SUMMARY: Chromosomal copy number aberrations can be efficiently detected and quantified using low-coverage whole-genome sequencing, but analysis is hampered by the lack of knowledge on absolute DNA copy numbers and tumor purity. Here, we describe an analytical tool for Absolute Copy number Estimation, ACE, which scales relative copy number signals from chromosomal segments to optimally fit absolute copy numbers, without the need for additional genetic information, such as SNP data. In doing so, ACE derives an estimate of tumor purity as well. ACE facilitates analysis of large numbers of samples, while maintaining the flexibility to customize models and generate output of single samples. AVAILABILITY AND IMPLEMENTATION: ACE is freely available via www.bioconductor.org and at www.github.com/tgac-vumc/ACE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. RESULTS: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. CONCLUSIONS: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.

Analysis of DNA copy number profiles requires methods tailored to the specific nature of these data. The number of available data analysis methods has grown enormously in the last 5 years. We discuss the typical characteristics of DNA copy number data, as measured by microarray technology and review the extensive literature on preprocessing methods such as segmentation and calling. Subsequently, the focus narrows to applications of DNA copy number in cancer, in particular, several downstream analyses of multi-sample data sets such as testing, clustering and classification. Finally, we look ahead: what should we prepare for and which methodology-related topics may deserve attention in the near future?

BACKGROUND: Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH). RESULTS: Here we explore power calculations for aCGH experiments comparing two groups. In a pilot experiment CGHpower estimates the biological diversity between groups and provides a statistical framework for estimating average power as a function of sample size. As the method requires pilot data, it can be used either in the planning stage of larger studies or in estimating the power achieved in past experiments. CONCLUSIONS: The proposed method relies on certain assumptions. According to our evaluation with public and simulated data sets, they do not always hold true. Violation of the assumptions typically leads to unreliable sample size estimates. Despite its limitations, this method is, at least to our knowledge, the only one currently available for performing sample size calculations in the context of aCGH. Moreover, the implementation of the method provides diagnostic plots that allow critical assessment of the assumptions on which it is based and hence on the feasibility and reliability of the sample size calculations in each case.The CGHpower web application and the program outputs from evaluation data sets can be freely accessed at http://www.cangem.org/cghpower/

MOTIVATION: Many high-resolution array comparative genomic hybridization tumor profiles contain a wave bias, which makes accurate detection of breakpoints in such profiles more difficult. RESULTS: An efficient and highly effective algorithm that largely removes the wave bias from tumor profiles by regressing the tumor profile data on data of profiles from the clinical genetics practice. Results are illustrated on two independent datasets. The algorithm is shown to be robust against the presence of true copy number aberrations. Moreover, the smoothed profiles are able to recapitulate the aberration location and signal for simulated tumor profiles. AVAILABILITY: Easy-to-use R scripts, user instructions and data are available from http://www.few.vu.nl/~mavdwiel/nowaves.html. SUPPLEMENTARY INFORMATION: Supplementary information are available at Bioinformatics online.

UNLABELLED: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint information from segmentation and by inclusion of several biological concepts that are ignored by existing algorithms. The algorithm is validated for simulated and verified real array CGH data. By incorporating more than three classes, CGHcall improves detection of single copy gains and amplifications. Moreover, it allows effective inclusion of chromosome arm information. AVAILABILITY: An R-package (GUI), a manual and an example data set are available at http://www.few.vu.nl/~mavdwiel/CGHcall.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: We describe a tool, called ACE-it (Array CGH Expression integration tool). ACE-it links the chromosomal position of the gene dosage measured by array CGH to the genes measured by the expression array. ACE-it uses this link to statistically test whether gene dosage affects RNA expression. Availability: ACE-it is freely available at Contact: [email protected] Supplementary Information: Programs, the manual and supplementary information are available on the website.

Summary: We compute P-values, based on the Wilcoxon test with ties, to compare two conditions with array comparative genomic hybridization data, and we provide a simple interface to export and plot these P-values. Availability: CGHMultiArray is freely available at http://www.win.tue.nl/~markvdw/CGHMultiArray.html Contact: [email protected] Supplementary information: Programs, the manual and supplementary information are available on the website.

SUMMARY: We describe a tool, called aCGH-Smooth, for the automated identification of breakpoints and smoothing of microarray comparative genomic hybridization (array CGH) data. aCGH-Smooth is written in visual C++, has a user-friendly interface including a visualization of the results and user-defined parameters adapting the performance of data smoothing and breakpoint recognition. aCGH-Smooth can handle array-CGH data generated by all array-CGH platforms: BAC, PAC, cosmid, cDNA and oligo CGH arrays. The tool has been successfully applied to real-life data. AVAILABILITY: aCGH-Smooth is free for researchers at academic and non-profit institutions at http://www.few.vu.nl/~vumarray/.