{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T16:38:46Z","timestamp":1775493526915,"version":"3.50.1"},"reference-count":9,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Drop-seq has recently emerged as a powerful technology to analyze gene expression from thousands of individual cells simultaneously. Currently, Drop-seq technology requires refinement and quality control (QC) steps are critical for such data analysis. There is a strong need for a convenient and comprehensive approach to obtain dedicated QC and to determine the relationships between cells for ultra-high-dimensional datasets.<\/jats:p>\n               <jats:p>Results: We developed Dr.seq, a QC and analysis pipeline for Drop-seq data. By applying this pipeline, Dr.seq 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. We assessed Dr.seq on simulated and published Drop-seq data. Both assessments exhibit reliable results. Overall, Dr.seq is a comprehensive QC and analysis pipeline designed for Drop-seq data that is easily extended to other droplet-based data types.<\/jats:p>\n               <jats:p>Availability and Implementation: Dr.seq is freely available at: http:\/\/www.tongji.edu.cn\/\u223czhanglab\/drseq and https:\/\/bitbucket.org\/tarela\/drseq<\/jats:p>\n               <jats:p>Contact: \u00a0yzhang@tongji.edu.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw174","type":"journal-article","created":{"date-parts":[[2016,4,5]],"date-time":"2016-04-05T00:43:26Z","timestamp":1459817006000},"page":"2221-2223","source":"Crossref","is-referenced-by-count":10,"title":["Dr.seq: a quality control and analysis pipeline for droplet sequencing"],"prefix":"10.1093","volume":"32","author":[{"given":"Xiao","family":"Huo","sequence":"first","affiliation":[{"name":"School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China"}]},{"given":"Sheng\u2019en","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China"}]},{"given":"Chengchen","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Shanghai Key Laboratory of Signaling and Disease Research, Tongji University, Shanghai 20092, China"}]}],"member":"286","published-online":{"date-parts":[[2016,3,15]]},"reference":[{"key":"2023020112451687100_btw174-B1","first-page":"732","article-title":"Measures of association for cross-classification","volume":"49","author":"Goodman","year":"1954","journal-title":"J. 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