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inClust: a general framework for clustering that integrates data from multiple sources

Lifei Wang, Rui Nie, Zhang Zhang, Weiwei Gu, Shuo Wang, Anqi Wang, Jiang Zhang, Jun Cai
doi: https://doi.org/10.1101/2022.05.27.493706
Lifei Wang
1Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
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  • For correspondence: fly99wang@gmail.com zhangjiang@bnu.edu.cn juncai@big.ac.cn
Rui Nie
2China National Center for Bioinformation, Beijing, China
3Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
4University of Chinese Academy of Sciences, Beijing, 100049, China
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Zhang Zhang
5School of Systems Science, Beijing Normal University, Beijing, 100875, China
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Weiwei Gu
6College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China
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Shuo Wang
5School of Systems Science, Beijing Normal University, Beijing, 100875, China
7D-ITET, ETH Zurich, Zurich, Switzerland
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Anqi Wang
1Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
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Jiang Zhang
5School of Systems Science, Beijing Normal University, Beijing, 100875, China
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  • For correspondence: fly99wang@gmail.com zhangjiang@bnu.edu.cn juncai@big.ac.cn
Jun Cai
2China National Center for Bioinformation, Beijing, China
3Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
4University of Chinese Academy of Sciences, Beijing, 100049, China
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  • For correspondence: fly99wang@gmail.com zhangjiang@bnu.edu.cn juncai@big.ac.cn
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Abstract

Clustering is one of the most commonly used methods in single-cell RNA sequencing (scRNA-seq) data analysis and other fields of biology. Traditional clustering methods usually use data from a single source as the input (e.g. scRNA-seq data). However, as the data become more and more complex and contain information from multiple sources, a clustering method that could integrate multiple data is required. Here, we present inClust (integrated clustering), a clustering method that integrates information from multiple sources based on variational autoencoder and vector arithmetic in latent space. inClust perform information integration and clustering jointly, meanwhile it could utilize the labeling information from data as regulation information. It is a flexible framework that can accomplish different tasks under different modes, ranging from supervised to unsupervised. We demonstrate the capability of inClust in the tasks of conditional out-of-distribution generation under supervised mode; label transfer under semi-supervised mode and guided clustering mode; spatial domain identification under unsupervised mode. inClust performs well in all tasks, indicating that it is an excellent general framework for clustering and task-related clustering in the era of multi-omics.

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 May 29, 2022.
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inClust: a general framework for clustering that integrates data from multiple sources
Lifei Wang, Rui Nie, Zhang Zhang, Weiwei Gu, Shuo Wang, Anqi Wang, Jiang Zhang, Jun Cai
bioRxiv 2022.05.27.493706; doi: https://doi.org/10.1101/2022.05.27.493706
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inClust: a general framework for clustering that integrates data from multiple sources
Lifei Wang, Rui Nie, Zhang Zhang, Weiwei Gu, Shuo Wang, Anqi Wang, Jiang Zhang, Jun Cai
bioRxiv 2022.05.27.493706; doi: https://doi.org/10.1101/2022.05.27.493706

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