title: Statistical design of experiments as a tool in mass spectrometry.
abstract: This Tutorial is an introduction to statistical design of experiments (DOE) with focus on demonstration of how DOE can be useful to the mass spectrometrist. In contrast with the commonly used one factor at a time approach, DOE methods address the issue of interaction of variables and are generally more efficient. The complex problem of optimizing data-dependent acquisition parameters in a bottom-up proteomics LC-MS/MS analysis is used as an example of the power of the technique. Using DOE, a new data-dependent method was developed that improved the quantity of confidently identified peptides from rat serum.
authors.affiliation: Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285, USA. riter_leah_stacey@lilly.com
authors.forename: Leah S
authors.initials: LS
authors.lastname: Riter
authors.forename: Olga
authors.initials: O
authors.lastname: Vitek
authors.forename: Karen M
authors.initials: KM
authors.lastname: Gooding
authors.forename: Barry D
authors.initials: BD
authors.lastname: Hodge
authors.forename: Randall K
authors.initials: RK
authors.lastname: Julian
mesh_terms: D000818:Animals; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D010455:Peptides / Q000097:blood; D040901:Proteomics / Q000379:methods*; D051381:Rats; D012107:Research Design; D012680:Sensitivity and Specificity
chemical_list: D010455:Peptides