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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-02-01
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| Series: | Genomics, Proteomics & Bioinformatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1672022916000437 |
| Summary: | 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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| ISSN: | 1672-0229 |