A survey of human brain transcriptome diversity at the single cell level
We used single cell RNA sequencing on 466 cells to capture the cellular complexity of the adult and fetal human brain at a whole transcriptome level. Healthy adult temporal lobe tissue was obtained from epileptic patients during temporal lobectomy for medically refractory seizures. We were able to classify individual cells into all of the major neuronal, glial, and vascular cell types in the brain.
Provenance — who produced it, who reused it
Linked to 58 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.
- Integrative single-cell analysis of transcriptional and epigenet... 2017 · 1,088 cites
- Fully-automated and ultra-fast cell-type identification using sp... 2022 · 783 cites
- Multiscale Analysis of Independent Alzheimer’s Cohorts Finds Dis... 2018 · 729 cites
- CIDR: Ultrafast and accurate clustering through imputation for s... 2017 · 582 cites
- Brain Cell Type Specific Gene Expression and Co-expression Netwo... 2018 · 547 cites
- SingleCellNet: A Computational Tool to Classify Single Cell RNA-... 2019 · 404 cites
- Integrative network analysis of nineteen brain regions identifie... 2016 · 344 cites
- Deep learning–based cell composition analysis from tissue expres... 2020 · 296 cites
- Polymer Simulations of Heteromorphic Chromatin Predict the 3D Fo... 2018 · 209 cites
- scIGANs: single-cell RNA-seq imputation using generative adversa... 2020 · 198 cites
- Dissecting transcriptomic signatures of neuronal differentiation... 2020 · 195 cites
- AutoImpute: Autoencoder based imputation of single-cell RNA-seq... 2018 · 187 cites
- Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex... 2018 · 172 cites
- Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with... 2017 · 160 cites
- Assessing similarity to primary tissue and cortical layer identi... 2016 · 113 cites
- Global landscape and genetic regulation of RNA editing in cortic... 2019 · 101 cites
- Deep autoencoder for interpretable tissue-adaptive deconvolution... 2022 · 96 cites
- Single-cell transcriptomics reveals specific RNA editing signatu... 2017 · 86 cites
- Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA... 2019 · 86 cites
- Single-cell RNA-seq clustering: datasets, models, and algorithms 2020 · 75 cites
- STAB: a spatio-temporal cell atlas of the human brain 2020 · 71 cites
36 further papers cite this accession but reuse could not be confirmed.
Deep data QC
86/100 · BStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The B 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