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scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data

Authors :
Xiangling Ji
Danielle Tsao
Kailun Bai
Min Tsao
Li Xing
Xuekui Zhang
Source :
Bioinformatics Advances. 3
Publication Year :
2023
Publisher :
Oxford University Press (OUP), 2023.

Abstract

MotivationSingle-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell-type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, no current cell annotation method explicitly utilizes dropout information. Fully utilizing dropout information motivated this work.ResultsWe present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene’s marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using 14 real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotate’s misclassified cells differ greatly from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy.Availability and implementationWe implemented scAnnotate as an R package and made it publicly available from CRAN: https://cran.r-project.org/package=scAnnotate.Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Subjects

Subjects :
General Medicine

Details

ISSN :
26350041
Volume :
3
Database :
OpenAIRE
Journal :
Bioinformatics Advances
Accession number :
edsair.doi...........dc719a120c3d444d4876f327fbd10278