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, researchers from the University of Hong Kong 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.
Comparing the homoscedasticity and normality assumptions between the conventional log-plus-one CPM transformation and Linnorm’s transformation
The Klein dataset is utilized. (A, B) Mean versus SD plots for the investigation of homoscedasticity, where SD should be stable across the mean. (C, D) Mean versus skewness plots for the examination of normality, where the normal distribution has the skewness of 0. All zeroes are ignored in this figure.
Availability – Linnorm is open source and is written in C++ and implemented into an R Package. It is freely available at (http://www.jjwanglab.org/linnorm) and on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/Linnorm.html).