Adapting censored regression methods to adjust for the limit of detection in the calibration of diagnostic rules for clinical mass spectrometry proteomic data
| Autor: | Bart Mertens, Werner Vach, Alexia Kakourou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Statistics and Probability FOS: Computer and information sciences Epidemiology Computer science Feature selection computer.software_genre 01 natural sciences Mass Spectrometry censored regression Set (abstract data type) Methodology (stat.ME) 010104 statistics & probability 03 medical and health sciences Health Information Management Complete information Calibration 0101 mathematics borrowing of information Statistics - Methodology Detection limit Censored regression model limit of detection prediction Random effects model 030104 developmental biology Clinical mass spectrometry-based proteomics Data Interpretation Statistical Measurement uncertainty Regression Analysis Data mining computer Algorithms variable selection |
| Zdroj: | Statistical Methods in Medical Research, 27(9), 2742-2755 |
| ISSN: | 2742-2755 |
| Popis: | Despite the recent advances in mass spectrometry (MS), summarizing and analyzing high-throughput mass-spectrometry data remains a challenging task. This is, on the one hand, due to the complexity of the spectral signal which is measured, and on the other, due to the limit of detection (LOD). The LOD is related to the limitation of instruments in measuring markers at a relatively low level. As a consequence, the outcome data set from the quantification step of proteomic analysis often consists of a reduced list of peaks where any peak intensities below the detection limit threshold are reported as missings. In this work, we propose the use of censored data methodology to handle spectral measurements within the presence of LOD, recognizing that those have been censored due to left-censoring mechanisms on low-abundance proteins. We apply this approach to the particular problem of calibrating prediction rules through prior estimation of the average isotope expression in MALDI-FTICR mass-spectrometry data, collected in the context of a pancreatic cancer case-control study. Our idea is to replace the set of incomplete spectral measurements with the average intensity estimates and use those as new input to a prediction model. We evaluate the proposed methods, with respect to their predictive ability, by comparing their performance with the one achieved using the complete information as well as alternative/competitive methods to deal with the LOD. |
| Databáze: | OpenAIRE |
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