IOBR is an R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology. https://iobr.github.io/IOBR/

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.

PhysiGym is a tool for applying reinforcement learning to PhysiCell

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

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

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

A pan-cancer morphology atlas linking histological features to molecular and clinical outcomes

End-to-end scRNA-seq + AI pipeline for melanoma TME biomarker discovery. CD79A validated in TCGA-SKCM (n=314, Log-Rank p=0.0044, +18.1 months OS).

Distinct mesenchymal cell states mediate prostate cancer progression

End-to-end CODEX multiplex IF analysis pipeline that includes cell segmentation, phenotyping, and spatial neighborhood analysis on the Schürch/Nolan CRC dataset

Code for IN-DEPTH

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

A comprehensive multidisciplinary review by Samuelson G. exploring Glioblastoma (GBM) pathobiology, metabolic starvation strategies, novel immuno-oncology therapeutics, adaptive clinical trials, and medical ethics.

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

Transfer learning framework (CoGAPS + projectR) quantifying cross-species conservation of tumor microenvironment transcriptional programs between mouse models and human melanoma, with immunotherapy response prediction (AUC = 0.917).

Operator-geometric inference of non-passive transport structure using PDE-constrained Hodge–Laplacian graph neural networks

Exploratory scRNA-seq analysis of T cell transcriptional states in human lung adenocarcinoma, contextualized within the global tumor microenvironment.