Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape

single-cell

rna-seq

database

Authors

Luke Zappia

Fabian J Theis

Date

October 29, 2021

Links

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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.

Citation

BibTeX citation:

@article{zappia2021,
  author = {Zappia, Luke and J Theis, Fabian},
  title = {Over 1000 Tools Reveal Trends in the Single-Cell {RNA-seq}
    Analysis Landscape},
  journal = {Genome biology},
  volume = {22},
  number = {1},
  pages = {301},
  date = {2021-10-29},
  url = {https://lazappi.id.au/publications/2021-zappia-1000-tools/},
  doi = {10.1186/s13059-021-02519-4},
  issn = {1465-6906},
  langid = {en},
  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.}
}

For attribution, please cite this work as:

Zappia, Luke, and Fabian J Theis. 2021. “Over 1000 Tools Reveal Trends in the Single-Cell RNA-Seq Analysis Landscape.” Genome Biology 22 (1): 301. https://doi.org/10.1186/s13059-021-02519-4.