  Omics technologies are powerful tools for analyzing patterns in gene
expression data for thousands of genes. Due to a number of systematic
variations in experiments, the raw gene expression data is often obfuscated by
undesirable technical noises. Various normalization techniques were designed in
an attempt to remove these non-biological errors prior to any statistical
analysis. One of the reasons for normalizing data is the need for recovering
the covariance matrix used in gene network analysis. In this paper, we
introduce a novel normalization technique, called the covariance shift
(C-SHIFT) method. This normalization algorithm uses optimization techniques
together with the blessing of dimensionality philosophy and energy minimization
hypothesis for covariance matrix recovery under additive noise (in biology,
known as the bias). Thus, it is perfectly suited for the analysis of
logarithmic gene expression data. Numerical experiments on synthetic data
demonstrate the method's advantage over the classical normalization techniques.
Namely, the comparison is made with Rank, Quantile, cyclic LOESS (locally
estimated scatterplot smoothing), and MAD (median absolute deviation)
normalization methods. We also evaluate the performance of C-SHIFT algorithm on
real biological data.
