Open AccessCorrecting systematic bias and instrument measurement drift with mzRefinery
Author(s) -
Bryson Gibbons,
Matthew Chambers,
Matthew Monroe,
David L. Tabb,
Samuel Payne
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv437
Subject(s) - computer science , calibration , systematic error , software , r package , data mining , quality (philosophy) , statistics , mathematics , operating system , computational science , philosophy , epistemology
Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments.
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