TY - JOUR T1 - The covariance shift (C-SHIFT) algorithm for normalizing biological data JF - bioRxiv DO - 10.1101/2020.04.13.038463 SP - 2020.04.13.038463 AU - Evgenia Chunikhina AU - Paul Logan AU - Yevgeniy Kovchegov AU - Anatoly Yambartsev AU - Debashis Mondal AU - Andrey Morgun Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/04/14/2020.04.13.038463.abstract N2 - 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.Competing Interest StatementThe authors have declared no competing interest. ER -