Proteomics

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Signatures for Mass Spectrometry Data Quality, part 1 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): LTQ XL ETD, LTQ, LTQ Velos

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

SUBMITTER: Matthew Monroe  

PROVIDER: PXD000320 | Pride | 2013-10-03

REPOSITORIES: Pride

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Dataset's files

Source:
Action DRS
QC_12_03_1000ng_run09_15Feb13_Rogue_12-10-04.RAW Raw
QC_12_03_1000ng_run09_15Feb13_Rogue_12-10-04.mgf Mgf
QC_12_03_1000ng_run09_15Feb13_Rogue_12-10-04_msgfplus.mzid Mzid
QC_12_03_1000ng_run10_15Feb13_Rogue_12-10-04.RAW Raw
QC_12_03_1000ng_run10_15Feb13_Rogue_12-10-04.mgf Mgf
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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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