title: Single-cell Transcriptome Study as Big Data.
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.
authors.affiliation: Genomics and Bioinformatics Lab, Baylor Institute for Immunology Research, Dallas, TX 75204, USA.
authors.forename: Pingjian
authors.initials: P
authors.lastname: Yu
authors.affiliation: Genomics and Bioinformatics Lab, Baylor Institute for Immunology Research, Dallas, TX 75204, USA. Electronic address: Wei.Lin@BaylorHealth.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Lin
mesh_terms: D000818:Animals; D019295:Computational Biology; D030541:Databases, Genetic; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D012313:RNA / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA; D059010:Single-Cell Analysis; D059467:Transcriptome
chemical_list: D012313:RNA
keywords: Big data; RNA-seq; Signal normalization; Single cell; Transcriptional heterogeneity