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Linnorm: improved statistical analysis for single cell RNA-seq expression data

Abstract Linnorm is a novel normalization and transformation method for the analysis of single cell RNA sequencing (scRNA-seq) data. Linnorm is developed to remove technical noises and simultaneously preserve biological variations in scRNA-seq data, such that existing statistical methods can be improved. Using real scRNA-seq data, we compared Linnorm with existing normalization methods, including NODES, SAMstrt, SCnorm, scran, DESeq and TMM. Linnorm shows advantages in speed, technical noise removal and preservation of cell heterogeneity, which can improve existing methods in the discovery of novel subtypes, pseudo-temporal ordering of cells, clustering analysis, etc. Linnorm also performs better than existing DEG analysis methods, including BASiCS,... (more)
Created Date 2017-09-18
Contributor Yip, Shun H. (Author) / Wang, Panwen (Author) / Kocher, Jean-Pierre A. (Author) / Sham, Pak Chung (Author) / Wang, Junwen (ASU author) / College of Health Solutions / Department of Biomedical Informatics
Type Text
Extent 12 pages
Language English
Identifier DOI: 10.1093/nar/gkx828 / ISSN: 1362-4962 / ISSN: 0305-1048
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Citation Yip, S. H., Wang, P., Kocher, J. A., Sham, P. C., & Wang, J. (2017). Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Research, 45(22). doi:10.1093/nar/gkx828
Note The final version of this article, as published in Nucleic Acids Research, can be viewed online at:
Collaborating Institutions ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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