| 摘要 |
An automatic peak selection method for multidimensional data can efficiently select peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data. 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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