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Moment based gene set tests

Overview of attention for article published in BMC Bioinformatics, April 2015
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  • Good Attention Score compared to outputs of the same age (68th percentile)

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7 tweeters

Citations

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5 Dimensions

Readers on

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28 Mendeley
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1 CiteULike
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Title
Moment based gene set tests
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/s12859-015-0571-7
Pubmed ID
Authors

Jessica L Larson, Art B Owen

Abstract

Permutation-based gene set tests are standard approaches for testing relationships between collections of related genes and an outcome of interest in high throughput expression analyses. Using M random permutations, one can attain p-values as small as 1/(M+1). When many gene sets are tested, we need smaller p-values, hence larger M, to achieve significance while accounting for the number of simultaneous tests being made. As a result, the number of permutations to be done rises along with the cost per permutation. To reduce this cost, we seek parametric approximations to the permutation distributions for gene set tests. We study two gene set methods based on sums and sums of squared correlations. The statistics we study are among the best performers in the extensive simulation of 261 gene set methods by Ackermann and Strimmer in 2009. Our approach calculates exact relevant moments of these statistics and uses them to fit parametric distributions. The computational cost of our algorithm for the linear case is on the order of doing |G| permutations, where |G| is the number of genes in set G. For the quadratic statistics, the cost is on the order of |G|(2) permutations which can still be orders of magnitude faster than plain permutation sampling. We applied the permutation approximation method to three public Parkinson's Disease expression datasets and discovered enriched gene sets not previously discussed. We found that the moment-based gene set enrichment p-values closely approximate the permutation method p-values at a tiny fraction of their cost. They also gave nearly identical rankings to the gene sets being compared. We have developed a moment based approximation to linear and quadratic gene set test statistics' permutation distribution. This allows approximate testing to be done orders of magnitude faster than one could do by sampling permutations. We have implemented our method as a publicly available Bioconductor package, npGSEA (www.bioconductor.org) .

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Luxembourg 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 39%
Student > Ph. D. Student 7 25%
Student > Bachelor 2 7%
Other 1 4%
Student > Master 1 4%
Other 2 7%
Unknown 4 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 36%
Biochemistry, Genetics and Molecular Biology 5 18%
Computer Science 3 11%
Mathematics 2 7%
Physics and Astronomy 1 4%
Other 3 11%
Unknown 4 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 24 November 2017.
All research outputs
#4,481,640
of 14,711,632 outputs
Outputs from BMC Bioinformatics
#2,071
of 5,475 outputs
Outputs of similar age
#71,430
of 228,295 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 14,711,632 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 5,475 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 228,295 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them