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

Shun H. Yip, Panwen Wang, Jean Pierre A. Kocher, Pak Chung Sham, Junwen Wang

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

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, NODES, SAMstrt, Seurat and DESeq2, in false positive rate control and accuracy.

Original languageEnglish (US)
Pages (from-to)e179
JournalNucleic acids research
Volume45
Issue number22
DOIs
StatePublished - Dec 15 2017

ASJC Scopus subject areas

  • Genetics

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