Single-cell RNA sequencing reveals tissue architecture during human high-grade serous ovarian cancer progression
As the most fatal type of gynecological cancers, high-grade serous ovarian cancer (HGSOC) exhibits heterogeneity that obstacles therapeutics. Up to date, the pathogenesis of HGSOC remains poorly understood. Here, we performed the deep single-cell RNA sequencing (scRNA-seq) using 59,324 cells isolated from seven treatment-naïve HGSOC patients at early or late tumor stages and five age-matched non-malignant ovarian samples. Our sequencing data showed a highly complex ecosystem of tumor, immune an...
Provenance — who produced it, who reused it
Linked to 20 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.
19 further papers cite this accession but reuse could not be confirmed.
Deep data QC
38/100 · FStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The F 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