Single-cell RNA-Seq reveals dynamic, random monoallelic gene expression in mammalian cells
In the diploid genome, genes come in two copies, which can have different DNA sequence and where one is maternal and one is paternal. In a particular cell, a gene could potentially be expressed from both copies (biallelic expression) or only one (monoallelic). We performed RNA-Sequencing on individual cells, from zygote to the cells of the late blastocyst, and also individual cells from the adult liver. Using first generation crosses between two distantly related mouse strains, CAST/Ei and C57BL...
Provenance — who produced it, who reused it
Linked to 48 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 accurate and robust imputation method scImpute for single-cel... 2018 · 762 cites
- Broad histone H3K4me3 domains in mouse oocytes modulate maternal... 2016 · 662 cites
- Single-cell multi-omics sequencing of mouse early embryos and em... 2017 · 393 cites
- scMerge leverages factor analysis, stable expression, and pseudo... 2019 · 207 cites
- AutoImpute: Autoencoder based imputation of single-cell RNA-seq... 2018 · 187 cites
- Heterochromatin establishment during early mammalian development... 2020 · 147 cites
- Linnorm: improved statistical analysis for single cell RNA-seq e... 2017 · 134 cites
- CaSTLe – Classification of single cells by transfer learning: Ha... 2018 · 132 cites
- A Single-Cell Transcriptomics CRISPR-Activation Screen Identifie... 2020 · 96 cites
- Exploiting single-cell expression to characterize co-expression... 2016 · 94 cites
- PseudotimeDE: inference of differential gene expression along ce... 2021 · 93 cites
- Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA... 2019 · 86 cites
- McImpute: Matrix Completion Based Imputation for Single Cell RNA... 2019 · 82 cites
- Transcriptome-wide Variability in Single Embryonic Development C... 2014 · 77 cites
34 further papers cite this accession but reuse could not be confirmed.
Deep data QC
95/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The A 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