Comparative Single Cell Transcriptomics Reveals Distinct Cell Fate Transition Statuses during Human Cardiac Reprogramming
Direct lineage conversion among various somatic cell types revolutionized the field of stem cell and regenerative medicine. In addition, the platform of cellular reprogramming offered a powerful system to gain new knowledge about cell plasticity and cell fate determination and ultimately challenged previous notions of cell identity. Previously, we successfully utilized single cell transcriptomics to reconstruct the molecular routes of how a murine fibroblast adopts cardiomyocyte fate following a...
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.
- Single-Cell Transcriptomic Analyses of Cell Fate Transitions dur... 2019 · 132 cites
Deep data QC
100/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq (human). Grade A: perfect measured QC (Q30=96.7%, base quality=38.1, duplication=28.93%, adapter=0.02%, N-content=0.013%), short reads (50 bp) but high quality, minimal technical artifacts. This is a high-confidence dataset with excellent properties across all metrics. Highly recommended for reuse in standard and sensitive RNA-seq applications.
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