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Application of principal component analysis to calculate significant differential expression between various types of tissues or between various treatments in microarray experiments

The comparison of gene expression profiles with respect to different conditions is a crucial task of microarray data analysis. Multivariate statistical methods have been applied to analyse these large and complicate data sets. The goal of the analyses is to determine which genes show evidence of significant differential expression between various types of tissues or between various treatments. In the present work the principal component analysis (PCA) has been applied according to microarray gene expression analysis by Alter O et al. (2000). The idea of PCA is in transformation of gene expression data from gene x arrays space to reduced diagonalized eigengenes x eigenarrays space, where eigengenes (or eigenarrays) are unique orthonormal superpositions of genes (or arrays). Sorting the data according to the eigengenes and eigenarrays results in a list of genes that are expressed significantly. The described method has been applied for gene expression analysis of data obtained by a set of experiments that have been performed for optimisation of a cholesterol homeostasis cDNA microarray. Different samples of total RNA obtained from immortal cell lines of human (HepG2, JEG3, OH23 and H295R) and mouse (3T3L1) origin as well as from mouse tissues, all treated with various stimuli, have been labeled and hybridized on microarrays. In order to optimise the microarray method, several experimental parameters have been evaluated: (1) concentration and age of cDNA targets (PCR products) on microarrays ; (2) different spotting solutions ; (3) different hybridization and washing conditions. Experiments have been performed at different times by different experimentators. Using PCA subsets of genes and arrays (experimental conditions) that differ from noise level are presented.

principal component analysis; differential expression; microarray experiments

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