In this repository, we provide the code used for the processing, analysis, and visualization of Xenium spatial transcriptomics data associated with the 2025 manuscript: “A neuronal-epithelial circuit promotes sensory convergence and intestinal immunity.” The spatial transcriptomics component of this work was performed in collaboration between the David Artis Laboratory (Weill Cornell Medicine) and the Allen Institute for Immunology.
Raw and processed spatial transcriptomics data are available at the EBI BioImage Archive under accession number: S-BIAD2351.
This directory contains the primary Jupyter notebooks and scripts used in the analysis pipeline. Each folder contains notebooks that are numbered in the order they were run (e.g. 01a_, 01b_). Each step builds on outputs from the previous stage.
Before running the code, adjust file and directory paths in config/paths.py to match computing environment.
| Subdirectory | Description |
|---|---|
01_pre-processing |
Scripts for initial data handling: removing Peyer's patches, QC, and doublet detection |
02_integration |
Construction of a shared latent space across samples using scVI |
03_cell-labels |
Clustering and iterative annotation of cell types |
04_spatial-axes |
Modeling and prediction of spatial crypt-villus axis scores in intestinal tissue |
05_spatial-neighborhoods |
Spatial neighborhood analysis using CellCharter for epithelial and immune subsets |
06_statistical-testing |
Differential expression analysis and related statistical workflows |
07_figures |
Data visualization for manuscript figures |
This directory contains environment specifications used for reproducible analyses. Each file defines the dependencies required for a particular stage of the workflow in the form of a pinned conda environment. This information is directly linked at the notebook-level.
Code developed and maintained by @mncowan.
Selected analyses adapted from cited external sources:
- Reina-Campos, Miguel, et al. “Tissue-resident memory CD8 T cell diversity is spatiotemporally imprinted.” Nature 639, 483–492 (2025).
- Goldrath Lab — Spatial TRM Paper Repository