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Functional annotation-driven unsupervised clustering of single-cell transcriptomes

View ORCID ProfileKeita Iida, View ORCID ProfileJumpei Kondo, View ORCID ProfileMasahiro Inoue, View ORCID ProfileMariko Okada
doi: https://doi.org/10.1101/2021.06.09.447731
Keita Iida
1Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
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  • For correspondence: kiida@protein.osaka-u.ac.jp
Jumpei Kondo
2Department of Biochemistry, Osaka International Cancer Institute, Osaka 541-8567, Japan
3Department of Clinical Bio-resource Research and Development, Graduate School of Medicine Kyoto University, Kyoto 606-8501, Japan
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Masahiro Inoue
2Department of Biochemistry, Osaka International Cancer Institute, Osaka 541-8567, Japan
3Department of Clinical Bio-resource Research and Development, Graduate School of Medicine Kyoto University, Kyoto 606-8501, Japan
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Mariko Okada
1Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
4Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, 567-0085, Japan
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Abstract

Single-cell RNA sequencing (scRNA-seq) analysis has significantly advanced our knowledge of functional states of cells. By analyzing scRNA-seq data, we can deconvolve individual cell states into thousands of gene expression profiles, allowing us to perform cell clustering, and identify significant genes for each cluster. However, interpreting these results remains challenging. Here, we present a novel scRNA-seq analysis pipeline named ASURAT, which simultaneously performs unsupervised cell clustering and biological interpretation in semi-automatic manner, in terms of cell type and various biological functions. We validate the reliable clustering performance of ASURAT by comparing it with existing methods, using six published scRNA-seq datasets from healthy donors and cancer patients. Furthermore, we applied ASURAT to patient-derived scRNA-seq datasets including small cell lung cancers, finding some putative cancer subpopulations showing different resistance mechanisms. ASURAT is expected to open new means of scRNA-seq analysis, focusing more on “biological meaning” than conventional gene-based analyses.

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 June 10, 2021.
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Functional annotation-driven unsupervised clustering of single-cell transcriptomes
Keita Iida, Jumpei Kondo, Masahiro Inoue, Mariko Okada
bioRxiv 2021.06.09.447731; doi: https://doi.org/10.1101/2021.06.09.447731
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Functional annotation-driven unsupervised clustering of single-cell transcriptomes
Keita Iida, Jumpei Kondo, Masahiro Inoue, Mariko Okada
bioRxiv 2021.06.09.447731; doi: https://doi.org/10.1101/2021.06.09.447731

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