Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1783408941275807744 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6449882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sunshiquan afastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata AT chenyabo afastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata AT liuyang afastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata AT shangxuequn afastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata AT sunshiquan fastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata AT chenyabo fastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata AT liuyang fastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata AT shangxuequn fastandefficientcountbasedmatrixfactorizationmethodfordetectingcelltypesfromsinglecellrnaseqdata |