Statistics and Probability Commons^™

Pooling Information Across Different Studies And Oligonucleotide Microarray Chip Types To Identify Prognostic Genes For Lung Cancer., Jeffrey S. Morris, Guosheng Yin, Keith A. Baggerly, Chunlei Wu, Li Zhang

Jeffrey S. Morris

Our goal in this work is to pool information across microarray studies conducted at different institutions using two different versions of Affymetrix chips to identify genes whose expression levels offer information on lung cancer patients’ survival above and beyond the information provided by readily available clinical covariates. We combine information across chip types by identifying “matching probes” present on both chips, and then assembling them into new probesets based on Unigene clusters. This method yields comparable expression level quantifications across chips without sacrificing much precision or significantly altering the relative ordering of the samples. We fit a series of multivariable …

Serum Proteomics Profiling: A Young Technology Begins To Mature, Kevin R. Coombes, Jeffrey S. Morris, Jianhua Hu, Sarah R. Edmondson, Keith A. Baggerly

Jeffrey S. Morris

No abstract provided.

Signal In Noise: Evaluating Reported Reproducibility Of Serum Proteomic Tests For Ovarian Cancer, Keith A. Baggerly, Jeffrey S. Morris, Sarah R. Edmonson, Kevin R. Coombes

Jeffrey S. Morris

Proteomic profi ling of serum initially appeared to be dramatically effective for diagnosis of early-stage ovarian cancer, but these results have proven diffi cult to reproduce. A recent publication reported good classifi cation in one dataset using results from training on a much earlier dataset, but the authors have since reported that they did not perform the analysis as described. We examined the reproducibility of the proteomic patterns across datasets in more detail. Our analysis reveals that the pattern that enabled successful classifi cation is biologically implausible and that the method, properly applied, does not classify the data accurately. We …

High-Resolution Serum Proteomic Patterns For Ovarian Cancer Detection, Keith A. Baggerly, Sarah R. Edmonson, Jeffrey S. Morris, Kevin R. Coombes

Jeffrey S. Morris

No abstract provided.

Rejoinder To "“Wavelet-Based Nonparametric Modeling Of Hierarchical Functions In Colon Carcinogenesis.”, Jeffrey S. Morris, Marina Vannucci, Philip J. Brown, Raymond J. Carroll

Jeffrey S. Morris

No abstract provided.

Quality Control And Peak Finding For Proteomics Data Collected From Nipple Aspirate Fluid Using Surface Enhanced Laser Desorption And Ionization., Jeffrey S. Morris, Kevin R. Coombes, Herbert A. Fritsche, Charlotte Clarke, Jeng-Neng Chen, Keith A. Baggerly, Lian-Chun Xiao, Mien-Chie Hung, Henry M. Kuerer

Jeffrey S. Morris

Background: Recently, researchers have been using mass spectroscopy to study cancer. For use of proteomics spectra in a clinical setting, stringent quality-control procedures will be needed.

Methods: We pooled samples of nipple aspirate fluid from healthy breasts and breasts with cancer to prepare a control sample. Aliquots of the control sample were used on two spots on each of three IMAC ProteinChip® arrays (Ciphergen Biosystems, Inc.) on 4 successive days to generate 24 SELDI spectra. In 36 subsequent experiments, the control sample was applied to two spots of each ProteinChip array, and the resulting spectra were analyzed to determine how …

Wavelet-Based Nonparametric Modeling Of Hierarchical Functions In Colon Carcinogenesis., Jeffrey S. Morris, Marina Vannucci, Philip J. Brown, Raymond J. Carroll

Jeffrey S. Morris

In this article we develop new methods for analyzing the data from an experiment using rodent models to investigate the effect of type of dietary fat on O6-methylguanine-DNA-methyltransferase (MGMT), an important biomarker in early colon carcinogenesis. The data consist of observed profiles over a spatial variable contained within a two-stage hierarchy, a structure that we dub hierarchical functional data. We present a new method providing a unified framework for modeling these data, simultaneously yielding estimates and posterior samples for mean, individual, and subsample-level profiles, as well as covariance parameters at the various hierarchical levels. Our method is nonparametric in that …

A Comprehensive Approach To The Analysis Of Maldi-Tof Proteomics Spectra From Serum Samples., Keith A. Baggerly, Jeffrey S. Morris, Jing Wang, David Gold, Lian-Chun Xiao, Kevin R. Coombes

Jeffrey S. Morris

For our analysis of the data from the First Annual Proteomics Data Mining Conference, we attempted to discriminate between 24 disease spectra (group A) and 17 normal spectra (group B). First, we processed the raw spectra by (i) correcting for additive sinusoidal noise (periodic on the time scale) affecting most spectra, (ii) correcting for the overall baseline level, (iii) normalizing, (iv) recombining fractions, and (v) using variable- width windows for data reduction. Also, we identified a set of polymeric peaks (at multiples of 180.6 Da) that is present in several normal spectra (B1–B8). After data processing, we found the intensities …

Bayesian Shrinkage Estimation Of The Relative Abundance Of Mrna Transcripts Using Sage, Jeffrey S. Morris, Keith A. Baggerly, Kevin R. Coombes

Jeffrey S. Morris

Serial analysis of gene expression (SAGE) is a technology for quantifying gene expression in biological tissue that yields count data that can be modeled by a multinomial distribution with two characteristics: skewness in the relative frequencies and small sample size relative to the dimension. As a result of these characteristics, a given SAGE sample may fail to capture a large number of expressed mRNA species present in the tissue. Empirical estimators of mRNA species’ relative abundance effectively ignore these missing species, and as a result tend to overestimate the abundance of the scarce observed species comprising a vast majority of …

A Bayesian Analysis Involving Colonic Crypt Structure And Coordinated Response To Carcinogens Incorporating Missing Crypts, Jeffrey S. Morris, Naisyin Wang, Joanne R. Lupton, Robert S. Chapkin, Nancy D. Turner, Mee-Young Hong, Raymond J. Carroll

Jeffrey S. Morris

This paper is concerned with modeling the architecture of colonic crypts and the implications of this modeling for understanding possible coordinated response of carcinogen–induced DNA damage between various regions of the colon. The methods we develop to address these two issues are applied to a particular important example in colon carcinogenesis. We cast the problem as an unusual and not previously studied hierarchical mixed-effects model characterized by completely missing covariates in units at a structurally base level, except for some randomly selected units. Information concerning the missing covariates is available through certain known ordering constraints and surrogate measures. Our methods …

Parametric And Nonparametric Methods For Understanding The Relationship Between Carcinogen-Induced Dna Adduct Levels In Distal And Proximal Regions Of The Colon., Jeffrey S. Morris, Naisyin Wang, Joanne R. Lupton, Robert S. Chapkin, Nancy D. Turner, Mee-Young Hong, Raymond J. Carroll

Jeffrey S. Morris

An important problem in studying the etiology of colon cancer is understanding the relationship between DNA adduct levels (broadly, DNA damage) in cells within colonic crypts in distal and proximal parts of the colon, following treatment with a carcinogen and different types of diet. In particular, it is important to understand whether rats who have elevated adduct levels in particular positions in distal region crypts also have elevated levels in the same positions of the crypts in proximal regions, and whether this relationship depends on diet. We cast this problem as estimating the correlation function of two responses as a …