permGPU: Using graphics processing units in RNA microarray association studies
TL;DR: A CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies and provides a dramatic increase in performance for permutation resampling analysis in the context of micro array association studies.
read more
Abstract: Background
Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
fMRI analysis on the GPU-Possibilities and challenges
TL;DR: All the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU, which will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI.
57
Fast Random Integer Generation in an Interval
TL;DR: An unbiased function to generate ranged integers from a source of random words that avoids integer divisions with high probability is reviewed and it is shown that this algorithm can multiply the speed of unbiased random shuffling on x64 processors.
Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis
TL;DR: It is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests, making statistical analysis of advanced detection methods in fMRI practically feasible.
Statistical Considerations for Analysis of Microarray Experiments
TL;DR: A broad overview of some of the major statistical considerations for the design and analysis of microarrays experiments conducted as correlative science studies to clinical trials is provided.
31
Fast Random Integer Generation in an Interval
TL;DR: In this article, an unbiased function to generate ranged integers from a source of random words that avoids integer divisions with high probability is presented. But it is not shown that this algorithm can multiply the speed of unbiased random shuffling on x64 processors.
References
•Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
410.8K
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Rafael A. Irizarry,Bridget G. Hobbs,Francois Collin,Yasmin Beazer-Barclay,Kristen J. Antonellis,Uwe Scherf,Terence P. Speed +6 more
TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment.
TL;DR: Resampling-Based Adjustments: Basic Concepts and Practical Applications.
2.3K