Kernel density weighted loess normalization improves the performance of detection within asymmetrical data.

ABSTRACT:

Background

Normalization of gene expression data has been studied for many years and various strategies have been formulated to deal with various types of data. Most normalization algorithms rely on the assumption that the number of up-regulated genes and the number of down-regulated genes are roughly the same. However, the well-known Golden Spike experiment presents a unique situation in which differentially regulated genes are biased toward one direction, thereby challenging the conclusions of previous bench mark studies.

Results

This study proposes two novel approaches, KDL and KDQ, based on kernel density estimation to improve upon the basic idea of invariant set selection. The key concept is to provide various importance scores to data points on the MA plot according to

SUBMITTER: Hsieh WP 

PROVIDER: S-EPMC3118355 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature