Deconvolution using signal segmentation

Extraction of peak areas and mass spectral information from chromatography mass spectral data such as obtained in metabolomics measurements requires much effort and the quality is often subjective to the operator that handles the data at hand. In multiple file deconvolution, all samples are processed simultaneously and alignment issues are part of the modeling strategy. However, processing the total data set as a whole is an impossible task and therefore the data processing task requires segmentation. Two intertwined divide and conquer strategies are proposed. The first strategy divides the retention time axis into equal parts and the second strategy divides the total data set into a model and a prediction data set. Dividing the data into smaller segments allows us to conquer the total problem. Post processing of the resulting matrices with peak areas and mass spectra ensures that a matrix with peak areas ready for statistics and a matrix with mass spectral information ready for peak annotation is obtained. The proposed methodology is implemented within a package called TNO-DECO but can easily be implemented in other data pre-processing approaches.