Multimodal spatial transcriptomic characterization of mouse kidney injury and repair
| Figure | Description | Code |
|---|---|---|
| Fig 2 | Xenium data preprocessing and visualization | link |
| Fig 3 | Xenium PT spatial trajectory analysis | link |
| Fig 4 | Xenium cell neighborhood analysis | link |
| Fig 5 A,B | Xenium Visium registration and alignment | link |
| Fig 5 E,F | Visium cell neighborhood functional characterization | link |
| Fig 5 G | TF activity inference | link |
| Fig 6 A | Xenium LR analysis | link |
| Fig 6 C-H | Visium cell-cell communication | link |
| Supple Fig 1 | Xenium and Visium QC | link |
| Supple Fig 3 | Xenium snRNA-seq integration | link |
| Supple Fig 8 | Pseudo-Visium analysis | link |
| Supple Fig 10 | Xenium imputation | link |
| Supple Fig 16 | Benchmark segmentation algorithm on Xenium | link |
| Supple Fig 17 | Benchmark spot deconvolution algorithm on Visium | link |
The pySTIM provides a comprehensive computational pipeline for the integration and analysis of spatial transcriptomics data. This tool is designed to integrate tissue histology with both high-definition spatial transcriptomics (e.g., Xenium) and transcriptome-wide spatial transcriptomics (e.g., Visium).
Installation using pip:
pip install pySTIM
import pySTIM as pst