Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape
Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape is a dataset published in Genome Biology (2021). On theSindex it has a DataRank of 4.4, placing it in the top 29.4% of the data-sharing corpus. It has been cited 187 times, with 156 citing works in its 1-hop citation network. Its calibrated FAIR score is 44/100.
Abstract
Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq) technologies, datasets, and analysis methods. Since 2016, the scRNA-tools database has cataloged software tools for analyzing scRNA-seq data. With the number of tools in the database passing 1000, we provide an update on the state of the project and the field. This data shows the evolution of the field and a change of focus from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find that open science practices reward developers with increased recognition and help accelerate the field.
›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
0.776
From this paper's citation signal
Citation Network Contribution
3.7
From 119 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.
- Spatial reconstruction of single-cell gene expression dataNature Biotechnology20157,529 citationsDataRank 1.3
- Snakemake—a scalable bioinformatics workflow engineBioinformatics20123,091 citationsDataRank 1.2
- Generalizing RNA velocity to transient cell states through dynamical modelingNature Biotechnology20203,016 citationsDataRank 1.2
- Best practices for single-cell analysis across modalitiesNature Reviews Genetics20231,013 citationsDataRank 1.0
- scGen predicts single-cell perturbation responsesNature Methods2019666 citationsDataRank 0.975
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 (119 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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