Gene Chip Analysis - Normalization

Normalization

Normalization is required to standardize data and focus on biologically relevant changes. There are many sources of systematic variation in microarray experiments that affect the measured gene expression levels such as dye bias, heat and light sensitivity, efficiency of dye incorporation, differences in the labeled cDNA hybridization conditions, scanning conditions, and unequal quantities of starting RNA, etc. Normalization is an important step in adjusting the data set for technical variation and removing relative abundance of gene expression profiles; this is the only point where 1- and 2-color data analyses vary. The normalization method depends on the data. The basic idea behind all the normalization methods is that the expected mean intensity ratio between the two channels should be one. If the observed mean intensity ratio deviates from one, the data is mathematically processed in such a way that the final observed mean intensity ratio becomes one. With the mean intensity ratio adjusted to one, the distribution of the gene expression is centered so that genuine differentials can be identified.