Single-cell Transcriptome Study as Big Data

Genomics Proteomics Bioinformatics. 2016 Feb;14(1):21-30. doi: 10.1016/j.gpb.2016.01.005. Epub 2016 Feb 11.

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.

Keywords: Big data; RNA-seq; Signal normalization; Single cell; Transcriptional heterogeneity.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Computational Biology
  • Databases, Genetic
  • High-Throughput Nucleotide Sequencing
  • Humans
  • RNA / chemistry
  • RNA / genetics
  • RNA / metabolism
  • Sequence Analysis, RNA
  • Single-Cell Analysis
  • Transcriptome*

Substances

  • RNA