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We consider the problem of alignment and classification of proteomics data, that is described in Koch et al. [4], using the Extended FisherRao (EFR) framework introduced in [6]. We demonstrate this framework by separating amplitude and phase components of functional data from patients having therapeutic treatments for Acute Myeloid Leukemia (AML). Then, using individual functional principal component analysis, for both the phase and amplitude components [8], we obtain bases for principal subspaces and model the data by imposing probability models on principal coefficients. Lastly, using the distances calculated from individual components, we demonstrate a successful discrimination between responders and non-responders to treatment for AML.

Analysis of proteomics data: Phase amplitude separation using an extended Fisher-Rao metric