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. 2020 Nov 28:2020:baaa073.
doi: 10.1093/database/baaa073.

A curated database reveals trends in single-cell transcriptomics

Valentine Svensson  1 , Eduardo da Veiga Beltrame  1 , Lior Pachter  1
Affiliations

A curated database reveals trends in single-cell transcriptomics

Valentine Svensson et al. Database (Oxford). .

Abstract

The more than 1000 single-cell transcriptomics studies that have been published to date constitute a valuable and vast resource for biological discovery. While various 'atlas' projects have collated some of the associated datasets, most questions related to specific tissue types, species or other attributes of studies require identifying papers through manual and challenging literature search. To facilitate discovery with published single-cell transcriptomics data, we have assembled a near exhaustive, manually curated database of single-cell transcriptomics studies with key information: descriptions of the type of data and technologies used, along with descriptors of the biological systems studied. Additionally, the database contains summarized information about analysis in the papers, allowing for analysis of trends in the field. As an example, we show that the number of cell types identified in scRNA-seq studies is proportional to the number of cells analysed. Database URL: www.nxn.se/single-cell-studies/gui.

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Figures

Figure 1.
Figure 1.
Studies over time. (Upper) The number of single-cell transcriptomics studies published per month. (Lower) The number of scRNA-seq studies published per month stratified by method.
Figure 2.
Figure 2.
Scale of experiments and data over time. (Upper): The number of cells measured in a study, stratified by the measurement method. (Middle): The number of cells measured in scRNA-seq experiments, stratified by scRNA-seq protocol. (Lower): The aggregate number of cells measured per month.
Figure 3.
Figure 3.
Preprint usage over time. The number of studies published in a given month stratified by whether they at some point were deposited to bioRxiv. (Including studies currently only available on bioRxiv).
Figure 4.
Figure 4.
Popularity of forms of analysis over time. (Top) The number of studies doing clustering per month. (Middle) The number of studies using t-SNE per month. (Bottom) The number of studies doing pseudotime analysis per month.
Figure 5.
Figure 5.
Cluster and cell numbers. The number of cells studied versus the number of clusters or cell types reported in a study. Red curves correspond to linear regression stratified to five quantiles of ‘Reported cells total’.
See this image and copyright information in PMC

References

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