Validation of noise models for single-cell transcriptomics
Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity amongst single cells. Here we identify two major sources of technical variability, sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this and after validation by single-molecule FISH experiments, we apply these models to demonstrate that growing mES cells in 2i instead of serum/LIF globally reduces gene expression varia...
Provenance — who produced it, who reused it
Linked to 13 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.
- Comparative analysis of differential gene expression analysis to... 2019 · 338 cites
- Quantification of cell identity from single-cell gene expression... 2015 · 170 cites
- Single-Cell RNA-Sequencing: Assessment of Differential Expressio... 2017 · 130 cites
- Correcting the Mean-Variance Dependency for Differential Variabi... 2018 · 120 cites
- Beyond comparisons of means: understanding changes in gene expre... 2016 · 106 cites
8 further papers cite this accession but reuse could not be confirmed.
Deep data QC
60/100 · DStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The D 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