Single-cell multi-modal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer
- Download the
sample_datafolder from: https://drive.google.com/drive/folders/1Uaql_imN9OqwGlAtYdn9R71E5OapOEkI?usp=sharingg - Unzip and place it within this repository
- sample_data: example datasets for testing and debugging the codes
- cal_spatial.py: perform spatial statistical analyses of the mIF data
- spatial_util.py: utility functions for spatial statistical analyses, visualization and group comparisons
- deseq_rank_genes.R: perform differential gene expression analysis
- geneset_rna.py: gene set enrichment analysis
- hover_net: training and evaluating scripts for nuclear segmentation and classification using histology images. The codes were adapted from Graham et al. (https://github.com/vqdang/hover_net), with customizations for incorporating weighted loss function.
- sc_MTOP: codes for extracting single-cell morphological, textural and topological features from histology images. The codes were adapted from Zhao et al. Nat Comm (2023), with customizations for incorporating new features and additional cell types
- To run spatial statistical analyses of the mIF data, install the scimap package: https://scimap.xyz/Getting%20Started/
- To train a nuclear segmentation and classification model on histology images, install the system requirements for hovernet: https://github.com/vqdang/hover_net
- To perform single-cell microenvironment analysis on histology images, install the system requirements for sc-MTOP: https://github.com/fuscc-deep-path/sc_MTOP
- To run differential gene expression analysis, install the DESeq2 library: https://bioconductor.org/packages/release/bioc/html/DESeq2.html
- To perform gene set enrichment analysis, install the GSEApy library: https://gseapy.readthedocs.io/en/latest/introduction.html#id1
-
Perform spatial statistical analyses of the mIF data
python cal_spatial.py -
Perform gene set enrichment analysis
Rscript deseq_rank_genes.R
python gene_set_rna.py -
Train nuclear segmentation and classification model on histology images
python extract_patches.py
python hover_net/run_train.py -
Run inference using pre-trained NucSegAI model: https://github.com/gevaertlab/NucSegAI
-
Extract nuclear morphology, textural, and topological features
python sc_MTOP/extract_features.py