Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes
Retinal ganglion cells (RGCs) convey the major output of information collected from the eye to the brain. Thirty subtypes of RGCs have been identified to date. Here, we analyze 6,225 RGCs (average of 5,000 genes per cell) from right and left eyes by single cell RNA-seq and classify them into 40 subtypes using clustering algorithms. We identify additional subtypes and markers, as well as transcription factors predicted to cooperate in specifying RGC subtypes. Zic1, a marker of the right eye-enric...
Provenance — who produced it, who reused it
Linked to 1 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.
Deep data QC
73/100 · CStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq (mouse). Grade C: problematic duplication (71.61%) combines with below-optimal base quality (Q30=84.3%, mean Q=36) to create a lower-tier dataset. Large sample size (731M reads, 71B bases) does not compensate for these technical issues, particularly the severe amplification bias. Reuse is not recommended without deep investigation and likely requires extensive filtering or filtering-aware quantitation.
The C 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