A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure
While the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures of cells. Here, we invoked inDrop, a droplet-based single-cell RNA-Seq method, to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two strains of mice. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, includin...
Provenance — who produced it, who reused it
Linked to 70 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.
- Comprehensive Integration of Single-Cell Data 2019 · 16,818 cites
- Integrating single-cell transcriptomic data across different con... 2018 · 14,641 cites
- Benchmarking atlas-level data integration in single-cell genomic... 2021 · 1,404 cites
- Efficient integration of heterogeneous single-cell transcriptome... 2019 · 997 cites
- Joint analysis of heterogeneous single-cell RNA-seq dataset coll... 2019 · 343 cites
- SCDC: bulk gene expression deconvolution by multiple single-cell... 2019 · 304 cites
- dittoSeq: universal user-friendly single-cell and bulk RNA seque... 2020 · 248 cites
- scMerge leverages factor analysis, stable expression, and pseudo... 2019 · 207 cites
- Longitudinal single-cell RNA-seq analysis reveals stress-promote... 2022 · 205 cites
- scClassify: sample size estimation and multiscale classification... 2020 · 165 cites
- Reconstruction of cell spatial organization from single-cell RNA... 2020 · 158 cites
- Iterative transfer learning with neural network for clustering a... 2020 · 156 cites
- Demystifying “drop-outs” in single-cell UMI data 2020 · 154 cites
- BERMUDA: a novel deep transfer learning method for single-cell R... 2019 · 150 cites
- The landscape of human tissue and cell type specific expression... 2022 · 113 cites
- Flexible comparison of batch correction methods for single-cell... 2021 · 100 cites
- Deep autoencoder for interpretable tissue-adaptive deconvolution... 2022 · 96 cites
- De novo analysis of bulk RNA-seq data at spatially resolved sing... 2022 · 95 cites
- Learning interpretable cellular and gene signature embeddings fr... 2021 · 94 cites
- Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA... 2019 · 86 cites
- Consistent RNA sequencing contamination in GTEx and other data s... 2020 · 85 cites
- A topology-preserving dimensionality reduction method for single... 2021 · 81 cites
48 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
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.