ESTIMATING GENOME-WIDE COPY NUMBER USING ALLELE SPECIFIC MIXTURE MODELS

Genomic changes such as copy number alterations are thought to be one of the major underlying causes of human phenotypic variation among normal and disease subjects [23,11,25,26,5,4,7,18]. These include chromosomal regions with so-called copy number alterations: instead of the expected two copies, a section of the chromosome for a particular individual may have zero copies (homozygous deletion), one copy (hemizygous deletions), or more than two copies (amplifications). The canonical example is Down syndrome which is caused by an extra copy of chromosome 21. Identification of such abnormalities in smaller regions has been of great interest, because it is believed to be an underlying cause of cancer. More than one decade ago comparative genomic hybridization (CGH)technology was developed to detect copy number changes in a high-throughput fashion. However, this technology only provides a 10 MB resolution which limits the ability to detect copy number alterations spanning small regions. It is widely believed that a copy number alteration as small as one base can have significant downstream effects, thus microarray manufacturers have developed technologies that provide much higher resolution. Unfortunately, strong probe effects and variation introduced by sample preparation procedures have made single-point copy number estimates too imprecise to be useful. CGH arrays use a two-color hybridization, usually comparing a sample of interest to a reference sample, which to some degree removes the probe effect. However, the resolution is not nearly high enough to provide single-point copy number estimates. Various groups have proposed statistical procedures that pool data from neighboring locations to successfully improve precision. However, these procedure need to average across relatively large regions to work effectively thus greatly reducing the resolution. Recently, regression-type models that account for probe-effect have been proposed and appear to improve accuracy as well as precision. In this paper, we propose a mixture model solution specifically designed for single-point estimation, that provides various advantages over the existing methodology. We use a 314 sample database, constructed with public datasets, to motivate and fit models for the conditional distribution of the observed intensities given allele specific copy numbers. With the estimated models in place we can compute posterior probabilities that provide a useful prediction rule as well as a confidence measure for each call. Software to implement this procedure will be available in the Bioconductor oligo packagehttp://www.bioconductor.org)

IGF-1 does not moderate the time-dependent transcriptional patterns of key homeostatic genes induced by sustained compression of bovine cartilage

Objective To determine changes in chondrocyte transcription of a range of anabolic, catabolic and signaling genes following simultaneous treatment of cartilage with Insulin-like growth factor-1 (IGF-1) and ramp-and-hold mechanical compression, and compare with effects on biosynthesis. Methods Explant disks of bovine calf cartilage were slowly compressed (unconfined) over 3-min to their 1 mm cut-thickness (0%-compression) or to 50%-compression with or without 300 ng/ml IGF-1. Expression of 24 genes involved in cartilage homeostasis was measured using qPCR at 2, 8, 24, 32, 48 h after compression ±IGF-1. Clustering analysis was used to identify groups of co-expressed genes to further elucidate mechanistic pathways. Results IGF-1 alone stimulated gene expression of aggrecan and collagen II, but simultaneous 24h compression suppressed this effect. Compression alone up-regulated expression of matrix metalloproteinase (MMP)-3, MMP-13, a disintegrin and metalloproteinase with thrombospondin motif (ADAMTS)-5 and transforming growth factor (TGF)-β, an effect not reversed by simultaneous IGF-1 treatment. Temporal changes in expression following IGF-1 treatment were generally slower than that following compression. Clustering analysis revealed five distinct groups within which the pairings, tissue inhibitor of metalloproteinase (TIMP)-3 and ADAMTS-5, MMP-1 and IGF-2, and IGF-1 and Collagen II, were all robustly co-expressed, suggesting inherent regulation and feedback in chondrocyte gene expression. While aggrecan synthesis was transcriptionally regulated by IGF-1, inhibition of aggrecan synthesis by sustained compression appeared post-transcriptionally regulated. Conclusion Sustained compression markedly altered the effects of IGF-1 on expression of genes involved in cartilage homeostasis, while IGF-1 was largely unable to moderate the transcriptional effects of compression alone. The demonstrated co-expressed gene pairings suggest a balance of anabolic and catabolic activity following simultaneous mechanical and growth factor stimuli.National Institutes of Health (U.S.) (grant R01-AR33236)National Institutes of Health (U.S.) (grant R01-HG003352)National Institutes of Health (U.S.) (grant P42-ES04699)National Institutes of Health (U.S.) (grant T32-EB006348

A variance-stabilizing transformation for gene-expression microarray data

We present a scalable volume rendering technique that exploits lossy compression and low-cost commodity hardware to permit highly interactive exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 42 millions voxels each time step) at interactive rates. By clustering multiple PCs together we demonstrate the data-size scalability of our method. The frame rates achieved make possible the interactive exploration of data in the temporal,spatial, and transfer function domains. A comprehensive evaluation of our method based on experimental studies using data sets (up to 134 millions voxels per time step) from turbulence flow simulations is also presented. Keywords--Compression, disk I/O, high performance computing, out-ofcore processing, parallel rendering, PC, scalable algorithms, scientific visualization, texture hardware, time-varying data, transform encoding, volume renderin

A Variance-Stabilizing Transformation for Gene-Expression Microarray Data

Motivation: Standard statistical techniques often assume that data are normally distributed, with constant variance not depending on the mean of the data. Data that violate these assumptions can often be brought in line with the assumptions by application of a transformation. Gene-expression microarray data have a complicated error structure, with a variance that changes with the mean in a non-linear fashion. Log transformations, which are often applied to microarray data, can inflate the variance of observations near background. Results: We introduce a transformation that stabilizes the variance of microarray data across the full range of expression. Simulation studies also suggest that this transformation approximately symmetrizes microarray data

Comment to the proposal of a serie of Books: Thermal and Nuclear Power Generation

Individual volume titles: Volume 1: Fundamentals of Power Generation Volume 2: Advances in Power Boilers Volume 3: Nuclear Power I: Pressurized Water Reactors Volume 4: Nuclear Power II: Boiling Water Reactors Volume 5: Nuclear Power III: Advanced Reactors ---HTGR--- Volume 6: Nuclear Power IV: Advanced Reactors ---FBR--