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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)-baseddoi:10.1016/j.gpb.2016.01.005 pmid:26876720 pmcid:PMC4792842 fatcat:g6zc5bsl4vhynhimaxpoi3tatq