Dataset Information

Correcting systematic bias and instrument measurement drift with mzRefinery.

ABSTRACT:

Motivation

Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments.

Results

We introduce the mzRefinery tool for calibration of mass spectrometry data files. Using confident peptide spectrum matches, three different calibration methods are explored and the optimal transform function is chosen. After calibration, systematic bias is removed and the mass measurement errors are centered at 0?ppm. Because it is part of the ProteoWizard package, mzRefinery can read and write a wide variety of file formats.

Availability and implementation

The mzRefinery tool is part of msConvert, available with the ProteoWizard open source package at http://proteowizard.sourceforge.net/

Contact

samuel.payne@pnnl.gov.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Gibbons BC

PROVIDER: S-EPMC4653383 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Correcting systematic bias and instrument measurement drift with mzRefinery.

Gibbons Bryson C BC Chambers Matthew C MC Monroe Matthew E ME Tabb David L DL Payne Samuel H SH

Bioinformatics (Oxford, England) 20150804 23

<h4>Motivation</h4>Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments.<h4>Results</h4>We introduce the mzRefinery tool for calibration of mass spectrometry data files. Using confident peptide spectrum matches, three different calibration methods are explored and the optimal transform function is chosen. After calibration, systematic bias is removed and the mass measurement errors are centered at 0 ppm. Because it is part of ...[more]

PMID: 26243018

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Project description:BACKGROUND:Fine particulate matter (PM2.5) is associated with various adverse health outcomes. The MicroPEM (RTI, NC), a miniaturized real-time portable particulate sensor with an integrated filter for collecting particles, has been widely used for personal PM2.5 exposure assessment. Five-day deployments were targeted on a total of 142 deployments (personal or residential) to obtain real-time PM2.5 levels from children living in New York City and Baltimore. Among these 142 deployments, 79 applied high-efficiency particulate air (HEPA) filters in the field at the beginning and end of each deployment to adjust the zero level of the nephelometer. However, unacceptable baseline drift was observed in a large fraction (> 40%) of acquisitions in this study even after HEPA correction. This drift issue has been observed in several other studies as well. The purpose of the present study is to develop an algorithm to correct the baseline drift in MicroPEM based on central site ambient data during inactive time periods. METHOD:A running baseline & gravimetric correction (RBGC) method was developed based on the comparison of MicroPEM readings during inactive periods to ambient PM2.5 levels provided by fixed monitoring sites and the gravimetric weight of PM2.5 collected on the MicroPEM filters. The results after RBGC correction were compared with those using HEPA approach and gravimetric correction alone. Seven pairs of duplicate acquisitions were used to validate the RBGC method. RESULTS:The percentages of acquisitions with baseline drift problems were 42%, 53% and 10% for raw, HEPA corrected, and RBGC corrected data, respectively. Pearson correlation analysis of duplicates showed an increase in the coefficient of determination from 0.75 for raw data to 0.97 after RBGC correction. In addition, the slope of the regression line increased from 0.60 for raw data to 1.00 after RBGC correction. CONCLUSIONS:The RBGC approach corrected the baseline drift issue associated with MicroPEM data. The algorithm developed has the potential for use with data generated from other types of PM sensors that contain a filter for weighing as well. In addition, this approach can be applied in many other regions, given widely available ambient PM data from monitoring networks, especially in urban areas.

Dataset Information

Correcting systematic bias and instrument measurement drift with mzRefinery.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Correcting systematic bias and instrument measurement drift with mzRefinery.

Similar Datasets

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