Signal Processing Techniques and Statistics for the Analysis of Human Genome Associated with Behavior Abnormalities
Almost all human genetic diseases such as cancers and developmental abnormalities are characterized by the presence of genetic variations. Microrray-based Comparative Genomic Hybridization techniques are used to map and measure DNA copy number variations with high-resolution. However, the observed copy numbers are corrupted by noise, making variations breakpoints hard to detect. In this paper, we provide a framework for the analysis of copy number datasets and it is divided into two parts. In the first part, we propose a novel image processing technique to analyze copy number variations based on extended version of Sigma filter algorithm as pre-processing technique. In the second part, we provide statistical searching model for classifying nonrandom genomic variations across multiple samples. Finally, we provide simulated and real data samples to study this effect.