Systems biological assessment of immunity to severe and mild COVID-19 infections
The recent emergence of COVID-19 presents a major global crisis. Profound knowledge gaps remain about the interaction between the virus and the immune system. Here, we used a systems biology approach to analyze immune responses in 76 COVID-19 patients and 69 age and sex- matched controls, from Hong Kong and Atlanta. Mass cytometry revealed prolonged plasmablast and effector T cell responses, reduced myeloid expression of HLA-DR and inhibition of mTOR signaling in plasmacytoid DCs (pDCs) during i...
Provenance — who produced it, who reused it
Linked to 34 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.
- Bioinformatics and machine learning approach identifies potentia... 2021 · 109 cites
33 further papers cite this accession but reuse could not be confirmed.
Deep data QC
99/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq (human). Grade A quality (99/100) with excellent measured metrics across all dimensions. Q30 of 95% (100/100 score) and mean base quality of 36.1 are the dual drivers of high reusability. Adapter content slightly elevated at 3.02% (94/100) but poses minimal risk for expression quantification.
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
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.