Gonzalez-Fernandez, A; Dexter, A; Nikula, C; Bunch, J (2023) NECTAR: A new algorithm for characterising and correcting noise in QToF-mass spectrometry imaging data. Journal of The American Society for Mass Spectrometry, 34 (11). pp. 2443-2453.

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Abstract

A typical mass spectrometry imaging experiment yields a very high number of detected peaks, many of which are noise, and thus unwanted. To select only peaks of interest, data pre-processing tasks are applied to raw data. A statistical study to characterise three types of noise in MSI Q-ToF data (random, chemical and background noise) is presented through NECTAR, a new NoisE CorrecTion AlgoRithm. Random noise is confirmed to be dominant at lower m/z values (~ 50 - 400 Da) while systematic chemical noise dominates at higher m/z values (> 400 Da). A statistical approach is presented to demonstrate that chemical noise can be corrected to reduce its presence by a factor of ~3. Reducing this effect helps to determine a more reliable baseline in the spectrum and therefore a more reliable noise level. Peaks are classified according to their spatial S/N on the single ion images, and background noise is thus removed from the list of peaks of interest. PCA and t-SNE multivariate analysis reviews of the top 4000 peaks and the final 744 de-noised peak list suggests an effective removal of un-informative peaks and proper selection of relevant peaks. This new algorithm was applied to MALDI Q-ToF data generated from the analysis of a mouse pancreatic tissue section to demonstrate its applicability and ability to filter out these types of noise in a relevant dataset.

Item Type:	Article
Keywords:	MSI, noise characterisation, noise determination
Subjects:	Mathematics and Scientific Computing > Signal Processing
Divisions:	Chemical & Biological Sciences
Identification number/DOI:	10.1021/jasms.3c00116
Last Modified:	06 Sep 2024 12:48
URI:	https://eprintspublications.npl.co.uk/id/eprint/9999