Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing.

ABSTRACT: Normalization of RNA-Seq data has proven essential to ensure accurate inferences and replication of findings. Hence, various normalization methods have been proposed for various technical artifacts that can be present in high-throughput sequencing transcriptomic studies. In this study, we set out to compare the widely used library size normalization methods (UQ, TMM, and RLE) and across sample normalization methods (SVA, RUV, and PCA) for RNA-Seq data using publicly available data from The Cancer Genome Atlas (TCGA) cervical cancer study. Additionally, an extensive simulation study was completed to compare the performance of the across sample normalization methods in estimating technical artifacts. Lastly, we investigated the effect of reduction in degrees of freedom in the normalized data

SUBMITTER: Abbas-Aghababazadeh F 

PROVIDER: S-EPMC6209231 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature