The systematic integration of expression profiles and other types of gene information, such as copy number, chromosomal localization, and sequence characteristics, still represents a challenge in the genomic arena. In particular, the integrative analysis of genomic and transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with structural and transcriptional imbalances often characterizing cancer. A computational framework based on locally adaptive statistical procedures (Global Smoothing Copy Number, GLSCN, and Locally Adaptive Statistical Procedure, LAP), which incorporate genomic and transcriptional data with structural information for the identification of imbalanced chromosomal regions, is described. Both GLSCN and LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of copy number and gene expression signals. The application of GLSCN and LAP to the integrative analysis of a human metastatic clear cell renal carcinoma cell line (Caki-1) allowed identifying chromosomal regions that are directly involved in known chromosomal aberrations characteristic of tumors.