Corpus 1,285 assessed · 1,186 scored · 647 reproduced ≥75 · 174 flagged ·∅ 73.9/100
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GSE99866

GEO first seen 2018

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

Organism
Mus musculus
Samples
74
Type
Expression profiling by high...
Submitted
2017-06-09

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in several of the reaction steps of Quartz-Seq2 allow us to effectively convert initial reads to UMI counts (at a rate of 30%–50%). To demonstrate the power of Quartz-Seq2, we an...

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.

Deposited / produced by
Hiroki DannoYohei SasagawaItoshi Nikaido
Reused by

Deep data QC

metadata only · no data-level QC for this type

Standardized, 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.

QC cost 22 s compute

measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently