Design Of A Single Cell Transcriptome Data Analysis Framework Based On Nextflow

As single-cell transcriptome sequencing(scRNA-seq)technology becomes more mature,multiple tools are available for each step of scRNA-seq data analysis.For researchers,how to efficiently install and use scRNA-seq data analysis tools has become increasingly challenging.The development of scRNA-seq technology has enabled scientists to analyze gene expression in thousands of individual cells simultaneously.Although the analysis of scRNA-seq data is becoming more standard,it also brings a series of new challenges.On the one hand,the tools available in this area are overwhelming,and the technical documentation of the tools is diverse,the dependent environment is complex,and it is difficult to install and use.On the other hand,for researchers who are not involved in computer related specialties,the flexible use of these analysis tools in a scalable and repeatable manner will face great difficulties.With the development of scRNA-seq technology today,existing tools are increasingly unable to meet people's needs for flexible and user-friendly data preprocessing tools.The framework designed in this article provides an out-of-the-box solution for analyzing scRNA-seq data.The framework combines the Nextflow scripting language and the Anaconda package manager to generate modular computing workflows.In addition,I used Docker technology to further simplify the installation of the environment.This framework not only makes it easy to develop highly flexible and repeatable pipelines,and extend and maintain these pipelines in multiple computing environments.This article encapsulates seven scRNA-seq data processing flows,which are designed to help users solve problems such as difficult installation environments and clumsy analysis of data.The developed framework can be flexibly extended to support future scRNA pipelines developed in any languages for scRNA-seq data analysis.