PCA and K-Means decipher genome

Details PCA and K-Means decipher genome PCA and K-Means decipher genome

2005-04-08

arxiv.org/abs/q-bio/0504013v2

A.N. Gorban, B. Kegl, D.C. Wunsch, A. Zinovyev (eds.) Principal Manifolds for Data Visualization and Dimension Reduction, Lect

Biology

Quantitative Biology

Quantitative Methods

18 pages, with program listings for MatLab, PCA analysis of genomes and additional animated 3D PCA plots

Scientific paper

10.1007/978-3-540-73750-6_14

In 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.

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