title: Functional clustering algorithm for high-dimensional proteomics data.
abstract: Clustering proteomics data is a challenging problem for any traditional clustering algorithm. Usually, the number of samples is largely smaller than the number of protein peaks. The use of a clustering algorithm which does not take into consideration the number of features of variables (here the number of peaks) is needed. An innovative hierarchical clustering algorithm may be a good approach. We propose here a new dissimilarity measure for the hierarchical clustering combined with a functional data analysis. We present a specific application of functional data analysis (FDA) to a high-throughput proteomics study. The high performance of the proposed algorithm is compared to two popular dissimilarity measures in the clustering of normal and human T-cell leukemia virus type 1 (HTLV-1)-infected patients samples.
authors.affiliation: Department of Statistic Operation and Management Sciences (SOMS), The University of Tennessee, Knoxville, TN 37996, USA.
authors.forename: Halima
authors.initials: H
authors.lastname: Bensmail
authors.forename: Buddana
authors.initials: B
authors.lastname: Aruna
authors.forename: O John
authors.initials: OJ
authors.lastname: Semmes
authors.forename: Abdelali
authors.initials: A
authors.lastname: Haoudi