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A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data

BACKGROUND: Single-cell RNA sequencing (scRNAseq) data always involves various unwanted variables, which would be able to mask the true signal to identify cell-types. More efficient way of dealing with this issue is to extract low dimension information from high dimensional gene expression data to r...

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Detalles Bibliográficos
Autores principales: Sun, Shiquan, Chen, Yabo, Liu, Yang, Shang, Xuequn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449882/
https://www.ncbi.nlm.nih.gov/pubmed/30953530
http://dx.doi.org/10.1186/s12918-019-0699-6
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author Sun, Shiquan
Chen, Yabo
Liu, Yang
Shang, Xuequn
author_facet Sun, Shiquan
Chen, Yabo
Liu, Yang
Shang, Xuequn
author_sort Sun, Shiquan
collection PubMed
description BACKGROUND: Single-cell RNA sequencing (scRNAseq) data always involves various unwanted variables, which would be able to mask the true signal to identify cell-types. More efficient way of dealing with this issue is to extract low dimension information from high dimensional gene expression data to represent cell-type structure. In the past two years, several powerful matrix factorization tools were developed for scRNAseq data, such as NMF, ZIFA, pCMF and ZINB-WaVE. But the existing approaches either are unable to directly model the raw count of scRNAseq data or are really time-consuming when handling a large number of cells (e.g. n>500). RESULTS: In this paper, we developed a fast and efficient count-based matrix factorization method (single-cell negative binomial matrix factorization, scNBMF) based on the TensorFlow framework to infer the low dimensional structure of cell types. To make our method scalable, we conducted a series of experiments on three public scRNAseq data sets, brain, embryonic stem, and pancreatic islet. The experimental results show that scNBMF is more powerful to detect cell types and 10 - 100 folds faster than the scRNAseq bespoke tools. CONCLUSIONS: In this paper, we proposed a fast and efficient count-based matrix factorization method, scNBMF, which is more powerful for detecting cell type purposes. A series of experiments were performed on three public scRNAseq data sets. The results show that scNBMF is a more powerful tool in large-scale scRNAseq data analysis. scNBMF was implemented in R and Python, and the source code are freely available at https://github.com/sqsun.
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spelling pubmed-64498822019-04-15 A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data Sun, Shiquan Chen, Yabo Liu, Yang Shang, Xuequn BMC Syst Biol Research BACKGROUND: Single-cell RNA sequencing (scRNAseq) data always involves various unwanted variables, which would be able to mask the true signal to identify cell-types. More efficient way of dealing with this issue is to extract low dimension information from high dimensional gene expression data to represent cell-type structure. In the past two years, several powerful matrix factorization tools were developed for scRNAseq data, such as NMF, ZIFA, pCMF and ZINB-WaVE. But the existing approaches either are unable to directly model the raw count of scRNAseq data or are really time-consuming when handling a large number of cells (e.g. n>500). RESULTS: In this paper, we developed a fast and efficient count-based matrix factorization method (single-cell negative binomial matrix factorization, scNBMF) based on the TensorFlow framework to infer the low dimensional structure of cell types. To make our method scalable, we conducted a series of experiments on three public scRNAseq data sets, brain, embryonic stem, and pancreatic islet. The experimental results show that scNBMF is more powerful to detect cell types and 10 - 100 folds faster than the scRNAseq bespoke tools. CONCLUSIONS: In this paper, we proposed a fast and efficient count-based matrix factorization method, scNBMF, which is more powerful for detecting cell type purposes. A series of experiments were performed on three public scRNAseq data sets. The results show that scNBMF is a more powerful tool in large-scale scRNAseq data analysis. scNBMF was implemented in R and Python, and the source code are freely available at https://github.com/sqsun. BioMed Central 2019-04-05 /pmc/articles/PMC6449882/ /pubmed/30953530 http://dx.doi.org/10.1186/s12918-019-0699-6 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Sun, Shiquan
Chen, Yabo
Liu, Yang
Shang, Xuequn
A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data
title A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data
title_full A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data
title_fullStr A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data
title_full_unstemmed A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data
title_short A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data
title_sort fast and efficient count-based matrix factorization method for detecting cell types from single-cell rnaseq data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449882/
https://www.ncbi.nlm.nih.gov/pubmed/30953530
http://dx.doi.org/10.1186/s12918-019-0699-6
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