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Deriving biomedical diagnostics from NMR spectroscopic data

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Abstract

Biomedical spectroscopic experiments generate large volumes of data. For accurate, robust diagnostic tools the data must be analyzed for only a few characteristic observations per subject, and a large number of subjects must be studied. We describe here two of the current data analytic approaches applied to this problem: SIMCA (principal component analysis, partial least squares), and the statistical classification strategy (SCS). We demonstrate the application of the SCS by three examples of its use in analyzing 1H NMR spectra: screening for colon cancer, characterization of thyroid cancer, and distinguishing cancer from cholangitis in the biliary tract.

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Abbreviations

FLD:

Fisher’s linear discriminant

FOBT:

Fecal occult blood test

NMR:

Nuclear magnetic resonance

PC:

Principal component

PCA:

Principal component analysis

PCR:

Principal component regression

PLS:

Partial least squares

PSC:

Primary sclerosing cholangitis

SCS:

Statistical classification strategy

SIMCA:

Soft independent modelling of class analogies

WCVBST:

Weighted cross validated bootstrap

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Authors and Affiliations

  1. Institute for Biodiagnostics, National Research Council, Winnipeg, R3B 1Y6, Canada

    Ian C. P. Smith & Ray L. Somorjai

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  1. Ian C. P. Smith
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  2. Ray L. Somorjai
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Correspondence to Ian C. P. Smith.

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Smith, I.C.P., Somorjai, R.L. Deriving biomedical diagnostics from NMR spectroscopic data. Biophys Rev 3, 47–52 (2011). https://doi.org/10.1007/s12551-011-0045-8

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