| Name (link) | Description | Type | Size |
|---|---|---|---|
| Microarray normalized profiles | A matrix: rows are genes, columns are GEO samples | RData | ~850MB |
| Microarray sample labels matrix | A binary matrix: rows are samples, columns are DO terms | RData | 46kb |
| Microarray sample annotation | A matrix: rows are samples, columns represent sample information (e.g., dataset, platform, DO terms, and more) | xlsx | 2.57MB |
| Microarray platforms info | An R list with an entry for each platform. The entry maps entrez genes to their probes | RData | 4.3MB |
| Sample to dataset mapping | A named vector that maps each sample to its dataset id - useful for running cross validation | RData | 36kb |
| Name (link) | Description | Type | Size |
|---|---|---|---|
| RNASeq expression profiles | A matrix: rows are samples, columns are genes (>18,000) | RData | 78MB |
| RNASeq sample labels matrix | A binary matrix: rows are samples, columns are DO terms | RData | 12kb |
| RNA-seq sample annotation | A matrix: rows are samples, columns are genes | xslx | 120kb |
| Name (link) | Description | Type | Size |
|---|---|---|---|
| Gene to cancer subtype mapping, COSMIC analysis (1) | Tab delimited: each row represents a gene-subtype pair (0.05 FDR) | txt | 1296kb |
| Drug to gene ids mapping (2) | An R list: each drug id is mapped to its genes | RData | 23kb |
| Gene PB-ROC scores | A matrix: rows are genes, columns are diseases | RData | 1274kb |
| Gene PN-ROC scores | A matrix: rows are genes, columns are diseases | RData | 1120kb |
| Gene SMQ scores | A matrix: rows are genes, columns are diseases | RData | 1299kb |
| Gene Entrez to Gene name | A mapping between entrez ids and gene names | txt | 254kb |
| PPI network form IntAct (3) | These PPIs were used in Figure 5 | txt | 600kb |
| Final Binary relevance model | An R object that contains the selected multilabel classifier (can be uploaded and used in the code examples below) | RData | 49MB |
| Pathway to genes mapping | An R list that maps each pathway (KEGG, NCI, Reactome, Biocarta) to its genes (entrez ids) | RData | 186kb |
| Our selected gene sets (Supplementary Table 1) | A table with the selected genes for each disease in our analysis | txt | 340kb |
(1) Forbes, S. A., Bindal, N., Bamford, S., Cole, C., Kok, C. Y., Beare, D., … Futreal, P. A. (2011). COSMIC: Mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Research, 39. doi:10.1093/nar/gkq929
(2) Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A. C., Liu, Y., … Wishart, D. S. (2014). DrugBank 4.0: Shedding new light on drug metabolism. Nucleic Acids Research, 42. doi:10.1093/nar/gkt1068
(3) Orchard, S., Ammari, M., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F., … Hermjakob, H. (2014). The MIntAct project - IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Research, 42. doi:10.1093/nar/gkt1115