomap results (gene ages per orthogroup) can be further used to calculate weigthed expression data (transcriptome evolutionary index) from scRNA sequencing objects.
scSPARKL is an Apache spark based pipeline for performing variety of preprocessing and downstream analysis of scRNA-seq data.
Gardener is an agentic system for single-cell RNA-seq analysis. Its intuitive GUI automates complex workflows, sharpens clustering, and supports deeper biological reasoning. You can easily branch, revert, and compare experiments for full analytical control. All data stays strictly on your local device, ensuring complete privacy and security.
PyPairs - A python scRNA-Seq classifier
This repository houses a workflow that uses biological feature trees to segregate cancer RNA-seq datasets, then it trains machine learning models to predict the presence or absence of known, cancer-associated DNA-level mutations.
The integration tool for 15 single cell RNA velocity algorithms
An effective tool for single-cell RNA sequencing.
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