Systematic comparative analysis of single cell RNA-sequencing methods
A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples – cell lines, peripheral blood mononuclear cells and brain tissue – generating 36 libraries in six s...
Provenance — who produced it, who reused it
Linked to 12 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.
- Comparison and evaluation of statistical error models for scRNA-... 2022 · 659 cites
- MultiVI: deep generative model for the integration of multimodal... 2023 · 310 cites
9 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently