Teschendorff, AE;
Maity, AK;
Hu, X;
Weiyan, C;
Lechner, M;
(2021)
Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data.
Bioinformatics
, 37
(11)
pp. 1528-1534.
10.1093/bioinformatics/btaa987.
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Abstract
Motivation: An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. Results: Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts.
| Type: | Article |
|---|---|
| Title: | Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/bioinformatics/btaa987 |
| Publisher version: | https://doi.org/10.1093/bioinformatics/btaa987 |
| Language: | English |
| 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. |
| Keywords: | Cell Differentiation, Gene Expression Profiling, RNA, Small Cytoplasmic, Sequence Analysis, RNA, Single-Cell Analysis, Software |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10179126 |
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