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pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms

Overview of attention for article published in BMC Bioinformatics, September 2017
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3 tweeters

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15 Mendeley
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Title
pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1838-y
Pubmed ID
Authors

Sophie Molnos, Clemens Baumbach, Simone Wahl, Martina Müller-Nurasyid, Konstantin Strauch, Rui Wang-Sattler, Melanie Waldenberger, Thomas Meitinger, Jerzy Adamski, Gabi Kastenmüller, Karsten Suhre, Annette Peters, Harald Grallert, Fabian J. Theis, Christian Gieger

Abstract

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different "omics" layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 33%
Researcher 4 27%
Professor 1 7%
Student > Ph. D. Student 1 7%
Student > Bachelor 1 7%
Other 1 7%
Unknown 2 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 27%
Biochemistry, Genetics and Molecular Biology 2 13%
Medicine and Dentistry 2 13%
Computer Science 1 7%
Economics, Econometrics and Finance 1 7%
Other 1 7%
Unknown 4 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 October 2017.
All research outputs
#11,583,480
of 17,829,815 outputs
Outputs from BMC Bioinformatics
#4,478
of 6,281 outputs
Outputs of similar age
#169,762
of 286,190 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 32 outputs
Altmetric has tracked 17,829,815 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,281 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.