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The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

We apply auto-tuning to a hybrid MPI-pthreads lattice Boltzmann computation running on the Cray XT4 at National Energy Research Scientific Computing Center (NERSC). Previous work showed that multicore-specific auto-tuning can improve the performance of lattice Boltzmann magnetohydrodynamics (LBMHD) by a factor of 4x when running on dual- and quad-core Opteron dual-socket SMPs. We extend these studies to the distributed memory arena via a hybrid MPI/pthreads implementation. In addition to conventional auto-tuning at the local SMP node, we tune at the message-passing level to determine the optimal aspect ratio as well as the correct balance between MPI tasks and threads per MPI task. Our study presents a detailed performance analysis when moving along an isocurve of constant hardware usage: fixed total memory, total cores, and total nodes. Overall, our work points to approaches for improving intra- and inter-node efficiency on large-scale multicore systems for demanding scientific applications.

To better understand how developmental regulatory networks are defined in the genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP) has developed a suite of methods to describe 3D gene expression data, i.e., the output of the network at cellular resolution for multiple time points. To allow researchers to explore these novel data sets we have developed PointCloudXplore (PCX). In PCX we have linked physical and information visualization views via the concept of brushing (cell selection). For each view dedicated operations for performing selection of cells are available. In PCX, all cell selections are stored in a central management system. Cells selected in one view can in this way be highlighted in any view allowing further cell subset properties to be determined. Complex cell queries can be defined by combining different cell selections using logical operations such as AND, OR, and NOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), the latest publicly available version of PCX. PCX2 has shown to be an effective tool for visual exploration of 3D gene expression data. We discuss (i) all views available in PCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture of PCX2., and (iv) illustrate the usefulness of PCX2 using selected examples.