Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
We developed a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or nuclei (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We applied sci-RNA-seq to profile nearly 50,000 cells from C. elegans at the L2 stage, effectively ~56-fold “shotgun cellular coverage” of its somatic cell composition.
Provenance — who produced it, who reused it
Linked to 5 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.
- An ultra high-throughput method for single-cell joint analysis o... 2019 · 380 cites
- Deep learning tackles single-cell analysis—a survey of deep lear... 2021 · 75 cites
2 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
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently