Electronic Journal of Statistics

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

J. Derek Tucker, Wei Wu, and Anuj Srivastava

Full-text: Open access

Abstract

We consider the problem of alignment and classification of proteomics data, that is described in Koch et al. [4], using the Extended Fisher-Rao (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.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 1724-1733.

Dates
First available in Project Euclid: 29 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414588155

Digital Object Identifier
doi:10.1214/14-EJS900B

Mathematical Reviews number (MathSciNet)
MR3273587

Zentralblatt MATH identifier
1305.62380

Keywords
Amplitude variability function principal component analysis functional data analysis phase variability

Citation

Tucker, J. Derek; Wu, Wei; Srivastava, Anuj. Analysis of proteomics data: Phase amplitude separation using an extended Fisher-Rao metric. Electron. J. Statist. 8 (2014), no. 2, 1724--1733. doi:10.1214/14-EJS900B. https://projecteuclid.org/euclid.ejs/1414588155


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References

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See also

  • Related item: Koch, I., Hoffmann, P., Marron, J. S. (2014). Proteomics profiles from mass spectrometry. Electron. J. Statist. 8(2) 1703–1713.