Preprocessing of microarray data

Normalization:

When microarray data is obtained from multiple arrays, it is necessary to normalize the dataset to avoid variation due to different environments. There are several normalization techniques available in the literature. For example, Lowess normalization, Quantile normalization etc. Among these, quantile normalization is the current favorite method applied on microarray analysis.

Transformation:

Besides normalization, it is also beneficial to transform the data to correctly treat both up- and down-regulated data. The most widely used transformation technique is the logarithmic base 2. Notably, logarithms treat numbers and their reciprocals symmetrically. For example: log₂(1) = 0, log₂(2) = 1, log₂(1/2) = -1, log₂(4) = 2, log₂(1/4) = -2.

Filtering:

If the intensity of hybridization in microarray is low (close to the background), then usually relative error becomes high. The common practice is to filter out (discard) the array elements which are statistically significantly different from the background.

References:

1. Slonim DK, Yanai I (2009) Getting Started in Gene Expression Microarray Analysis. PLoS Comput Biol 5(10): e1000543. doi:10.1371/journal.pcbi.1000543
2. Quackenbush, J. (2002) Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501.