Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape
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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
@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://doi.org/10.1186/s13059-021-02519-4},
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.}
}