Gene expression data from 62 colorectal cancers
We stratified colorectal tumor samples using a new unsupervised, iterative method based on non-negative matrix factorization (NMF). The resulting five subtypes exhibited activation of specific signaling pathways, and significant differences in microsatellite status and tumor location. We could also align three CRC cell lines panels to these subtypes.
Provenance — who produced it, who reused it
Linked to 25 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- The consensus molecular subtypes of colorectal cancer 2015 · 5,246 cites
- DeepCC: a novel deep learning-based framework for cancer molecul... 2019 · 234 cites
- Prognostic genome and transcriptome signatures in colorectal can... 2024 · 134 cites
- PreMSIm: An R package for predicting microsatellite instability... 2020 · 114 cites
- Suppression Colitis and Colitis-Associated Colon Cancer by Anti-... 2017 · 95 cites
19 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.