Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA.
Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.
Oberg
Ann L
AL
Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, USA. oberg.ann@mayo.edu
Mahoney
Douglas W
DW
Eckel-Passow
Jeanette E
JE
Malone
Christopher J
CJ
Wolfinger
Russell D
RD
Hill
Elizabeth G
EG
Cooper
Leslie T
LT
Onuma
Oyere K
OK
Spiro
Craig
C
Therneau
Terry M
TM
Bergen
H Robert
HR
D000704:Analysis of Variance; D045424:Complex Mixtures / Q000032:analysis*; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D007553:Isotope Labeling / Q000379:methods; D007554:Isotopes; D013058:Mass Spectrometry; D040901:Proteomics / Q000379:methods
D045424:Complex Mixtures; D007554:Isotopes