Single-cell transcriptomics reveals widespread heterogeneity in gene regulation and RNA processing in stimulated immune cells
Here, we used single-cell RNA-Seq to discover extensive cell-to-cell variability – in transcript expression levels, cell state, circuit usage, and alternative splicing – between stimulated immune cells
Provenance — who produced it, who reused it
Linked to 5 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.
- MAST: a flexible statistical framework for assessing transcripti... 2015 · 3,574 cites
- Inferring Causal Gene Regulatory Networks from Coupled Single-Ce... 2020 · 181 cites
3 further papers cite this accession but reuse could not be confirmed.
Deep data QC
97/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The A 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