摘要
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 characteris- tics of scRNA-seq data and primary objectives of single-cell studies.
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 characteris- tics of scRNA-seq data and primary objectives of single-cell studies.
基金
supported by Baylor Research Institute start-up funding,USA to WL
