| Name | Version | Summary | date |
| NODE-deconvolution-scipy |
0.1.3 |
A Python program for deconvolution spatial transcriptomics data and inference of spatial communication |
2024-05-04 12:02:47 |
| NODE-deconvolution |
0.1.3 |
A Python program for deconvolution spatial transcriptomics data and inference of spatial communication |
2024-05-01 12:17:41 |
| SpatialDM |
0.2.0 |
SpatialDM: Spatial co-expression Detected by bivariate Moran |
2024-04-28 09:50:47 |
| step-kit |
0.2.2 |
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. |
2024-04-25 17:18:19 |
| squidpy |
1.4.1 |
Spatial Single Cell Analysis in Python |
2024-02-06 13:04:07 |
| dissect-deconv |
0.1 |
|
2024-01-11 15:48:42 |
| SOAPy-st |
0.1.4 |
Spatial Omics Analysis in Python |
2024-01-02 13:39:15 |
| DeepTalk-ST |
0.0.2 |
Cell-cell communication prediction for ST data |
2023-12-22 11:53:34 |
| nico-sc-sp |
0.1.0 |
This package finds covariation patterns between interacted niche cell types from single-cell resolution spatial transcriptomics data. |
2023-12-15 10:51:03 |
| HumanHeartSpatial |
0.0.3 |
Python package for analysis of slide-seq data on heart tissue |
2023-10-12 04:38:28 |
| SpatialHumanHeart |
0.0.4 |
Python package for analysis of slide-seq data on heart tissue |
2023-10-12 02:40:16 |
| HumanSpatialHeart |
0.0.6 |
Python package for analysis of slide-seq data on heart tissue |
2023-10-12 02:24:55 |
| ecmanalysis |
0.0.18 |
Tools for analysis of the extracellular matrix |
2023-01-18 13:45:30 |