IOBR is an R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology.

Deciphering tumor ecosystems at super-resolution from spatial transcriptomics with TESLA

Spatial Multiomics Profiler for Spatial Characterization of Tissue Microenvironment (https://smprofiler.io)

A collection of tumour microenvironment single-cell RNA sequencing datasets for use in R and other pipelines.

Predictive models and analysis of cancer prognosis and drug response using primary tumor microbial abundances derived from WGS and RNA-seq sequencing data for 32 TCGA cancers (Poore et al. Nature 2020), including equivalent models using TCGA RNA-seq gene expression and combined microbial abundance and gene expression for comparison.

Analysis of treatment naive and neo-adjuvant chemotherapy treated high-grade serous ovarian cancer samples

Optimized pipelines for Spatial Transcriptomics (ST) data analysis using Seurat & Giotto, designed for reproducible benchmarking and biological insight.

A classifier for tumor microenvironment subtype based on ensemble machine learning models

PhysiGym is a tool for applying reinforcement learning to PhysiCell

Distinct mesenchymal cell states mediate prostate cancer progression

Spatial EcoTyper is a machine learning framework for systematic identification of spatially distinct multicellular communities from single-cell spatial transcriptomics data.

Tracking Multiphase blood flow into a tumor

xCell framework, enabling expert-level tumor microenvironment cell-type enrichment and TME index profiling from bulk transcriptomic data.

This repository contains code and data for the study: "Neoantigens and Stochastic Fluctuations Regulate T Cell Proliferation in Primary and Metastatic M

A reproducible framework for modeling spatial cell–cell interactions in the tumor microenvironment using multitype Gibbs point processes and multiplexed imaging data.

Interpretable spatial graph framework integrating pathway and ligand–receptor priors with tissue architecture. Generates pathway maps and H&E overlays that reveal how tumors organize and rewire signaling in space.

An R package for modeling asymmetric spatial associations between cell types in tissue images using a multilevel Bayesian framework.

Code and experiments for "Non-convex SVM for cancer diagnosis based on morphologic features of tumor microenvironment"

Serial Imaging of the Tumor and microEnvironment

Generalized promotion time cure model