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Subgroup detection in genotype data using invariant coordinate selection

Overview of attention for article published in BMC Bioinformatics, March 2017
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Title
Subgroup detection in genotype data using invariant coordinate selection
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1589-9
Pubmed ID
Authors

Daniel Fischer, Mervi Honkatukia, Maria Tuiskula-Haavisto, Klaus Nordhausen, David Cavero, Rudolf Preisinger, Johanna Vilkki

Abstract

The current gold standard in dimension reduction methods for high-throughput genotype data is the Principle Component Analysis (PCA). The presence of PCA is so dominant, that other methods usually cannot be found in the analyst's toolbox and hence are only rarely applied. We present a modern dimension reduction method called 'Invariant Coordinate Selection' (ICS) and its application to high-throughput genotype data. The more commonly known Independent Component Analysis (ICA) is in this framework just a special case of ICS. We use ICS on both, a simulated and a real dataset to demonstrate first some deficiencies of PCA and how ICS is capable to recover the correct subgroups within the simulated data. Second, we apply the ICS method on a chicken dataset and also detect there two subgroups. These subgroups are then further investigated with respect to their genotype to provide further evidence of the biological relevance of the detected subgroup division. Further, we compare the performance of ICS also to five other popular dimension reduction methods. The ICS method was able to detect subgroups in data where the PCA fails to detect anything. Hence, we promote the application of ICS to high-throughput genotype data in addition to the established PCA. Especially in statistical programming environments like e.g. R, its application does not add any computational burden to the analysis pipeline.

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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 40%
Professor 2 13%
Student > Ph. D. Student 2 13%
Student > Master 1 7%
Unknown 4 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 47%
Computer Science 1 7%
Biochemistry, Genetics and Molecular Biology 1 7%
Immunology and Microbiology 1 7%
Neuroscience 1 7%
Other 0 0%
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 03 July 2018.
All research outputs
#8,263,058
of 13,177,477 outputs
Outputs from BMC Bioinformatics
#3,378
of 4,951 outputs
Outputs of similar age
#160,678
of 268,150 outputs
Outputs of similar age from BMC Bioinformatics
#12
of 22 outputs
Altmetric has tracked 13,177,477 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 4,951 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.