A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
- PMID: 22988256
- DOI: 10.1093/bib/bbs046
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
Abstract
During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.
Keywords: RNA-seq; differential analysis; high-throughput sequencing; normalization.
Similar articles
-
The Impact of Normalization Methods on RNA-Seq Data Analysis.Biomed Res Int. 2015;2015:621690. doi: 10.1155/2015/621690. Epub 2015 Jun 15. Biomed Res Int. 2015. PMID: 26176014 Free PMC article.
-
Assessment of Single Cell RNA-Seq Normalization Methods.G3 (Bethesda). 2017 Jul 5;7(7):2039-2045. doi: 10.1534/g3.117.040683. G3 (Bethesda). 2017. PMID: 28468817 Free PMC article.
-
A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.PLoS One. 2017 May 1;12(5):e0176185. doi: 10.1371/journal.pone.0176185. eCollection 2017. PLoS One. 2017. PMID: 28459823 Free PMC article.
-
Normalization for Single-Cell RNA-Seq Data Analysis.Methods Mol Biol. 2019;1935:11-23. doi: 10.1007/978-1-4939-9057-3_2. Methods Mol Biol. 2019. PMID: 30758817 Review.
-
Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools.IEEE/ACM Trans Comput Biol Bioinform. 2020 Mar-Apr;17(2):566-586. doi: 10.1109/TCBB.2018.2873010. Epub 2018 Oct 1. IEEE/ACM Trans Comput Biol Bioinform. 2020. PMID: 30281477 Review.
Cited by
-
Meta-analysis towards FSHD reveals misregulation of neuromuscular junction, nuclear envelope, and spliceosome.Commun Biol. 2024 May 25;7(1):640. doi: 10.1038/s42003-024-06325-z. Commun Biol. 2024. PMID: 38796645 Free PMC article.
-
Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.Brief Bioinform. 2024 Mar 27;25(3):bbae241. doi: 10.1093/bib/bbae241. Brief Bioinform. 2024. PMID: 38770720 Free PMC article.
-
Transcriptome analysis reveals key genes and pathways associated with heat stress in Pleurotus pulmonarius.Int Microbiol. 2024 May 16. doi: 10.1007/s10123-024-00536-4. Online ahead of print. Int Microbiol. 2024. PMID: 38750284
-
A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC Bioinformatics. 2024 May 8;25(1):181. doi: 10.1186/s12859-024-05801-x. BMC Bioinformatics. 2024. PMID: 38720247 Free PMC article.
-
Targeting EMT using low-dose Teniposide by downregulating ZEB2-driven activation of RNA polymerase I in breast cancer.Cell Death Dis. 2024 May 8;15(5):322. doi: 10.1038/s41419-024-06694-7. Cell Death Dis. 2024. PMID: 38719798 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources