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Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure

Overview of attention for article published in BMC Bioinformatics, January 2011
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Mentioned by

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1 tweeter

Citations

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20 Dimensions

Readers on

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23 Mendeley
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3 CiteULike
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Title
Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure
Published in
BMC Bioinformatics, January 2011
DOI 10.1186/1471-2105-12-255
Pubmed ID
Authors

Tulaya Limpiti, Apichart Intarapanich, Anunchai Assawamakin, Philip J Shaw, Pongsakorn Wangkumhang, Jittima Piriyapongsa, Chumpol Ngamphiw, Sissades Tongsima

Abstract

The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 4%
United States 1 4%
France 1 4%
Unknown 20 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 39%
Professor 4 17%
Student > Ph. D. Student 3 13%
Other 2 9%
Student > Postgraduate 2 9%
Other 2 9%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 78%
Mathematics 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Environmental Science 1 4%
Engineering 1 4%
Other 0 0%
Unknown 1 4%

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 02 November 2011.
All research outputs
#2,671,721
of 5,039,474 outputs
Outputs from BMC Bioinformatics
#2,026
of 2,893 outputs
Outputs of similar age
#32,012
of 74,457 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 96 outputs
Altmetric has tracked 5,039,474 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,893 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.