|
Activity Number:
|
427
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 9, 2006 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #307430 |
|
Title:
|
Data Normalization of Stable-Isotope Labeled Peptides in Mass Spectrometry
|
|
Author(s):
|
Douglas Mahoney*+ and Ann L. Oberg and Jeanette E. Eckel-Passow and Terry M. Therneau and Suresh T. Chari and Unnikrishnan Gopinathan and Lawrence E. Ward and Xuan-Mai T. Persson and Sreekumar Raghavakaimal
|
|
Companies:
|
Mayo Clinic College of Medicine and Mayo Clinic College of Medicine and Mayo Clinic College of Medicine and Mayo Clinic College of Medicine and Mayo Clinic College of Medicine and Mayo Clinic College of Medicine and Mayo Clinic College of Medicine and Mayo Clinic College of Medicine and Mayo Clinic College of Medicine
|
|
Address:
|
201 1st Street, SW, Rochester, MN, 55902,
|
|
Keywords:
|
mass spectrometry ; normalization ; stable-isotope labeling ; proteomics ; iTRAQ ; O18
|
|
Abstract:
|
Mass spectrometry (MS) along with stable-isotope labeling (SIL) affords relative quantification of the global proteome in two or more biospecimens simultaneously. Unlike gene expression arrays which assay a predetermined set of genes with fixed probe sequences, MS requires algorithms that locate and quantify features that denote potential peptides per spectra. The nature of this global proteome scan results in a data matrix containing many censored values due to detection thresholds or peptides not being present in some biospecimens. This complicates normalization procedures which allow results to be compared across experimental runs. MVA plots reveal non-linear trends in bias between experimental runs similar to those seen in two color microarrays. Statistical issues encountered in extending normalization algorithms developed on microarray data to SIL MS experiments will be discussed.
|
