Open AccessNormalization of two-channel microarray experiments: a semiparametric approach
Author(s) -
Jeanette E. Eckel,
Chris Gennings,
Terry M. Therneau,
Lyle D. Burgoon,
Darrell R. Boverhof,
Tim Zacharewski
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bti105
Subject(s) - normalization (sociology) , computer science , smoothing , design of experiments , channel (broadcasting) , statistics , linear regression , regression analysis , r package , data mining , mathematics , machine learning , computer network , sociology , anthropology
An important underlying assumption of any experiment is that the experimental subjects are similar across levels of the treatment variable, so that changes in the response variable can be attributed to exposure to the treatment under study. This assumption is often not valid in the analysis of a microarray experiment due to systematic biases in the measured expression levels related to experimental factors such as spot location (often referred to as a print-tip effect), arrays, dyes, and various interactions of these effects. Thus, normalization is a critical initial step in the analysis of a microarray experiment, where the objective is to balance the individual signal intensity levels across the experimental factors, while maintaining the effect due to the treatment under investigation.