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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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Testing a Claim about Population Proportion

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Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
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Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Optimization of a parallel permutation testing function for the SPRINT R package.

Savvas Petrou1, Terence M Sloan, Muriel Mewissen

  • 1Edinburgh Parallel Computing Centre, University of Edinburgh Edinburgh, EH9 3JZ, UK.

Concurrency and Computation : Practice & Experience
|January 22, 2013
PubMed
Summary
This summary is machine-generated.

The Simple Parallel R Interface (SPRINT) package offers biostatisticians access to High Performance Computing for R. This study benchmarks SPRINT performance across supercomputers, cloud, and desktop platforms for microarray data analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Computing

Background:

  • R and Bioconductor are popular for microarray data analysis.
  • Large datasets challenge common bioinformatics infrastructure.
  • High Performance Computing (HPC) offers a solution for data-intensive analyses.

Purpose of the Study:

  • To evaluate the performance of the Simple Parallel R Interface (SPRINT) package.
  • To compare SPRINT's performance across diverse computing platforms.
  • To assess the feasibility of using SPRINT on non-supercomputer resources.

Main Methods:

  • Benchmarking the SPRINT implementation of an R permutation testing function.
  • Testing performance on a supercomputer, cloud resources, and a multi-core desktop.
  • Analyzing scaling and efficiency across different hardware configurations.

Main Results:

  • SPRINT demonstrates near-optimal scaling on supercomputers up to 512 processors.
  • Performance benchmarks on cloud and desktop platforms are compared to supercomputer results.
  • The study quantifies SPRINT's effectiveness on accessible computing infrastructures.

Conclusions:

  • SPRINT provides efficient parallelization for R in biostatistics.
  • Accessible platforms like cloud and desktops can effectively utilize SPRINT.
  • SPRINT enhances the computational capacity for large-scale bioinformatics analyses.