Abstract Motivation: Cancer genomes are characterized by the accumulation of point mutations and structural alterations such as copy-number alterations and genomic rearrangements. Among structural changes, systematic analyses of copy-number alterations have provided deeper insight into the architecture of cancer genomes and had led to new potential treatment opportunities. During the course of cancer genome evolution, selection mechanisms are leading to a non-random pattern of mutational events contributing to fitness benefits of the cancer cells. We therefore developed a new method to dissect random from non-random patterns in copy-number data and thereby to assess significantly enriched somatic copy-number aberrations across a set of tumor specimens or cell lines. In contrast to existing approaches, the method is invariant to any strictly monotonous transformation of the input data which results to an insensitivity of differences in tumor purity, array saturation effects and copy-number baseline levels. Results: We applied our approach to recently published datasets of small-cell lung cancer and squamous cell lung cancer and validated its performance by comparing the results to an orthogonal approach. In addition, we found a new deletion peak containing the HLA-A gene in squamous cell lung cancer. Availability: The CGARS program package is available for download at http://www.translational-genomics.uni-koeln.de/scientific-resources/ Documentation and examples are available together with the package. Contact: [email protected] Supplementary Information: Supplementary information is available at Bioinformatics online.

MOTIVATION: DNA copy number aberration--both inherited and sporadic--is a significant contributor to a variety of human diseases. Copy number characterization is therefore an area of intense research. Probe hybridization-based arrays are important tools used to measure copy number in a high-throughput manner. RESULTS: In this article, we present a simple but powerful nonparametric rank-based approach to detect deletions and gains from raw array copy number measurements. We use three different rank-based statistics to detect three separate molecular phenomena-somatic lesions, germline deletions and germline gains. The approach is robust and rigorously grounded in statistical theory, thereby enabling the meaningful assignment of statistical significance to each putative aberration. We demonstrate the flexibility of our approach by applying it to data from three different array platforms. We show that our method compares favorably with established approaches by applying it to published well-characterized samples. Power simulations demonstrate exquisite sensitivity for array data of reasonable quality. CONCLUSIONS: Our flexible rank-based framework is suitable for multiple platforms including single nucleotide polymorphism arrays and array comparative genomic hybridization, and can reliably detect gains or losses of genomic DNA, whether inherited, de novo, or somatic. AVAILABILITY: An R package RankCopy containing the methods described here, and is freely available from the author's web site (http://mendel.gene.cwru.edu/laframboiselab/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: High-throughput screening of pharmaceutical compound activity in tissue culture experiments requires time-consuming repeated analysis of the large amounts of data generated. Automation of the evaluation procedure and assessment of measurement accuracy can save time and improve the comparability of results. RESULTS: We present a tool for simultaneous evaluation of an arbitrary number of compound screens including a standardized statistical validation. It is provided as a novel R package with a Tcl/Tk-based GUI for convenient use in the lab and runs on usual platforms like Linux, Windows and Mac OS. In a compound screen of lung cancer cells, the tool was successfully and efficiently applied for data analysis. CONCLUSION: The package provides an efficient and intuitive platform for automatic evaluation of compound screens, improving the performance and standardization of data analysis.