Bo Ding1, Lina Zheng1, Yun Zhu1, Nan Li1, Haiyang Jia2, Rizi Ai1, Andre Wildberg1, Wei Wang2. 1. Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA. 2. Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.
Abstract
UNLABELLED: A major roadblock towards accurate interpretation of single cell RNA-seq data is large technical noise resulted from small amount of input materials. The existing methods mainly aim to find differentially expressed genes rather than directly de-noise the single cell data. We present here a powerful but simple method to remove technical noise and explicitly compute the true gene expression levels based on spike-in ERCC molecules. AVAILABILITY AND IMPLEMENTATION: The software is implemented by R and the download version is available at http://wanglab.ucsd.edu/star/GRM. CONTACT: wei-wang@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: A major roadblock towards accurate interpretation of single cell RNA-seq data is large technical noise resulted from small amount of input materials. The existing methods mainly aim to find differentially expressed genes rather than directly de-noise the single cell data. We present here a powerful but simple method to remove technical noise and explicitly compute the true gene expression levels based on spike-in ERCC molecules. AVAILABILITY AND IMPLEMENTATION: The software is implemented by R and the download version is available at http://wanglab.ucsd.edu/star/GRM. CONTACT: wei-wang@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Philip Brennecke; Simon Anders; Jong Kyoung Kim; Aleksandra A Kołodziejczyk; Xiuwei Zhang; Valentina Proserpio; Bianka Baying; Vladimir Benes; Sarah A Teichmann; John C Marioni; Marcus G Heisler Journal: Nat Methods Date: 2013-09-22 Impact factor: 28.547
Authors: Barbara Treutlein; Doug G Brownfield; Angela R Wu; Norma F Neff; Gary L Mantalas; F Hernan Espinoza; Tushar J Desai; Mark A Krasnow; Stephen R Quake Journal: Nature Date: 2014-04-13 Impact factor: 49.962