Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

ABSTRACT: Motivation:Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC). Results:Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-base

SUBMITTER: Franks JM 

PROVIDER: S-EPMC5972664 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature