Deep sequencing reveals cell-type specific patterns of single cell transcriptome variation
We present high quality deep read-depth single cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cel...
Provenance — who produced it, who reused it
Linked to 4 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 cytokinesis by β-adrenergic receptors indicates an ap... 2019 · 116 cites
- Deep sequencing reveals cell-type-specific patterns of single-ce... 2015 · 101 cites
2 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently