Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data.
Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data.
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Dr. seq2：并行单细胞转录组和表观基因组数据的质量控制和分析流程
DOI：
10.1371/journal.pone.0180583
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Zhao C;Hu S;Huo X;Zhang Y
越来越多的单细胞转录组和表观基因组技术，包括单细胞ATAC-seq（scATAC-seq），最近已被开发为同时分析许多单个细胞特征的强大工具。然而，这些方法和软件是针对一种特定的数据类型设计的，并且仅针对单细胞转录组数据。需要一种用于表观基因组数据和多种类型的转录组数据的系统方法来控制数据质量并对这些超高维转录组和表观基因组数据集进行细胞间异质性分析。在这里，我们开发了Dr.seq2，这是一个质量控制（QC）和分析管道，用于多种类型的单细胞转录组和表观基因组数据，包括scATAC-seq和Drop-ChIP数据。该管道的应用提供了四组QC测量和不同的分析，包括细胞异质性分析。Dr.seq2在已发表的单细胞转录组和表观基因组数据集上产生了可靠的结果。总的来说，Dr.seq2是一个系统和全面的QC和分析管道，专为并行单细胞转录组和表观基因组数据而设计。Dr.seq2可在http://www.tongji.edu.cn/~zhanglab/drseq2/和https://github.com/ChengchenZhao/DrSeq2上免费获得。
An increasing number of single cell transcriptome and epigenome technologies, including single cell ATAC-seq (scATAC-seq), have been recently developed as powerful tools to analyze the features of many individual cells simultaneously. However, the methods and software were designed for one certain data type and only for single cell transcriptome data. A systematic approach for epigenome data and multiple types of transcriptome data is needed to control data quality and to perform cell-to-cell heterogeneity analysis on these ultra-high-dimensional transcriptome and epigenome datasets. Here we developed Dr.seq2, a Quality Control (QC) and analysis pipeline for multiple types of single cell transcriptome and epigenome data, including scATAC-seq and Drop-ChIP data. Application of this pipeline provides four groups of QC measurements and different analyses, including cell heterogeneity analysis. Dr.seq2 produced reliable results on published single cell transcriptome and epigenome datasets. Overall, Dr.seq2 is a systematic and comprehensive QC and analysis pipeline designed for parallel single cell transcriptome and epigenome data. Dr.seq2 is freely available at: http://www.tongji.edu.cn/~zhanglab/drseq2/ and https://github.com/ChengchenZhao/DrSeq2.