File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Robust Joint Clustering of Multi-omics Single-cell Data via Multi-modal High-order Neighborhood Laplacian Matrix Optimization

TitleRobust Joint Clustering of Multi-omics Single-cell Data via Multi-modal High-order Neighborhood Laplacian Matrix Optimization
Authors
Issue Date1-Jul-2023
PublisherOxford University Press
Citation
Bioinformatics, 2023, v. 39, n. 7 How to Cite?
Abstract

Motivation

Simultaneous profiling of multi-omics single-cell data represents exciting technological advancements for understanding cellular states and heterogeneity. Cellular indexing of transcriptomes and epitopes by sequencing allowed for parallel quantification of cell-surface protein expression and transcriptome profiling in the same cells; methylome and transcriptome sequencing from single cells allows for analysis of transcriptomic and epigenomic profiling in the same individual cells. However, effective integration method for mining the heterogeneity of cells over the noisy, sparse, and complex multi-modal data is in growing need.

Results

In this article, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework for integrating the multi-omics single-cell data: scHoML. Hierarchical clustering method was presented for analyzing the optimal embedding representation and identifying cell clusters in a robust manner. This novel method by integrating high-order and multi-modal Laplacian matrices would robustly represent the complex data structures and allow for systematic analysis at the multi-omics single-cell level, thus promoting further biological discoveries.

Availability and implementation

Matlab code is available at https://github.com/jianghruc/scHoML.


Persistent Identifierhttp://hdl.handle.net/10722/330939
ISSN
2023 Impact Factor: 4.4
2023 SCImago Journal Rankings: 2.574
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, H-
dc.contributor.authorZhan, S-
dc.contributor.authorChing, W-
dc.contributor.authorChen, L-
dc.date.accessioned2023-09-21T06:51:18Z-
dc.date.available2023-09-21T06:51:18Z-
dc.date.issued2023-07-01-
dc.identifier.citationBioinformatics, 2023, v. 39, n. 7-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/330939-
dc.description.abstract<p>Motivation</p><p>Simultaneous profiling of multi-omics single-cell data represents exciting technological advancements for understanding cellular states and heterogeneity. Cellular indexing of transcriptomes and epitopes by sequencing allowed for parallel quantification of cell-surface protein expression and transcriptome profiling in the same cells; methylome and transcriptome sequencing from single cells allows for analysis of transcriptomic and epigenomic profiling in the same individual cells. However, effective integration method for mining the heterogeneity of cells over the noisy, sparse, and complex multi-modal data is in growing need.</p><p>Results</p><p>In this article, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework for integrating the multi-omics single-cell data: scHoML. Hierarchical clustering method was presented for analyzing the optimal embedding representation and identifying cell clusters in a robust manner. This novel method by integrating high-order and multi-modal Laplacian matrices would robustly represent the complex data structures and allow for systematic analysis at the multi-omics single-cell level, thus promoting further biological discoveries.</p><p>Availability and implementation</p><p>Matlab code is available at <a href="https://github.com/jianghruc/scHoML">https://github.com/jianghruc/scHoML</a>.</p>-
dc.languageeng-
dc.publisherOxford University Press-
dc.relation.ispartofBioinformatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleRobust Joint Clustering of Multi-omics Single-cell Data via Multi-modal High-order Neighborhood Laplacian Matrix Optimization-
dc.typeArticle-
dc.identifier.doi10.1093/bioinformatics/btad414-
dc.identifier.scopuseid_2-s2.0-85164259802-
dc.identifier.volume39-
dc.identifier.issue7-
dc.identifier.eissn1367-4811-
dc.identifier.isiWOS:001027790700001-
dc.identifier.issnl1367-4803-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats