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A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data

Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potentia...

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Autores principales: Upadhyay, Piu, Ray, Sumanta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043858/
https://www.ncbi.nlm.nih.gov/pubmed/35495159
http://dx.doi.org/10.3389/fgene.2022.788832
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author Upadhyay, Piu
Ray, Sumanta
author_facet Upadhyay, Piu
Ray, Sumanta
author_sort Upadhyay, Piu
collection PubMed
description Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potential marker genes in each cluster is cumbersome, which impedes the systematic analysis of single-cell RNA sequencing data. To address this challenge, we proposed a framework based on regularized multi-task learning (RMTL) that enables us to simultaneously learn the subpopulation associated with a particular cell type. Learning the structure of subpopulations is treated as a separate task in the multi-task learner. Regularization is used to modulate the multi-task model (e.g., W (1), W (2), … W ( t )) jointly, according to the specific prior. For validating our model, we trained it with reference data constructed from a single-cell RNA sequencing experiment and applied it to a query dataset. We also predicted completely independent data (the query dataset) from the reference data which are used for training. We have checked the efficacy of the proposed method by comparing it with other state-of-the-art techniques well known for cell type detection. Results revealed that the proposed method performed accurately in detecting the cell type in scRNA-seq data and thus can be utilized as a useful tool in the scRNA-seq pipeline.
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spelling pubmed-90438582022-04-28 A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data Upadhyay, Piu Ray, Sumanta Front Genet Genetics Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potential marker genes in each cluster is cumbersome, which impedes the systematic analysis of single-cell RNA sequencing data. To address this challenge, we proposed a framework based on regularized multi-task learning (RMTL) that enables us to simultaneously learn the subpopulation associated with a particular cell type. Learning the structure of subpopulations is treated as a separate task in the multi-task learner. Regularization is used to modulate the multi-task model (e.g., W (1), W (2), … W ( t )) jointly, according to the specific prior. For validating our model, we trained it with reference data constructed from a single-cell RNA sequencing experiment and applied it to a query dataset. We also predicted completely independent data (the query dataset) from the reference data which are used for training. We have checked the efficacy of the proposed method by comparing it with other state-of-the-art techniques well known for cell type detection. Results revealed that the proposed method performed accurately in detecting the cell type in scRNA-seq data and thus can be utilized as a useful tool in the scRNA-seq pipeline. Frontiers Media S.A. 2022-04-13 /pmc/articles/PMC9043858/ /pubmed/35495159 http://dx.doi.org/10.3389/fgene.2022.788832 Text en Copyright © 2022 Upadhyay and Ray. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Upadhyay, Piu
Ray, Sumanta
A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data
title A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data
title_full A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data
title_fullStr A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data
title_full_unstemmed A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data
title_short A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data
title_sort regularized multi-task learning approach for cell type detection in single-cell rna sequencing data
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043858/
https://www.ncbi.nlm.nih.gov/pubmed/35495159
http://dx.doi.org/10.3389/fgene.2022.788832
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