Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression

<p>Abstract</p> <p>Background</p> <p>Gene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability.</p> <p>Results</p> <p>We have developed a novel algorithm Iterative Clique Enumeration (ICE) for identifying relatively independent maximal cliques as co-expression modules and a module-based approach to the analysis of gene expression data. Applying this approach on a public breast cancer dataset identified 19 modules whose expression levels were significantly correlated with tumor grade. The correlations were reproducible for 17 modules in an independent breast cancer dataset, and the reproducibility was considerably higher than that based on individual genes or modules identified by other algorithms. Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories. Specifically, modules related to cell proliferation and immune response were up-regulated in high-grade tumors while those related to cell adhesion was down-regulated. Further analyses showed that transcription factors NYFB, E2F1/E2F3, NRF1, and ELK1 were responsible for the up-regulation of the cell proliferation modules. IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules. Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression. The module without GO enrichment was found to be associated with a potential genomic gain in 8q21-23 in high-grade tumors. The 17-module signature of breast tumor progression clustered patients into subgroups with significantly different relapse-free survival times. Namely, patients with lower cell proliferation and higher cell adhesion levels had significantly lower risk of recurrence, both for all patients (<it>p </it>= 0.004) and for those with grade 2 tumors (<it>p </it>= 0.017).</p> <p>Conclusions</p> <p>The ICE algorithm is effective in identifying relatively independent co-expression modules from gene co-expression networks and the module-based approach illustrated in this study provides a robust, interpretable, and mechanistic characterization of transcriptional changes.</p

Revisiting Matrix Product on Master-Worker Platforms

This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous master-worker platforms. While matrix-product is well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses that render our work original and innovative: - Centralized data. We assume that all matrix files originate from, and must be returned to, the master. - Heterogeneous star-shaped platforms. We target fully heterogeneous platforms, where computational resources have different computing powers. - Limited memory. Because we investigate the parallelization of large problems, we cannot assume that full matrix panels can be stored in the worker memories and re-used for subsequent updates (as in ScaLAPACK). We have devised efficient algorithms for resource selection (deciding which workers to enroll) and communication ordering (both for input and result messages), and we report a set of numerical experiments on various platforms at Ecole Normale Superieure de Lyon and the University of Tennessee. However, we point out that in this first version of the report, experiments are limited to homogeneous platforms

Fast network centrality analysis using GPUs

<p>Abstract</p> <p>Background</p> <p>With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU) provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs.</p> <p>Results</p> <p>We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package <it>gpu</it>-<it>fan </it>(GPU-based Fast Analysis of Networks) for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks.</p> <p>Conclusions</p> <p><it>gpu</it>-<it>fan </it>provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL) at <url>http://bioinfo.vanderbilt.edu/gpu-fan/</url>.</p

Scheduling tasks with precedence constraints on heterogeneous distributed computing systems

Efficient scheduling is essential to exploit the tremendous potential of high performance computing systems. Scheduling tasks with precedence constraints is a well studied problem and a number of heuristics have been proposed. In this thesis, we first consider the problem of scheduling task graphs in heterogeneous distributed computing systems (HDCS) where the processors have different capabilities. A novel, list scheduling-based algorithm to deal with this particular situation is proposed. The algorithm takes into account the resource scarcity when assigning the task node weights. It incorporates the average communication cost between the scheduling node and its node when computing the Earliest Finish Time (EFT). Comparison studies show that our algorithm performs better than related work overall. We next address the problem of scheduling task graphs to both minimize the makespan and maximize the robustness in HDCS. These two objectives are conflicting and an ᵋ-constraint method is employed to solve the bi-objective optimization problem. We give two definitions of robustness based on tardiness and miss rate. We also prove that slack is an effective metric to be used to adjust the robustness. The overall performance of a schedule must consider both the makespan and robustness. Experiments are carried out to validate the performance of the proposed algorithm. The uncertainty nature of the task execution times and data transfer rates is usually neglected by traditional scheduling heuristics. We model those performance characteristics of the system as random variables. A stochastic scheduling problem is formulated to minimize the expected makespan and maximize the robustness. We propose a genetic algorithm based approach to tackle this problem. Experiment results show that our heuristic generates schedules with smaller makespan and higher robustness compared with other deterministic approaches

Robust task scheduling in non-deterministic heterogeneous computing systems

The paper addresses the problem of matching and scheduling of DAG-structured application to both minimize the makespan and maximize the robustness in a heterogeneous computing system. Due to the conflict of the two objectives, it is usually impossible to achieve both goals at the same time. We give two definitions of robustness of a schedule based on tardiness and miss rate. Slack is proved to be an effective metric to be used to adjust the robustness. We employ ǫ-constraint method to solve the bi-objective optimization problem where minimizing the makespan and maximizing the slack are the two objectives. Overall performance of a schedule considering both makespan and robustness is defined such that user have the flexibility to put emphasis on either objective. Experiment results are presented to validate the performance of the proposed algorithm