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Single-cell Transcriptome Study as Big Data
The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the s...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792842/ https://www.ncbi.nlm.nih.gov/pubmed/26876720 http://dx.doi.org/10.1016/j.gpb.2016.01.005 |
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author | Yu, Pingjian Lin, Wei |
author_facet | Yu, Pingjian Lin, Wei |
author_sort | Yu, Pingjian |
collection | PubMed |
description | The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. |
format | Online Article Text |
id | pubmed-4792842 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-47928422016-03-24 Single-cell Transcriptome Study as Big Data Yu, Pingjian Lin, Wei Genomics Proteomics Bioinformatics Review The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. Elsevier 2016-02 2016-02-11 /pmc/articles/PMC4792842/ /pubmed/26876720 http://dx.doi.org/10.1016/j.gpb.2016.01.005 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Yu, Pingjian Lin, Wei Single-cell Transcriptome Study as Big Data |
title | Single-cell Transcriptome Study as Big Data |
title_full | Single-cell Transcriptome Study as Big Data |
title_fullStr | Single-cell Transcriptome Study as Big Data |
title_full_unstemmed | Single-cell Transcriptome Study as Big Data |
title_short | Single-cell Transcriptome Study as Big Data |
title_sort | single-cell transcriptome study as big data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792842/ https://www.ncbi.nlm.nih.gov/pubmed/26876720 http://dx.doi.org/10.1016/j.gpb.2016.01.005 |
work_keys_str_mv | AT yupingjian singlecelltranscriptomestudyasbigdata AT linwei singlecelltranscriptomestudyasbigdata |