Thermoelectric Properties of Fe0.2Co3.8Sb12-xTex ... - Physics

Random matrix theory and gene correlation coefficient statistics
of DNA- Microarray data: Application in understanding the
system biology of gene regulation
Debayan Dey 1 , S. Ramakumar 1
1
Department of Physics, Indian Institute of Science, Bangalore, 560012, India
Abstract: The fundamental question in biology is to understand the mechanism by which a
cell functions. The gene regulation of a cell and its interaction with environment & other cells
makes a complex living organism. Gene regulation is the key process which dictates cell
function and any imbalance in it results in disease. Understanding gene regulation using high
throughput methods is pivotal to understand the holistic nature of gene regulatory network. But it
suffers from large embedded noise within it; so a noise reduction method is very important to
deduce sensible biological information which further can be experimentally tested. DNA-
Microarray technique provides gene expression level data for the whole cell’s activity at a given
time. The understanding of gene correlation matrix provided by the data is essential for
biological elucidation of gene regulatory network.
In our study, we have used Random matrix theory (RMT) to separate non random and system
specific features in the complex biological system from noisy data. We have analyzed various
parameters that affect the threshold determination of Gene correlation network (GCN) e.g. size
of the matrix, no. of variables (biological conditions) used to create the matrix, quality of
microarray data etc. Here, we report the variations in statistical properties of gene correlation
coefficient and its eigenvalue distribution in different biological scenarios. This property reflects
the switching mechanism and dynamicity in the gene regulatory controls. We further discuss on
the gene specific correlation coefficient distribution and its unique properties regarding global
gene regulation. Gene correlations are not constant but change in response to physiological
changes. The variations in the gene correlation and its effect in the gene regulatory network are
analyzed. We have applied this method to understand the gene regulatory network of
Mycobacterium tuberculosis and to understand its global regulatory circuit. We discuss a few
more areas in computational biology where RMT can be used to separate noise from true
correlations.