A single-cell transcriptome atlas of the human pancreas [CEL-seq2]
To understand organ function it is important to have an inventory of the cell types present in the tissue and of the corresponding markers that identify them. This is a particularly challenging task for human tissues like the pancreas, since reliable markers are limited. Transcriptome-wide studies are typically done on pooled islets of Langerhans, which obscures contributions from rare cell types and/or potential subpopulations. To overcome this challenge, we developed an automated single-cell...
Provenance — who produced it, who reused it
Linked to 52 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
- Batch effects in single-cell RNA-sequencing data are corrected b... 2018 · 2,713 cites
- Benchmarking atlas-level data integration in single-cell genomic... 2021 · 1,404 cites
- Efficient integration of heterogeneous single-cell transcriptome... 2019 · 997 cites
- Fully-automated and ultra-fast cell-type identification using sp... 2022 · 783 cites
- DSTG: deconvoluting spatial transcriptomics data through graph-b... 2020 · 231 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
- Evaluation of Cell Type Annotation R Packages on Single-Cell RNA... 2020 · 157 cites
- Iterative transfer learning with neural network for clustering a... 2020 · 156 cites
- BERMUDA: a novel deep transfer learning method for single-cell R... 2019 · 150 cites
- Aggressive PDACs Show Hypomethylation of Repetitive Elements and... 2020 · 129 cites
- scAlign: a tool for alignment, integration, and rare cell identi... 2019 · 111 cites
- Flexible comparison of batch correction methods for single-cell... 2021 · 100 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
- PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human I... 2019 · 80 cites
- Coordinated single-cell tumor microenvironment dynamics reinforc... 2023 · 75 cites
31 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.