Batch effects and the effective design of single-cell gene expression studies
Single cell RNA sequencing (scRNA-seq) can be used to characterize variation in gene expression levels at high resolution. However, the sources of experimental noise in scRNA-seq are not yet well understood. We investigated the technical variation associated with sample processing using the single cell Fluidigm C1 platform. To do so, we processed three C1 replicates from three human induced pluripotent stem cell (iPSC) lines. We added unique molecular identifiers (UMIs) to all samples, to accoun...
Provenance — who produced it, who reused it
Linked to 10 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.
- Batch effects and the effective design of single-cell gene expre... 2017 · 370 cites
- Demystifying “drop-outs” in single-cell UMI data 2020 · 154 cites
- Detecting heterogeneity in single-cell RNA-Seq data by non-negat... 2017 · 97 cites
7 further papers cite this accession but reuse could not be confirmed.
Deep data QC
52/100 · 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