Proof of principle of a generalized fuzzy Hough transform approach to peak alignment of one-dimensional 1H NMR data.
In metabolic profiling, multivariate data analysis techniques are used to interpret one-dimensional (1D) 1H NMR data. Multivariate data analysis techniques require that peaks are characterised by the same variables in every spectrum. This location constraint is essential for correct comparison of the intensities of several NMR spectra. However, variations in physicochemical factors can cause the locations of the peaks to shift. The location prerequisite may thus not be met, and so, to solve this problem, alignment methods have been developed. However, current state-of-the-art algorithms for data alignment cannot resolve the inherent problems encountered when analysing NMR data of biological origin, because they are unable to align peaks when the spatial order of the peaks changes-a commonly occurring phenomenon. In this paper a new algorithm is proposed, based on the Hough transform operating on an image representation of the NMR dataset that is capable of correctly aligning peaks when existing methods fail. The proposed algorithm was compared with current state-of-the-art algorithms operating on a selected plasma dataset to demonstrate its potential. A urine dataset was also processed using the algorithm as a further demonstration. The method is capable of successfully aligning the plasma data but further development is needed to address more challenging applications, for example urine data.
Csenki
Leonard
L
Department of Analytical Chemistry, BioSysteMetrics Group, Stockholm University, 106 91, Stockholm, Sweden.
Alm
Erik
E
Torgrip
Ralf J O
RJ
Aberg
K Magnus
KM
Nord
Lars I
LI
Schuppe-Koistinen
Ina
I
Lindberg
Johan
J
D000465:Algorithms; D002462:Cell Membrane / Q000737:chemistry / Q000378:metabolism; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000235:genetics / Q000502:physiology*; D015999:Multivariate Analysis; D012680:Sensitivity and Specificity