Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development
The development of single-cell RNA-Sequencing (scRNA-Seq) has allowed high resolution analysis of cell type diversity and transcriptional networks controlling cell fate specification. To identify the transcriptional networks governing human retinal development, we performed scRNA-Seq over retinal organoid and in vivo retinal development, across 20 timepoints. Using both pseudotemporal and cross-species analyses, we examined the conservation of gene expression across retinal progenitor maturation...
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
42/100 · FStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq of Homo sapiens via HiSeq 4000 with lower base quality (60.6% Q30, 0.009% N content) and confirmed GEO UID, spanning ~802M reads and 240 Gb total bases. This dataset enables human gene expression profiling with substantial depth despite moderate quality. The high-throughput platform and large scale support comprehensive transcriptome characterization, though aggressive quality filtering is essential before reliable analysis.
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