Mapping Mouse Cell Atlas by Microwell-seq
We developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we constructed a basic scheme for the Mouse Cell Atlas. We reveal single cell hierarchy for many tissues that have not been well previously characterized. Our study demonstrates the wide applicability of the Microwell-seq technology and the Mouse Cell Atlas resource.
Provenance — who produced it, who reused it
Linked to 21 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.
- Revealing the Critical Regulators of Cell Identity in the Mouse... 2018 · 382 cites
- Data denoising with transfer learning in single-cell transcripto... 2019 · 206 cites
- Scalable analysis of cell-type composition from single-cell tran... 2019 · 185 cites
- Single-Cell Transcriptomic Map of the Human and Mouse Bladders 2019 · 178 cites
- Network inference with Granger causality ensembles on single-cel... 2022 · 125 cites
- scGCN is a graph convolutional networks algorithm for knowledge... 2021 · 123 cites
- Categorization of lung mesenchymal cells in development and fibr... 2021 · 87 cites
- Significant Evolutionary Constraints on Neuron Cells Revealed by... 2020 · 81 cites
- A Quantitative Framework for Evaluating Single-Cell Data Structu... 2020 · 74 cites
12 further papers cite this accession but reuse could not be confirmed.
Deep data QC
81/100 · 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