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Identification of cell-type marker genes from plant single-cell RNA-seq data using machine learning

Haidong Yan, Qi Song, Jiyoung Lee, John Schiefelbein, Song Li
doi: https://doi.org/10.1101/2020.11.22.393165
Haidong Yan
1School of Plant and Environmental Sciences (SPES)
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Qi Song
1School of Plant and Environmental Sciences (SPES)
2Graduate program in Genetics, Bioinformatics and Computational Biology (GBCB)
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Jiyoung Lee
1School of Plant and Environmental Sciences (SPES)
2Graduate program in Genetics, Bioinformatics and Computational Biology (GBCB)
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John Schiefelbein
3Department of Molecular, Cellular, and Developmental Biology, University of Michigan. Ann Arbor, MI 48109
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Song Li
1School of Plant and Environmental Sciences (SPES)
2Graduate program in Genetics, Bioinformatics and Computational Biology (GBCB)
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  • For correspondence: songli@vt.edu
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Abstract

An essential step of single-cell RNA sequencing analysis is to classify specific cell types with marker genes in order to dissect the biological functions of each individual cell. In this study, we integrated five published scRNA-seq datasets from the Arabidopsis root containing over 25,000 cells and 17 cell clusters. We have compared the performance of seven machine learning methods in classifying these cell types, and determined that the random forest and support vector machine methods performed best. Using feature selection with these two methods and a correlation method, we have identified 600 new marker genes for 10 root cell types, and more than 70% of these machine learning-derived marker genes were not identified before. We found that these new markers not only can assign cell types consistently as the previously known cell markers, but also performed better than existing markers in several evaluation metrics including accuracy and sensitivity. Markers derived by the random forest method, in particular, were expressed in 89-98% of cells in endodermis, trichoblast, and cortex clusters, which is a 29-67% improvement over known markers. Finally, we have found 111 new orthologous marker genes for the trichoblast in five plant species, which expands the number of marker genes by 58-170% in non-Arabidopsis plants. Our results represent a new approach to identify cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 22, 2020.
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Identification of cell-type marker genes from plant single-cell RNA-seq data using machine learning
Haidong Yan, Qi Song, Jiyoung Lee, John Schiefelbein, Song Li
bioRxiv 2020.11.22.393165; doi: https://doi.org/10.1101/2020.11.22.393165
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Identification of cell-type marker genes from plant single-cell RNA-seq data using machine learning
Haidong Yan, Qi Song, Jiyoung Lee, John Schiefelbein, Song Li
bioRxiv 2020.11.22.393165; doi: https://doi.org/10.1101/2020.11.22.393165

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