A Matlab Toolbox for Data Reduction, Visualization, Classification and Knowledge Extraction of Complex Biological Data

In this paper, first we present A Matlab toolbox which gives the possibility to simulate the data for testing the algorithms such as: Principal Component Analysis (PCA), Factor Analysis(FA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and many other classification methods which can be used in Data Reduction (DR), Data Visualization (DV), supervised and unsupervised classification of multivariate great dimensional biological data. Then, we describe some biological experiments related to studying the circadian cell cycles and cancer treatment where the biologists observe different kind of data such as the variations of temperature, activity, hormones, genes and proteins expressions. These data are often complex: multivariate, great dimensionality, heterogeneous, with missing data, and observed at different sampling rates. The classical methods of PCA, FA, ICA and LDA can not directly handle these data. In this paper, we show how this toolbox can help them to visualize, to analyse and to do classifications on these data and finally to extract some knowledge from them.