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Dr.seq: a quality control and analysis pipeline for droplet sequencing
Dr.seq is a QC and analysis pipeline for Drop-seq data.
Drop-seq is a powerful technology to analyze gene expression from thousands of individual cells simultaneously. It only requires 12 hours to prepare 10,000 single-cell libraries for sequencing (Macosko, et al., 2015; Klein, et al., 2015). The breakthrough of this technology provides many applications, such as the deconstruction of a cell population, the detection of rare cell types and the inference of interactions between genes.
Here we provided a powerful pipeline, Dr.seq, for QC and analysis of Drop-seq data.
With the help of Dr.seq, users can easily get through the basic analysis steps for RNAseq and our new developed analysis specifically for Drop-seq data, including STAMP selection(real cell selection, removing bias and empty barcodes), dimentional reduction(PCA & t-SNE) and cluster single cells to different groups (group number was automatically decided according to data preference).
Dr.seq is user friendly and DON'T have ANY requirements except basic Python and R.
We also provided a detailed "Quick Start" page for users to get familiar with Dr.seq within 3 steps ( on a linux/MacOS system with ONLY python and R installed.).
The latest version of Dr.seq software is available at bitbucket: https://bitbucket.org/Tarela/drseq.The related paper is accepted by Bioinformatics.
And the Quick Start toturial available here:
http://www.tongji.edu.cn/~zhanglab/drseq/index.html
By applying this pipeline, Dr.seq takes two sequencing files as input (data_1.fastq for barcode information, data_2.fastq for reads information, see our testing data and Manual section for more information) and provides four groups of QC measurements for given Drop-seq data, including reads level, bulk-cell level, individual-cell level and cell-clustering level QC.
I hope this proves useful to some people here. Any feedbacks will be appreciated.
Chengchen
Drop-seq is a powerful technology to analyze gene expression from thousands of individual cells simultaneously. It only requires 12 hours to prepare 10,000 single-cell libraries for sequencing (Macosko, et al., 2015; Klein, et al., 2015). The breakthrough of this technology provides many applications, such as the deconstruction of a cell population, the detection of rare cell types and the inference of interactions between genes.
Here we provided a powerful pipeline, Dr.seq, for QC and analysis of Drop-seq data.
With the help of Dr.seq, users can easily get through the basic analysis steps for RNAseq and our new developed analysis specifically for Drop-seq data, including STAMP selection(real cell selection, removing bias and empty barcodes), dimentional reduction(PCA & t-SNE) and cluster single cells to different groups (group number was automatically decided according to data preference).
Dr.seq is user friendly and DON'T have ANY requirements except basic Python and R.
We also provided a detailed "Quick Start" page for users to get familiar with Dr.seq within 3 steps ( on a linux/MacOS system with ONLY python and R installed.).
The latest version of Dr.seq software is available at bitbucket: https://bitbucket.org/Tarela/drseq.The related paper is accepted by Bioinformatics.
And the Quick Start toturial available here:
http://www.tongji.edu.cn/~zhanglab/drseq/index.html
By applying this pipeline, Dr.seq takes two sequencing files as input (data_1.fastq for barcode information, data_2.fastq for reads information, see our testing data and Manual section for more information) and provides four groups of QC measurements for given Drop-seq data, including reads level, bulk-cell level, individual-cell level and cell-clustering level QC.
I hope this proves useful to some people here. Any feedbacks will be appreciated.
Chengchen
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