  Computational analysis methods including machine learning have a significant
impact in the fields of genomics and medicine. High-throughput gene expression
analysis methods such as microarray technology and RNA sequencing produce
enormous amounts of data. Traditionally, statistical methods are used for
comparative analysis of gene expression data. However, more complex analysis
for classification of sample observations, or discovery of feature genes
requires sophisticated computational approaches. In this review, we compile
various statistical and computational tools used in analysis of expression
microarray data. Even though the methods are discussed in the context of
expression microarrays, they can also be applied for the analysis of RNA
sequencing and quantitative proteomics datasets. We discuss the types of
missing values, and the methods and approaches usually employed in their
imputation. We also discuss methods of data normalization, feature selection,
and feature extraction. Lastly, methods of classification and class discovery
along with their evaluation parameters are described in detail. We believe that
this detailed review will help the users to select appropriate methods for
preprocessing and analysis of their data based on the expected outcome.
