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Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data.

Lixiang Zhang¹, Lin Lin², Jia Li¹

¹Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania, United States of America.

Plos Computational Biology

|April 17, 2023

Summary

This summary is machine-generated.

We introduce CPS-merge analysis, a novel multi-view clustering method. It effectively integrates complementary information across views, outperforming existing methods on single-cell datasets.

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Area of Science:

Computational Biology
Data Science
Bioinformatics

Background:

Multi-view data integration is crucial for scientific discovery across diverse fields.
Existing multi-view clustering methods have limitations, including data pooling requirements and ignoring complementary information.
There is a need for advanced methods that leverage both consensus and complementary effects between data views.

Purpose of the Study:

To develop a new multi-view clustering approach, CPS-merge analysis.
To overcome limitations of existing methods regarding data pooling and algorithmic restrictions.
To account for both consensus and complementary information across multiple data views.

Main Methods:

CPS-merge analysis merges single-view clusters using the Cartesian product of labels.
Clustering stability is maximized by the CPS analysis, guiding the merge process.
The method quantifies individual view contributions to cluster formation and integrates easily into existing pipelines.

Main Results:

CPS-merge analysis effectively integrates complementary information between views.
The approach outperforms state-of-the-art methods on single-cell datasets.
It allows for the application of advanced single-view clustering algorithms.

Conclusions:

CPS-merge analysis offers a flexible and powerful approach to multi-view clustering.
The method enhances discovery by effectively utilizing multi-view data characteristics.
It provides a significant advancement over existing ensemble clustering techniques.