Solo: doublet identification via semi-supervised deep learning
We developed a semi-supervised deep learning framework for the identification of doublets in scRNA-seq analysis called Solo. To validate our method, we used MULTI-seq, cholesterol modified oligos (CMOs), to experimentally identify doublets in a solid tissue with diverse cell types, mouse kidney, and showed Solo recapitulated experimentally identified doublets.
Provenance — who produced it, who reused it
Linked to 2 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.
- Benchmarking Computational Doublet-Detection Methods for Single-... 2020 · 218 cites
1 further paper cites this accession but reuse could not be confirmed.
Deep data QC
26/100 · FStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The F 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