Electronic Journal of Statistics

Analysis of proteomics data: Block $k$-mean alignment

Mara Bernardi, Laura M. Sangalli, Piercesare Secchi, and Simone Vantini

Full-text: Open access

Abstract

We analyze the proteomics data introducing a block $k$-mean alignment procedure. This technique is able to jointly align and cluster the data, accounting appropriately for the block structure of these data, that includes measurement repetitions for each patient. An analysis of area-under-peaks, following the alignment, separates patients who respond and those who do not respond to treatment.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 1714-1723.

Dates
First available in Project Euclid: 29 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1414588154

Digital Object Identifier
doi:10.1214/14-EJS900A

Mathematical Reviews number (MathSciNet)
MR3273586

Zentralblatt MATH identifier
1305.62365

Keywords
Block $k$-mean alignment registration functional clustering proteomics data

Citation

Bernardi, Mara; Sangalli, Laura M.; Secchi, Piercesare; Vantini, Simone. Analysis of proteomics data: Block $k$-mean alignment. Electron. J. Statist. 8 (2014), no. 2, 1714--1723. doi:10.1214/14-EJS900A. https://projecteuclid.org/euclid.ejs/1414588154


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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 1703–1713.