Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
2822480 | Genomics, Proteomics & Bioinformatics | 2016 | 10 Pages |
Abstract
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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Authors
Pingjian Yu, Wei Lin,