Context specificity of the EMT transcriptional response
Epithelial-mesenchymal plasticity contributes to a variety of biological processes, including tumour progression. A variety of epithelial-mesenchymal transition (EMT) responses have been reported and no common, EMT-defining gene expression program has been identified. Here, we have performed a comparative analysis of the EMT response, leveraging highly multiplexed single-cell RNA sequencing (scRNA-seq) to measure expression profiles of 103,999 cells from 960 samples, comprising 12 EMT time cours...
Provenance — who produced it, who reused it
Linked to 13 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.
- Control of cell state transitions 2022 · 89 cites
- Immunosuppressive Traits of the Hybrid Epithelial/Mesenchymal Ph... 2021 · 83 cites
- A mechanistic model captures the emergence and implications of n... 2021 · 79 cites
10 further papers cite this accession but reuse could not be confirmed.
Deep data QC
100/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq from Homo sapiens; perfect A-grade (100/100). Exceptionally low duplication (1.46%) and high base quality (95.3% Q30, Q34.2 mean) are the dual drivers; despite short 25.6bp reads, minimal duplication risk and reliable base calls make this ideal for precise differential-expression reuse and meta-analysis pooling.
The A grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0
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.