PCA and K-Means decipher genome

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dc.creatorGorban, A. N.
dc.creatorZinovyev, A. Y.
dc.date2005-04-08
dc.date2008-01-05
dc.date.accessioned2026-07-07T08:54:55Z
dc.date.available2026-07-07T08:54:55Z
dc.descriptionIn this paper, we aim to give a tutorial for undergraduate students studying statistical methods and/or bioinformatics. The students will learn how data visualization can help in genomic sequence analysis. Students start with a fragment of genetic text of a bacterial genome and analyze its structure. By means of principal component analysis they ``discover'' that the information in the genome is encoded by non-overlapping triplets. Next, they learn how to find gene positions. This exercise on PCA and K-Means clustering enables active study of the basic bioinformatics notions. Appendix 1 contains program listings that go along with this exercise. Appendix 2 includes 2D PCA plots of triplet usage in moving frame for a series of bacterial genomes from GC-poor to GC-rich ones. Animated 3D PCA plots are attached as separate gif files. Topology (cluster structure) and geometry (mutual positions of clusters) of these plots depends clearly on GC-content.
dc.description18 pages, with program listings for MatLab, PCA analysis of genomes and additional animated 3D PCA plots
dc.identifierhttps://arxiv.org/abs/q-bio/0504013
dc.identifierhttp://arxiv.org/abs/q-bio/0504013
dc.identifierA.N. Gorban, B. Kegl, D.C. Wunsch, A. Zinovyev (eds.) Principal Manifolds for Data Visualization and Dimension Reduction, Lecture Notes in Computational Science and Engineering 58, Springer, Berlin - Heidelberg, 2008, 307-323
dc.identifierdoi:10.1007/978-3-540-73750-6_14
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/146113
dc.subjectQuantitative Methods
dc.subjectGenomics
dc.titlePCA and K-Means decipher genome
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