A curated database reveals trends in single cell transcriptomics
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.
Additional Information
© The Author(s) 2020. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Received: 14 October 2019; Revision received: 10 July 2020; Editorial decision: 03 August 2020; Accepted: 16 November 2020; Published: 28 November 2020. We thank Carlos Talavera-López for helpful feedback on the manuscript. Cloud infrastructure was partially funded through the Google Cloud Platform research credits program. The work was partly funded by NIH U19MH114830.Attached Files
Published - baaa073.pdf
Submitted - 742304.full.pdf
Supplemental Material - baaa073_supp.zip
Supplemental Material - media-1.tsv
Files
Additional details
- Alternative title
- A curated database reveals trends in single-cell transcriptomics
- PMCID
- PMC7698659
- Eprint ID
- 98067
- Resolver ID
- CaltechAUTHORS:20190821-092511308
- Google Cloud Platform
- NIH
- U19MH114830
- Created
-
2019-08-21Created from EPrint's datestamp field
- Updated
-
2023-06-01Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering (BBE)