Characterization of the single-cell (ES and MEF) transcriptional landscape by highly multiplex RNA-Seq
Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constitutent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-Seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data was projected. The resulti...
Provenance — who produced it, who reused it
Linked to 18 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
- SCnorm: robust normalization of single-cell RNA-seq data 2017 · 316 cites
- Observation weights unlock bulk RNA-seq tools for zero inflation... 2018 · 278 cites
- Linnorm: improved statistical analysis for single cell RNA-seq e... 2017 · 134 cites
- Single-Cell RNA-Sequencing: Assessment of Differential Expressio... 2017 · 130 cites
- Comparison of methods to detect differentially expressed genes b... 2016 · 104 cites
- Normalization Methods on Single-Cell RNA-seq Data: An Empirical... 2020 · 96 cites
11 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