Yip Shun H, Wang Panwen, Kocher Jean-Pierre A, Sham Pak Chung, Wang Junwen
Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA.
Nucleic Acids Res. 2017 Dec 15;45(22):e179. doi: 10.1093/nar/gkx828.
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, NODES, SAMstrt, Seurat and DESeq2, in false positive rate control and accuracy.
Linnorm是一种用于分析单细胞RNA测序（scRNA-seq）数据的新型标准化和转换方法。开发Linnorm的目的是去除技术噪声，同时保留scRNA-seq数据中的生物学变异，从而改进现有的统计方法。我们使用真实的scRNA-seq数据，将Linnorm与现有的标准化方法进行了比较，包括NODES、SAMstrt、SCnorm、scran、DESeq和TMM。Linnorm在速度、技术噪声去除和细胞异质性保留方面具有优势，这可以在发现新亚型、细胞的伪时间排序、聚类分析等方面改进现有方法。在误报率控制和准确性方面，Linnorm也比现有的差异表达基因（DEG）分析方法表现更好，包括BASiCS、NODES、SAMstrt、Seurat和DESeq2。
