Peak selection in multidimensional data

Details Peak selection in multidimensional data Peak selection in multidimensional data

2005-03-29

2005-03-29

Wachsman, Hal (Department: 2857)

Data processing: measuring, calibrating, or testing

Measurement system in a specific environment

Chemical analysis

C702S030000, C702S027000, C702S032000, C250S282000

Reexamination Certificate

active

06873915

ABSTRACT:
An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

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