HyperNetWalk: data and reference networks

This repository contains the supporting data for HyperNetWalk, an unsupervised
hypergraph-based framework for personalized and cohort-level cancer driver gene
identification via reverse inference on a layered signaling–regulatory network.

These files are too large to host on GitHub and are archived here. The source
code is available at: https://github.com/xqxu921/HyperNetWalk

Contents (download what you need and unzip into the `data/` directory of the
HyperNetWalk repository):

– network_reference.zip — PPI/GRN/TF–target networks and gene annotation
  (STRINGv12.txt, 9606.protein.links.v12.0.onlyAB.tsv, RegNet_human_V2.txt,
  gencode.v36.annotation.gtf.gene.probemap, omnipath_interactions.tsv).
  Required to run the model. Unzip into data/.

– processed_data.zip — Preprocessed model inputs (mutation and expression
  matrices) for 12 TCGA cancer types (BRCA, COAD, HNSC, KIRC, KIRP, LIHC,
  LUAD, LUSC, PRAD, STAD, THCA, UCEC). Unzip into data/processed_data/.

– rawdata.zip — TCGA raw files (somatic mutation, expression counts/TPM,
  survival) per cancer type, for full reproduction from raw data.
  Unzip into data/rawdata/.

See the repository README for usage instructions. If you use these data,
please cite the associated manuscript (in preparation) and this record.