rizanb/lusc-ppi

This dataset combines expression data from TCGA with protein interaction network from STRING. An example implementation of a GCN trained on this dataset is available here.

How

I built node indices from filtered human ppi in notebooks/process_human_ppi.ipynb to make a custom PyG Dataset. human ppi -> filter for high confidence (score > 0.7) -> filter to keep only genes in expression dataset -> gene ids -> integer ids -> edge indices GCN expects a graph dataset; each graph represents a patient sample. Nodes represent genes, and edges represent protein-protein interactions between genes. The structure of the graph is the same for all samples (given by edge indices), only the node features (expression levels for each gene) vary.

Data

• expression: TCGA LUSC HiSeqV2 gene expression data (178 samples, 20530 genes), UCSC Xena
• subtype labels: TCGA Nature 2012 paper supplementary data
• STRING PPI network: Homo sapiens interaction data; high-confidence interactions (score > 0.7)
• class distribution:

classical: 65 (36.5%)
basal: 43 (24.2%)
secretory: 43 (24.2%)
primitive: 27 (15.2%)