Dataset Information

Signatures for Mass Spectrometry Data Quality, part 2 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 Orbitrap

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

SUBMITTER: Matthew Monroe

PROVIDER: PXD000321 | Pride | 2013-10-03

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	QC_Dataset_Details.xlsx	Xlsx
	QC_ShewAndreySyncCh2.RAW	Raw
	QC_ShewAndreySyncCh2.mgf	Mgf
	QC_ShewAndreySyncCh2_msgfplus.mzid	Mzid
	QC_Shew_021313_3.RAW	Raw

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Signatures for mass spectrometry data quality.

Amidan Brett G BG Orton Daniel J DJ Lamarche Brian L BL Monroe Matthew E ME Moore Ronald J RJ Venzin Alexander M AM Smith Richard D RD Sego Landon H LH Tardiff Mark F MF Payne Samuel H SH

Journal of proteome research 20140324 4

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]

PMID: 24611607

Dataset Information

Signatures for Mass Spectrometry Data Quality, part 2 of 5

Dataset's files

Publications

Signatures for mass spectrometry data quality.

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