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- Publisher Website: 10.1109/JBHI.2019.2944865
- Scopus: eid_2-s2.0-85086236259
- PMID: 31581101
- WOS: WOS:000542956600029
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Article: Pathway-based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests
Title | Pathway-based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests |
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Authors | |
Keywords | Radio frequency Sociology Statistics Vegetation Gene expression |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221020 |
Citation | IEEE Journal of Biomedical and Health Informatics, 2020, v. 24 n. 6, p. 1814-1822 How to Cite? |
Abstract | Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell populations for scRNA-Seq data, few existing methods have utilized machine learning techniques to investigate functional pathways in classifying heterogeneous cell populations. As genes often work interactively at the pathway level, studying the cellular heterogeneity based on pathways can facilitate the interpretation of biological functions of different cell populations. In this paper, we propose a pathway-based analytic framework using Random Forests (RF) to identify discriminative functional pathways related to cellular heterogeneity as well as to cluster cell populations for scRNA-Seq data. We further propose a novel method to construct gene-gene interactions (GGIs) networks using RF that illustrates important GGIs in differentiating cell populations. The co-occurrence of genes in different discriminative pathways and ‘cross-talk’ genes connecting those pathways are also illustrated in our networks. Our novel pathway-based framework clusters cell populations, prioritizes important pathways, highlights GGIs and pivotal genes bridging cross-talked pathways, and groups co-functional genes in networks. These features allow biomedical researchers to better understand the functional heterogeneity of different cell populations and to pinpoint important genes driving heterogeneous cellular functions. |
Persistent Identifier | http://hdl.handle.net/10722/281771 |
ISSN | 2023 Impact Factor: 6.7 2023 SCImago Journal Rankings: 1.964 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, H | - |
dc.contributor.author | Sham, P | - |
dc.contributor.author | Tong, T | - |
dc.contributor.author | Pang, H | - |
dc.date.accessioned | 2020-03-27T04:22:21Z | - |
dc.date.available | 2020-03-27T04:22:21Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Journal of Biomedical and Health Informatics, 2020, v. 24 n. 6, p. 1814-1822 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | http://hdl.handle.net/10722/281771 | - |
dc.description.abstract | Single-cell RNA-Sequencing (scRNA-Seq), an advanced sequencing technique, enables biomedical researchers to characterize cell-specific gene expression profiles. Although studies have adapted machine learning algorithms to cluster different cell populations for scRNA-Seq data, few existing methods have utilized machine learning techniques to investigate functional pathways in classifying heterogeneous cell populations. As genes often work interactively at the pathway level, studying the cellular heterogeneity based on pathways can facilitate the interpretation of biological functions of different cell populations. In this paper, we propose a pathway-based analytic framework using Random Forests (RF) to identify discriminative functional pathways related to cellular heterogeneity as well as to cluster cell populations for scRNA-Seq data. We further propose a novel method to construct gene-gene interactions (GGIs) networks using RF that illustrates important GGIs in differentiating cell populations. The co-occurrence of genes in different discriminative pathways and ‘cross-talk’ genes connecting those pathways are also illustrated in our networks. Our novel pathway-based framework clusters cell populations, prioritizes important pathways, highlights GGIs and pivotal genes bridging cross-talked pathways, and groups co-functional genes in networks. These features allow biomedical researchers to better understand the functional heterogeneity of different cell populations and to pinpoint important genes driving heterogeneous cellular functions. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221020 | - |
dc.relation.ispartof | IEEE Journal of Biomedical and Health Informatics | - |
dc.rights | IEEE Journal of Biomedical and Health Informatics. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Radio frequency | - |
dc.subject | Sociology | - |
dc.subject | Statistics | - |
dc.subject | Vegetation | - |
dc.subject | Gene expression | - |
dc.title | Pathway-based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests | - |
dc.type | Article | - |
dc.identifier.email | Sham, P: pcsham@hku.hk | - |
dc.identifier.email | Pang, H: herbpang@hku.hk | - |
dc.identifier.authority | Sham, P=rp00459 | - |
dc.identifier.authority | Pang, H=rp01857 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JBHI.2019.2944865 | - |
dc.identifier.pmid | 31581101 | - |
dc.identifier.scopus | eid_2-s2.0-85086236259 | - |
dc.identifier.hkuros | 309585 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 1814 | - |
dc.identifier.epage | 1822 | - |
dc.identifier.isi | WOS:000542956600029 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2168-2194 | - |