Table 1.
Overview of multi-omics datasets analyzed for method benchmarking and in two case studies
| Dataset | n | Omics | p |
|---|---|---|---|
| Colon | 92 | mRNA | 17 814 |
| Wang et al. (2014) | High: 33 | miRNA | 312 |
| Low: 59 | CpGs | 23 088 | |
| Kidney | 122 | mRNA | 17 665 |
| Wang et al. (2014) | High: 61 | miRNA | 329 |
| Low: 61 | CpGs | 24 960 | |
| Glioblastoma | 213 | mRNA | 12 042 |
| Wang et al. (2014) | High: 105 | miRNA | 534 |
| Low: 108 | CpGs | 1305 | |
| Lung | 106 | mRNA | 12 042 |
| Wang et al. (2014) | High: 53 | miRNA | 353 |
| Low: 53 | CpGs | 23 074 | |
| Breast Cancer | 989 | mRNA | 16 851 |
| TCGA Research Network (2012) | Basal: 76 (102) | miRNA | 349 |
| Her2: 38 (40) | CpGs | 9482 | |
| LumA: 188 (346) | Proteins | 115 (0) | |
| LumB: 77 (122) | |||
| Asthma | 28 | Cell types | 9 |
| Singh et al. (2013, 2014) | Pre: 14 | mRNA modules | 229 |
| Post: 14 | Metabolite modules | 60 |
Note: The breast cancer case study includes training (test) datasets for all omics types except proteins.