Linnorm Improved Statistical Analysis for Single Cell RNA-seq Expression DataLinnorm 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, NODES, SAMstrt, Seurat and DESeq2, in false positive rate control and accuracy.
]]>autYip, Shun H.autWang, PanwenautKocher, Jean-Pierre A.autSham, Pak ChungautWang, JunwenctbCollege of Health SolutionsengThe final version of this article, as published in Nucleic Acids Research, can be viewed online at: https://academic.oup.com/nar/article/45/22/e179/4160405Yip, 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
]]>10.1093/nar/gkx8281362-49620305-1048https://hdl.handle.net/2286/R.I.465270012 pages115191585311638817188127881mogborn1In CopyrightAttribution-NonCommercial-NoDerivs2017-09-18