Universal prediction of cell-cycle position using transfer learning.
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
AuthorsZheng, Shijie C
Augustin, Jonathan J
Carosso, Giovanni A
Bjornsson, Hans T
Goff, Loyal A
Hansen, Kasper D
MetadataShow full item record
CitationZheng SC, Stein-O'Brien G, Augustin JJ, et al. Universal prediction of cell-cycle position using transfer learning. Genome Biol. 2022;23(1):41. Published 2022 Jan 31. doi:10.1186/s13059-021-02581-y
AbstractBackground: The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Results: Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays. Conclusions: Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data. Keywords: Cell cycle; Single-cell RNA-sequencing; Transfer learning.
DescriptionTo access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Download
Rights© 2021. The Author(s).
- Integration and transfer learning of single-cell transcriptomes via cFIT.
- Authors: Peng M, Li Y, Wamsley B, Wei Y, Roeder K
- Issue date: 2021 Mar 9
- Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.
- Authors: Stein-O'Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q, Sealfon R, Liu S, Qian J, Colantuoni C, Blackshaw S, Goff LA, Fertig EJ
- Issue date: 2019 May 22
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
- Authors: Mieth B, Hockley JRF, Görnitz N, Vidovic MM, Müller KR, Gutteridge A, Ziemek D
- Issue date: 2019 Dec 30
- CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments.
- Authors: Lieberman Y, Rokach L, Shay T
- Issue date: 2018
- scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data.
- Authors: Nguyen V, Griss J
- Issue date: 2022 Jan 17