DOI: 10.5281/zenodo.15496011
title: Gene Regulatory Networks of LINCS and TCGA

description: This data is based on a simple Bayesian network from publicly available data on gene expression levels TCGA and LINCS.For more information, please refer to the following.

Producer name withheld due to blind review in progress.

## Data

### 1. GRN Dataset & Preprocessing  All graph data objects live under `data/GRN_dataset/` and are generated by the preprocessing script:

```plaintext└── data/    └── GRN_dataset/        ├── Breast/        │   ├── Edge_feature/        │   │   ├── Breast_tcga_ecv.csv      # TCGA patient-specific edge contribution values (ECv)        │   │   └── Breast_lincs_kd_ecv.csv  # LINCS knockdown edge contribution values        │   ├── Node_feature/        │   │   ├── Breast_TCGA_exp.csv       # TCGA patient-specific gene expression levels        │   │   └── Breast_LINCS_KD_exp.csv   # LINCS knockdown gene expression levels        │   └── make_GRN_dataset/        │       └── mk_GRN.py                 # builds per-sample PyG graphs and pickles them        ├── Colorectal/…                       # same structure for colorectal cancer        └── Lung/…                             # same structure for lung cancer```

- Edge_feature: 1D scalar “edge contribution values” (ECv) per sample- Node_feature: 1D scalar gene expression per sample- mk_GRN.py: combines node + edge features into torch_geometric.data.Data objects and serializes them.

### 2. Label Data for FinetuningAll task labels live under `data/labels/`:

```plaintext└── data/    └── labels/        ├── BP_data/        │   └── gene_with_BP_multilabels.csv    # GO-BP multilabels (shared)        ├── CC_data/        │   └── gene_with_CC_multilabels.csv    # GO-CC multilabels (shared)        ├── Cancer_rel_data/        │   └── gene_with_cancer_relation.csv   # Cancer-relation labels (shared)        ├── Subtype_data/        │   └── Breast/subtype.csv              # Breast cancer subtype per patient        └── Survival_data/            ├── Breast/tcga_survival_time.csv   # OS time & event for hazard prediction            ├── Colorectal/…            └── Lung/…```

### 3. MetadataHelper files for mapping and filtering samples, under `data/meta_data/`:

```plaintextSupGCL/└── data/    └── meta_data/        └── Breast/            ├── Breast_LINCS_KD_graphs_metadata.pkl   # Order of LINCS KD graphs            ├── LINCS_sampleID_KDgene_metadata.pkl    # Map LINCS sample → knocked-down gene            └── Breast_tcga_graphs_metadata.pkl       # Order of TCGA patient graphs```- LINCS metadata: used by SupGCL pretraining to match teacher (knockdown) graphs- TCGA metadata: used in finetuning to filter only patients with survival/subtype annotations (via --meta)

## About TCGA DatasetsThe Cancer Genome Atlas (TCGA) Research Network .TCGA TARGET GTEx data were accessed through the UCSC Xena Browser:  Derived data available at: https://xenabrowser.net/datapages/?cohort=TCGA%2520TARGET%2520GTEx&removeHub=https%253A%252F%252Fxena.treehouse.gi.ucsc.edu%253A443

## About LINCS DatasetsSubramanian A, et al. "A Next Generation Connectivity Map: L1000 Platformand the First 1,000,000 Profiles." Cell 2017.LINCS Program, NIH. L1000 data available at GEO: GSE.92742## About Algorithm of Bayesian NetworkYoshinori Tamada, Teppei Shimamura, Rui Yamaguchi, Seiya Imoto, Masao Nagasaki, and Satoru Miyano.Sign: Large-Scale Gene Network Estimation Environment for High Performance Computing. GenomeInformatics, 25(1):40–52, 2011.Seiya Imoto, Takao Goto, and Satoru Miyano. Estimation of genetic networks and functional struc-tures between genes by using Bayesian networks and nonparametric regression. Pacific Symposium onBiocomputing. Pacific Symposium on Biocomputing, pages 175–186, 2002.