An integrated hierarchical Bayesian approach to normalizing left-censored microRNA microarray data.

ABSTRACT:

Background

MicroRNAs (miRNAs) are small endogenous ssRNAs that regulate target gene expression post-transcriptionally through the RNAi pathway. A critical pre-processing procedure for detecting differentially expressed miRNAs is normalization, aiming at removing the between-array systematic bias. Most normalization methods adopted for miRNA data are the same methods used to normalize mRNA data; but miRNA data are very different from mRNA data mainly because of possibly larger proportion of differentially expressed miRNA probes, and much larger percentage of left-censored miRNA probes below detection limit (DL). Taking the unique characteristics of miRNA data into account, we present a hierarchical Bayesian approach that integrates normalization, missing data imputation, and feature

SUBMITTER: Kang J 

PROVIDER: S-EPMC3734108 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature