File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitleLinnorm: improved statistical analysis for single cell RNA-seq expression data
Authors
Issue Date2017
Citation
Nucleic Acids Research, 2017, v. 45, n. 22, p. E179 How to Cite?
AbstractLinnorm 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.
Persistent Identifierhttp://hdl.handle.net/10722/324518
ISSN
2023 Impact Factor: 16.6
2023 SCImago Journal Rankings: 7.048
PubMed Central ID
ISI Accession Number ID
Errata

 

DC FieldValueLanguage
dc.contributor.authorYip, Shun H.-
dc.contributor.authorWang, Panwen-
dc.contributor.authorKocher, Jean Pierre A.-
dc.contributor.authorSham, Pak Chung-
dc.contributor.authorWang, Junwen-
dc.date.accessioned2023-02-03T07:03:44Z-
dc.date.available2023-02-03T07:03:44Z-
dc.date.issued2017-
dc.identifier.citationNucleic Acids Research, 2017, v. 45, n. 22, p. E179-
dc.identifier.issn0305-1048-
dc.identifier.urihttp://hdl.handle.net/10722/324518-
dc.description.abstractLinnorm 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.-
dc.languageeng-
dc.relation.ispartofNucleic Acids Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleLinnorm: improved statistical analysis for single cell RNA-seq expression data-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1093/NAR/GKX828-
dc.identifier.pmid28981748-
dc.identifier.pmcidPMC5727406-
dc.identifier.scopuseid_2-s2.0-85040554215-
dc.identifier.volume45-
dc.identifier.issue22-
dc.identifier.spageE179-
dc.identifier.eissn1362-4962-
dc.identifier.isiWOS:000419064400001-
dc.relation.erratumdoi:10.1093/NAR/GKX1189-
dc.relation.erratumeid:eid_2-s2.0-85068494352-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats