Linnorm: improved statistical analysis for single cell RNA-seq expression data
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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.
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Expressions of CMs were normalized using Linnorm and batch effect was corrected with removeBatchEffect.
“Within each tissue configuration, expressions of the CMs were normalized using R package Linnorm (version 2.16.0),55 and batch effect was corrected with removeBatchEffect function from R package Limma (version3.48.3).56”
From Single-cell RNA sequencing reveals maturation trajectory in human pluripotent stem cell-derived cardiomyocytes in engineered tissues (2023) · cites this paper as [55]
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- Authors
- Yip, S. H., Wang, P., Kocher, J.-P. A., Sham, P. C., & Wang, J.
- Affiliations
- Mayo Clinic (US)
- University of Hong Kong (HK)
- Corresponding author
- Wang, Junwen
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-09-18
- Volume
- 45
- Issue
- 22
- Pages
- e179-e179
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC5727406
- OpenAlex
- W2755506013
- Cited by
- 121
- References
- 11
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 13 · 2023: 11 · 2022: 19 · 2021: 20 · 2020: 16 · 2019: 14 · 2018: 9 · 2017: 1
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Arizona State University Library Digital Repository (Arizona State University) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, data normalization, differential expression analysis, cellular heterogeneity, clustering, false positive rate control
- MeSH terms
- Algorithms, RNA, Reproducibility of Results, Cluster Analysis, Linear Models, Sequence Analysis, RNA, Gene Expression Profiling, Biostatistics, Single-Cell Analysis
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