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.
Highlights
A variety of mass spectrometry-based platforms are currently available for providing information on both protein patterns and protein identity [1, 2]
Depending upon the range of masses the investigator wishes to study, there are a variety of possible slide surfaces; for example, the strong anion exchange (SAX) or the weak cation exchange (WCX) surface
We propose to implement a hierarchical clustering algorithm for proteomics data using functional data analysis (FDA)
Summary
A variety of mass spectrometry-based platforms are currently available for providing information on both protein patterns and protein identity [1, 2]. A flexible dissimilarity measure is the one that may combine the characteristic of both measures δHZ and δC This means that a potential dissimilarity measure should use the collected estimated points of the original curve obtained from FDA so that no information is lost and should work on different type of smoothed curves without using the monotonicity restriction. In this sense, we propose a functional-based dissimilarity δB measure which uses the rank of the curve proposed by Heckman and Zamar and generalizes Cerioli et al dissimilarity measure as follows:.
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