| Name | Version | Summary | date |
| omicverse |
1.7.8 |
OmicVerse: A single pipeline for exploring the entire transcriptome universe |
2025-11-03 23:51:44 |
| IRescue |
1.2.0 |
Uncertainty-aware quantification of transposable elements expression in scRNA-seq |
2025-10-30 11:20:33 |
| biobatchnet |
0.1.4 |
A deep learning framework for batch effect correction in biological data |
2025-09-09 20:04:06 |
| scpair |
0.1.0 |
scPair: boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases |
2025-09-08 04:30:09 |
| abcoder |
0.3.1 |
Agentic bioinformatics coder |
2025-09-05 09:38:28 |
| cellproportion |
1.1.0 |
Cell type proportion analysis for single-cell and spatial transcriptomics data |
2025-08-29 14:37:07 |
| picasso-phylo |
0.1.2 |
Phylogenetic Inference of Copy number Alterations in Single-cell Sequencing data Optimization |
2025-08-28 17:24:12 |
| scmcp-shared |
0.6.6 |
A shared function libray for scmcphub |
2025-08-25 17:28:07 |
| biomarker-mcp |
0.2.1 |
Natural language interface for celltype marker query through MCP. |
2025-08-20 17:45:00 |
| nico-sc-sp |
1.5.0 |
This package finds covariation patterns between interacted niche cell types from single-cell resolution spatial transcriptomics data. |
2025-07-12 05:54:37 |
| step-kit |
0.3 |
STEP, an acronym for Spatial Transcriptomics Embedding Procedure, is a deep learning-based tool for the analysis of single-cell RNA (scRNA-seq) and spatially resolved transcriptomics (SRT) data. STEP introduces a unified approach to process and analyze multiple samples of scRNA-seq data as well as align several sections of SRT data, disregarding location relationships. Furthermore, STEP conducts integrative analysis across different modalities like scRNA-seq and SRT. |
2025-02-02 11:59:12 |
| gene-trajectory |
1.0.4 |
Compute gene trajectories |
2024-08-04 15:06:13 |
| omicfate |
0.0.1 |
OmicFate: Unraveling the Secrets of Cellular Fate Determination |
2024-07-19 08:33:46 |
| scprel |
1.2 |
Single-cell data preprocessing for multiple samples. |
2024-05-23 10:36:49 |
| CeLEryPy |
1.2.1 |
Leverage spatial transcriptomics data to recover cell locations in single-cell RNA RNA-seq |
2024-04-10 20:29:13 |