A COMPUTATIONAL PROCEDURE FOR THE INTEGRATIVE ANALYSIS OF GENOMIC DATA AT THE SINGLE SAMPLE LEVEL

The integrative analysis of DNA copy number levels and transcriptional profiles, in context of the physical location of genes in a genome, still represents a challenge in the bioinformatics arena. A computational framework based on locally adaptive statistical procedures (Locally Adaptive Statistical Procedure, LAP and Global Smoothing Copy Number, GLSCN) for the identification of imbalanced chromosomal regions in single samples is described. The application of LAP and GLSCN to the integrative analysis of clear cell renal carcinoma patients allowed identifying chromosomal regions that are directly involved in known and novel chromosomal aberrations characteristic of tumors.