RNA-Seq profiling of 29 immune cell types and peripheral blood mononuclear cells
We performed RNA-Seq transcriptome profiling on 29 immune cell types consituting peripheral blood mononuclear cells (PBMCs) sorted from 4 Singaporean-Chinese individuals (S4 cohort). We also performed RNA-Seq and microarray transcriptome profiling of PBMCs from an extended cohort of 13 individuals (S13 cohort). The data was used first to characterize the transcriptomic signatures and relationships among the 29 immune cell types. Then we explored the difference in mRNA composition in terms of tra...
Provenance — who produced it, who reused it
Linked to 26 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 learning–based cell composition analysis from tissue expres... 2020 · 296 cites
- CARD8 inflammasome activation triggers pyroptosis in human T cel... 2020 · 189 cites
- EPISCORE: cell type deconvolution of bulk tissue DNA methylomes... 2020 · 119 cites
- Deep autoencoder for interpretable tissue-adaptive deconvolution... 2022 · 96 cites
- Polygenic regression uncovers trait-relevant cellular contexts t... 2023 · 94 cites
20 further papers cite this accession but reuse could not be confirmed.
Deep data QC
100/100 · 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