Drop-Seq analysis of P17 FACS sorted retinal cells from the Tg(Chx10-EGFP/cre,-ALPP)2Clc or Vsx2-GFP transgenic line
Vsx2-GFP mouse retinas were dissected, FACS sorted for GFP+ cells and single-cell mRNAseq libraries generated with Drop-Seq
Provenance — who produced it, who reused it
Linked to 10 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.
- Comprehensive Integration of Single-Cell Data 2019 · 16,818 cites
- Analytic Pearson residuals for normalization of single-cell RNA-... 2021 · 206 cites
- CaSTLe – Classification of single cells by transfer learning: Ha... 2018 · 132 cites
- VEGA is an interpretable generative model for inferring biologic... 2021 · 106 cites
- Learning interpretable cellular and gene signature embeddings fr... 2021 · 94 cites
5 further papers cite this accession but reuse could not be confirmed.
Deep data QC
68/100 · DStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq from mouse tissue. Grade D (68/100) reflects two critical failures: Q30 bases at 79.4% (scored 47/100) and mean base quality of 31.7 (scored 62/100), both substantially below acceptable thresholds. The read length of 20 bp is extremely short for RNA-seq and compounds mapping and annotation challenges; this dataset is not recommended for reuse.
The D 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.