Shotgun proteomics experiments integrate a complex sequence of processes, any of which can introduce variability. Quality metrics computed from LC-MS/MS data have relied upon identifying MS/MS scans, but a new mode for the QuaMeter software produces metrics that are independent of identifications. Rather than evaluating each metric independently, we have created a robust multivariate statistical toolkit that accommodates the correlation structure of these metrics and allows for hierarchical relationships among data sets. The framework enables visualization and structural assessment of variability. Study 1 for the Clinical Proteomics Technology Assessment for Cancer (CPTAC), which analyzed three replicates of two common samples at each of two time points among 23 mass spectrometers in nine laboratories, provided the data to demonstrate this framework, and CPTAC Study 5 provided data from complex lysates under Standard Operating Procedures (SOPs) to complement these findings. Identification-independent quality metrics enabled the differentiation of sites and run-times through robust principalcomponents analysis and subsequent factor analysis. Dissimilarity metrics revealed outliers in performance, and a nested ANOVA model revealed the extent to which all metrics or individual metrics were impacted by mass spectrometer and run time. Study 5 data revealed that even when SOPs have been applied, instrument-dependent variability remains prominent, although it may bereduced, while within-site variability is reduced significantly. Finally, identification-independent quality metrics were shown to bepredictive of identification sensitivity in these data sets. QuaMeter and the associated multivariate framework are available from http://fenchurch.mc.vanderbilt.edu and http://homepages.uc.edu/~wang2x7/, respectively

%B Analytical Chemistry
%V 86
%P 2497 − 2509
%G eng
%U http://pubs.acs.org/doi/pdf/10.1021/ac4034455
%R dx.doi.org/10.1021
%0 Journal Article
%J Cement Concrete and Aggregates
%D 2000
%T Quantitative description of coarse aggregate volume fraction gradients
%A A. F. Karr
%A S.S. Jaiswal
%A T. Igusa
%A J.D. Picka
%A S. P. Shah
%X Within any cast cylinder of concrete, the coarse aggregate will tend to be inhomogeneously distributed. This variability may arise as a result of segregation caused by gravity or as a result of the wall effect that is caused by the inability of the aggregate to penetrate the walls of the mold. Using methods from image analysis, stereology, and statistics, local estimates of aggregate inhomogeniety are defined that quantify phenomena that have been qualitatively described in the past. These methods involve modification of the two-dimensional images to prepare them for analysis, as well as simple diagnostic statistics for determining the presence of a wall effect. While the techniques presented herein are developed specifically for cast cylinders, they can be generalized to other cast or cored concrete specimens.

%B Cement Concrete and Aggregates %V 22 %P 151-159 %G eng %R 10.1520/CCA10473J %0 Journal Article %J In Transporta tion Research Part C: Emerging Technologies %D 1996 %T Quality of Information given by Advanced Traveler Information Systems %A Piyushimita Thakuriah %A Sen, Ashish %B In Transporta tion Research Part C: Emerging Technologies %V 4 %P 249 - 266 %G eng %0 Journal Article %J Journal of Educational and Behavioral Statistics %D 1995 %T Questioning Multilevel Models %A de Leeuw, Jan %A Kreft, Ita G.G. %XIn this article, practical problems with multilevel techniques are discussed. These problems, brought to our attention by the National Center for Education Statistics (NCES), have to do with terminology, computer programs employing different algorithms, and interpretations of the coefficients in one or two steps. We discuss the usefulness of the hierarchical linear model (HM) in the most common situation in education-that of a large number of relatively small groups. We also point to situations where the more complicated HMs can be replaced with simpler models, with statistical properties that are easier to study. We conclude that more studies need to be done to establish the claimed superiority of restricted versus unrestricted maximum likelihood, to study the effects of shrinkage on the estimators, and to explore the merits of simpler methods such as weighted least squares. Finally, distinctions must be made between choice of model, choice of technique, choice of algorithm, and choice of computer program. While HMs are an elegant conceptualization, they are not always necessary. Traditional techniques perform as well, or better, if there are large groups and small intraclass correlations, and if the researcher is interested only in the fixed-level regression coefficients.

%B Journal of Educational and Behavioral Statistics %V 20 %P 171-189 %G eng