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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...

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Detalles Bibliográficos
Autores principales: Yu, Pingjian, Lin, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
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.
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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
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