A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex is a dataset published in Nature (2021). On theSindex it has a DataRank of 4.9, placing it in the top 29% of the data-sharing corpus. It has been cited 372 times, with 129 citing works in its 1-hop citation network. Its calibrated FAIR score is 53/100.
Abstract
Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.879
From this paper's citation signal
Citation Network Contribution
4.1
From 129 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Fast and accurate short read alignment with Burrows–Wheeler transformBioinformatics200962,117 citationsDataRank 1.7
- STAR: ultrafast universal RNA-seq alignerBioinformatics201355,202 citationsDataRank 1.6
- BEDTools: a flexible suite of utilities for comparing genomic featuresBioinformatics201030,023 citationsDataRank 1.5
- Fast unfolding of communities in large networksJournal of Statistical Mechanics: Theory and Experiment200820,951 citationsDataRank 1.5
- Model-based Analysis of ChIP-Seq (MACS)Genome Biology200819,654 citationsDataRank 1.5
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 18% comes from its base citations and 82% from the citation network (129 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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