A Multi-Step Approach to Time Series Analysis and Gene Expression Clustering

Motivation: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. Results: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA Neural Network for feature extraction, and Probabilistic Principal Surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user friendly visualization interface, can work on noisy data with missing points, and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle data set and a detailed analysis confirm the biological nature of the most significant clusters. Availability: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author.