Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: Analytical and Bioanalytical Chemistry, Jahrgang 414, Nr. 22, 09.2022, S. 6635-6645.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra
AU - Reuschenbach, Max
AU - Hohrenk-Danzouma, Lotta L.
AU - Schmidt, Torsten C.
AU - Renner, Gerrit
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022/9
Y1 - 2022/9
N2 - High-resolution mass spectrometry is widely used in many research fields allowing for accurate mass determinations. In this context, it is pretty standard that high-resolution profile mode mass spectra are reduced to centroided data, which many data processing routines rely on for further evaluation. Yet information on the peak profile quality is not conserved in those approaches; i.e., describing results reliability is almost impossible. Therefore, we overcome this limitation by developing a new statistical parameter called data quality score (DQS). For the DQS calculations, we performed a very fast and robust regression analysis of the individual high-resolution peak profiles and considered error propagation to estimate the uncertainties of the regression coefficients. We successfully validated the new algorithm with the vendor-specific algorithm implemented in Proteowizard’s msConvert. Moreover, we show that the DQS is a sum parameter associated with centroid accuracy and precision. We also demonstrate the benefit of the new algorithm in nontarget screenings as the DQS prioritizes signals that are not influenced by non-resolved isobaric ions or isotopic fine structures. The algorithm is implemented in Python, R, and Julia programming languages and supports multi- and cross-platform downstream data handling.
AB - High-resolution mass spectrometry is widely used in many research fields allowing for accurate mass determinations. In this context, it is pretty standard that high-resolution profile mode mass spectra are reduced to centroided data, which many data processing routines rely on for further evaluation. Yet information on the peak profile quality is not conserved in those approaches; i.e., describing results reliability is almost impossible. Therefore, we overcome this limitation by developing a new statistical parameter called data quality score (DQS). For the DQS calculations, we performed a very fast and robust regression analysis of the individual high-resolution peak profiles and considered error propagation to estimate the uncertainties of the regression coefficients. We successfully validated the new algorithm with the vendor-specific algorithm implemented in Proteowizard’s msConvert. Moreover, we show that the DQS is a sum parameter associated with centroid accuracy and precision. We also demonstrate the benefit of the new algorithm in nontarget screenings as the DQS prioritizes signals that are not influenced by non-resolved isobaric ions or isotopic fine structures. The algorithm is implemented in Python, R, and Julia programming languages and supports multi- and cross-platform downstream data handling.
KW - Centroiding
KW - Data processing
KW - Data quality
KW - HRMS
KW - Chemistry
UR - http://www.scopus.com/inward/record.url?scp=85134558702&partnerID=8YFLogxK
U2 - 10.1007/s00216-022-04224-y
DO - 10.1007/s00216-022-04224-y
M3 - Journal articles
C2 - 35871703
AN - SCOPUS:85134558702
VL - 414
SP - 6635
EP - 6645
JO - Analytical and Bioanalytical Chemistry
JF - Analytical and Bioanalytical Chemistry
SN - 1618-2642
IS - 22
ER -