Proteomics

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Signatures for Mass Spectrometry Data Quality, part 3 of 5

ABSTRACT: Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.

INSTRUMENT(S): Exactive, Q Exactive

ORGANISM(S): Shewanella Oneidensis (strain Mr-1)

SUBMITTER: Matthew Monroe  

PROVIDER: PXD000322 | Pride | 2013-10-04

REPOSITORIES: Pride

Json Xml

Dataset's files

Source:
Action DRS
QC_1-Frodo_12-2-34.mgf Mgf
QC_1-Frodo_12-2-34.raw Raw
QC_1-Frodo_12-2-34_msgfplus.mzid Mzid
QC_1_01102013_CE24_Top12.mgf Mgf
QC_1_01102013_CE24_Top12.raw Raw
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Publications

Ensuring data quality and proper instrument functionality is a prerequisite for scientific investigation. Manual quality assurance is time-consuming and subjective. Metrics for describing liquid chromatography mass spectrometry (LC-MS) data have been developed; however, the wide variety of LC-MS instruments and configurations precludes applying a simple cutoff. Using 1150 manually classified quality control (QC) data sets, we trained logistic regression classification models to predict whether a  ...[more]

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