Subgroup detection in genotype data using invariant coordinate selection

BMC Bioinformatics

Methodology Article

Klaus Nordhausen¹  David Cavero²  Rudolf Preisinger²  Mervi Honkatukia³  Daniel Fischer³  Maria Tuiskula-Haavisto³  Johanna Vilkki³ 

[1] Department of Mathematics and Statistics, University of Turku, Turku, Finland;University of Tampere, School of Health Sciences, Medisiinarinkatu 3, 33014, Tampere, Finland;Lohmann Tierzucht GmbH, Am Seedeich 9-11, 27454, Cuxhaven, Germany;Natural Resources Institute Finland (LUKE), Myllytie 1, Jokioinen, Finland;

关键词: ICS;    PCA;    Genotype data;    Classification;    Dimension reduction;   

DOI  :  10.1186/s12859-017-1589-9

 received in 2016-08-09, accepted in 2017-03-09,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsWe 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.ConclusionThe 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.

【 授权许可】

CC BY   
© The Author(s) 2017

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