Statistically appraising process quality of affinity isolation experiments

Julia L. Sharp, John J. Borkowski, Denise Schmoyer, Don S. Daly, Samuel Purvine, William R. Cannon and Gregory B. Hurst

Computational Statistics & Data Analysis, 2009, vol. 53, issue 5, 1720-1726

Abstract: Quality affinity isolation experiments are necessary to identify valid protein-protein interactions. Biological error, processing error, and random variability can reduce the quality of an experiment, and thus hinder the identification of protein interaction pairs. Appraising affinity isolation assay quality is essential to inferring protein associations. An important step of the assay is the mass spectrometric identification of proteins. To evaluate this step, a known mixture of proteins is processed through a mass spectrometer as a quality control mixture. If the mass spectrometer yields unexpected results, the process is currently qualitatively evaluated, tuned, and reset. Statistical quality control (SQC) procedures, including the use of cumulative sum, the individual measurement, and moving range charts are implemented to analyze the stability of the mass spectrometric analysis. The SQC measures presented can assist in establishing preliminary control limits to identify an out-of-control process and investigate assignable causes for shifts in the process mean in real time.

Date: 2009
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00259-4
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:5:p:1720-1726

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().