Single-cell RNA-seq of mouse cerebral cortex
We have applied a recently developed, highly accurate and sensitive single-cell RNA-seq method (STRT/C1) to perform a molecular census of two regions of the mouse cerebral cortex: the somatosensory cortex and hippocampus CA1.
Provenance — who produced it, who reused it
Linked to 66 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
- 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
- scGNN is a novel graph neural network framework for single-cell... 2021 · 457 cites
- Characterizing transcriptional heterogeneity through pathway and... 2016 · 439 cites
- Genetic mapping of cell type specificity for complex traits 2019 · 384 cites
- Realistic in silico generation and augmentation of single-cell R... 2020 · 262 cites
- AutoImpute: Autoencoder based imputation of single-cell RNA-seq... 2018 · 187 cites
- Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with... 2017 · 160 cites
- SERGIO: A Single-Cell Expression Simulator Guided by Gene Regula... 2020 · 131 cites
- Fast and accurate single-cell RNA-seq analysis by clustering of... 2016 · 129 cites
- Correcting the Mean-Variance Dependency for Differential Variabi... 2018 · 120 cites
- Mapping the transcriptional diversity of genetically and anatomi... 2019 · 96 cites
- De novo analysis of bulk RNA-seq data at spatially resolved sing... 2022 · 95 cites
- Graph embedding and Gaussian mixture variational autoencoder net... 2023 · 95 cites
- Exploiting single-cell expression to characterize co-expression... 2016 · 94 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
- Significant Evolutionary Constraints on Neuron Cells Revealed by... 2020 · 81 cites
46 further papers cite this accession but reuse could not be confirmed.
Deep data QC
76/100 · CStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The C 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