| Name | Version | Summary | date |
| abcoder |
0.2.2 |
Agentic bioinformatics coder |
2025-07-15 10:16:43 |
| 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 |
| scmcp-shared |
0.6.3 |
A shared function libray for scmcphub |
2025-07-09 21:27:34 |
| omicverse |
1.7.5 |
OmicVerse: A single pipeline for exploring the entire transcriptome universe |
2025-07-09 10:43:52 |
| 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 |
| IRescue |
1.1.2 |
Interspersed Repeats singl-cell quantifier |
2024-09-12 16:20:05 |
| 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 |