Jeffrey S. Morris

A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series

Josue G. Martinez et al. — Wed, 20 Feb 2013 07:05:40 PST

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable widow overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

Global quantitative assessment of the colorectal polyp burden in familial adenomatous polyposis using a Web-based tool

Patrick M. Lynch et al. — Mon, 28 Jan 2013 12:26:45 PST

Background: Accurate measures of the total polyp burden in familial adenomatous polyposis (FAP) are lacking. Current assessment tools include polyp quantitation in limited-field photographs and qualitative total colorectal polyp burden by video.

Objective: To develop global quantitative tools of the FAP colorectal adenoma burden.

Design: A single-arm, phase II trial.

Patients: Twenty-seven patients with FAP.

Intervention: Treatment with celecoxib for 6 months, with before-treatment and after-treatment videos posted to an intranet with an interactive site for scoring.

Main Outcome Measurements: Global adenoma counts and sizes (grouped into categories: less than 2 mm, 2-4 mm, and greater than 4 mm) were scored from videos by using a novel Web-based tool. Baseline and end-of-study adenoma burden results were summarized by using 5 models. Correlations between pairs of reviewers were analyzed for each model.

Results: Interobserver agreement was high for all 5 measures of polyp burden. Measures that used both polyp count and polyp size had better interobserver agreement than measures based only on polyp count. The measure in which polyp counts were weighted according to diameter, calculated as 1 * (no. of polyps < 2 mm) + 3 * (no. of polyps 2-4 mm) + 5 * (no. of polyps greater than 4 mm) had the highest interobserver agreement (Pearson correlation= 0.978 for two gastroenterologists, 0.786 and 0.846 for the surgeon vs each gastroenterologist). Treatment reduced the polyp burden by these measurements in 70% to 89% of patients (P<.001).

Limitations: Phase II study.

Conclusion: This novel, Web-based polyp scoring method provides a convenient and reproducible way to quantify the global colorectal adenoma burden in FAP patients and a framework for developing a clinical staging system for FAP. (Gastrointest Endosc 2013;xx:xxx.)

Integrative Bayesian Analysis of High-Dimensional Multi-Platform Genomics Data

Wenting Wang et al. — Fri, 02 Nov 2012 12:31:05 PDT

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.

Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms into one model.

Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using The Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma.

Availability: http://odin.mdacc.tmc.edu/˜vbaladan/

A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Modeling of Nonstationary Time Series Data with Time-Dependent Spectra

Josue G. Martinez et al. — Mon, 01 Oct 2012 09:15:42 PDT

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

Robust Classification of Functional and Quantitative Image Data using Functional Mixed Models

Hongxiao Zhu et al. — Thu, 17 Nov 2011 12:31:38 PST

In this paper, we introduce classification of complex high dimensional functional data in the functional mixed model (FMM) framework. The FMM relates a functional response to a set of scalar predictors through functional fixed and random effects, and therefore is able to account for various factors that affecting the functions and inducing correlations. Classification is performed through training the data by treating the class as one of the fixed effects, and then predicting on the test data using posterior predictive probabilities. Through a Bayesian scheme, we are able to incorporate not only all factors that influencing the functions, but also factors that directly affect class designation. While this classification method is general for all FMM methods, we provide details for two specific Bayesian approaches, the Gaussian, wavelet-based functional mixed model (G-WFMM) and the robust, wavelet-based functional mixed model (R-WFMM). Both methods perform modeling in the wavelet space, which yields parsimonious representations for the functions, and can naturally adapt to local features, and accommodates various nonstationarities. The R-WFMM has the additional advantage of allowing potentially heavier tails for features of the functions indexed by particular wavelet coefficients, leading to a down-weighting of outliers that makes the method robust to outlying functions or regions of functions. The models are applied to a real mass spectroscopy dataset in pancreatic cancer research. Our results show improved classification when comparing FMM with other typical functional data classification methods and the ad hoc methods that are based on detected spectral peaks.

Statistical Methods for Proteomic Biomarker Discovery Based on Feature Extraction or Functional Modeling Approaches

Jeffrey S. Morris — Tue, 27 Sep 2011 07:36:39 PDT

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry (Cromwell ) and 2D gel electrophoresis (Pinnacle) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods presented are applied to two specific proteomic technologies, MALDI-TOF and 2D gel electrophoresis, these methods and the other principles discussed in the paper apply much more broadly to other expression proteomics technologies.

Robust, Adaptive Functional Regression in Functional Mixed Model Framework

Hongxiao Zhu et al. — Fri, 11 Mar 2011 12:07:48 PST

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images),and using other invertible transformations as alternatives to wavelets.

Members’ Discoveries: Fatal flaws in cancer research

Jeffrey S. Morris — Wed, 26 May 2010 14:52:47 PDT

A recent article published in The Annals of Applied Statistics (AOAS) by two MD Anderson researchers—Keith Baggerly and Kevin Coombes—dissects results from a highly-influential series of medical papers involving genomics-driven personalized cancer therapy, and outlines a series of simple yet fatal flaws that raises serious questions about the veracity of the original results. Having immediate and strong impact, this paper, along with related work, is providing the impetus for new standards of reproducibility in scientific research.

Informatics and Statistics for Analyzing 2-D Gel Electrophoresis Images

Andrew W. Dowsey et al. — Wed, 26 May 2010 14:48:20 PDT

Whilst recent progress in ‘shotgun’ peptide separation by integrated liquid chromatography and mass spectrometry (LC/MS) has enabled its use as a sensitive analytical technique, proteome coverage and reproducibility is still limited and obtaining enough replicate runs for biomarker discovery is a challenge. For these reasons, recent research demonstrates the continuing need for protein separation by two-dimensional gel electrophoresis (2-DE). However, with traditional 2-DE informatics, the digitized images are reduced to symbolic data though spot detection and quantification before proteins are compared for differential expression by spot matching. Recently, a more robust and automated paradigm has emerged where gels are directly aligned in the image domain before spots are detected across the whole image set as a whole. In this chapter we describe the methodology for both approaches and discuss the pitfalls present when reasoning statistically about the differential protein expression discovered.

Statistical Contributions to Proteomic Research

Jeffrey S. Morris et al. — Wed, 26 May 2010 14:39:39 PDT

Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.

Bayesian Random SegmentationModels to Identify Shared Copy Number Aberrations for Array CGH Data

Veerabhadran Baladandayuthapani et al. — Wed, 26 May 2010 14:35:50 PDT

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to local false discovery rates for both single and multiple groups. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set.

Wavelet-based functional linear mixed models: an application to measurement error–corrected distributed lag models

Elizabeth J. Malloy et al. — Wed, 26 May 2010 14:28:28 PDT

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1–7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.

Automated Analysis of Quantitative Image Data Using Isomorphic Functional Mixed Models with Application to Proteomics Data

Jeffrey S. Morris et al. — Wed, 17 Feb 2010 14:39:19 PST

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a united analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is exible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one example. With suitable modeling choices, this approach leads to efficient calculations and can result in exible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method frequently correspond to subregions of visible spots that may represent post-translational modifications or co-migrating proteins that cannot be visually resolved from adjacent, more abundant proteins on the gel image. Thus, it is possible that this image-based approach may actually improve the realized resolution of the gel, revealing differentially expressed proteins that would not have even been detected as spots by modern spot-based analyses.

Evaluating the performance of new approaches to spot quantification and differential expression in 2-dimensional gel electrophoresis studies

Jeffrey S. Morris et al. — Sun, 15 Feb 2009 04:27:12 PST

2-DE is an important method for proteomics. Accurate spot detection and quantification on the resulting images is a challenging task, but must be done effectively for the technology to fulfill its potential. Traditional analytical methods have significant weaknesses, including spot mismatching and missing data. These problems require time-consuming manual editing to correct, which dramatically decreases throughput and compromises the objectivity and reproducibility of the analysis. To address this issue, we developed Pinnacle, a new method that markedly improves spot detection and quantification precision. Another new method implemented in Progenesis SameSpots, a commercial 2-DE analysis product, has also been touted as an improvement upon traditional approaches. In this study, we compared Pinnacle and SameSpots in spot detection and precision of quantification using two different dilution series, and evaluated the detection of differentially expressed proteins in two differential expression experiments. We found that SameSpots at times had problems with spot delineation, while Pinnacle did not. While both Pinnacle and SameSpots showed marked improvement in precision of spot quantifications over conventional methods, Pinnacle yielded spot quantifications with greater validity and reliability than SameSpots. Pinnacle detected more differentially expressed proteins than SameSpots, which may be a result of its increased precision and improved spot delineation.

Statistical Issues in Proteomic Research

Jeffrey S. Morris — Thu, 31 Jul 2008 11:03:24 PDT

Microproteomics: Analysis of protein diversity in small samples

Howard B. Gutstein et al. — Fri, 13 Jun 2008 14:38:53 PDT

Proteomics, the large-scale study of protein expression in organisms, offers the potential to evaluate global changes in protein expression and their post-translational modifications that take place in response to normal or pathological stimuli. One challenge has been the requirement for substantial amounts of tissue in order to perform comprehensive proteomic characterization. In heterogeneous tissues, such as brain, this has limited the application of proteomic methodologies. Efforts to adapt standard methods of tissue sampling, protein extraction, arraying, and identification are reviewed, with an emphasis on those appropriate to smaller samples ranging in size from several microliters down to single cells. The effects of miniaturization on these analyses are highlighted using neuroscience-related examples, as are statistical issues unique to the high-dimensional datasets generated by proteomic experiments.

Pinnacle: A Fast, Automatic Method for Detecting and Quantifying Protein Spots in 2-Dimensional Gel Electrophoresis Data

Jeffrey S. Morris et al. — Tue, 04 Dec 2007 09:44:53 PST

Motivation: One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust, and reproducible methods for detecting, matching, and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels, and finally quantifying each spot by calculating normalized spot volumes. This approach is time con-suming, error-prone, and frequently requires extensive manual edit-ing, which can unintentionally introduce bias into the results.

Results: We introduce a new method for spot detection and quanti-fication called Pinnacle that is automatic, quick, sensitive and spe-cific, and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller CVs. More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences.

Availability: Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.

Laser capture sampling and analytical issues in proteomics

Howard Gutstein et al. — Tue, 04 Dec 2007 09:35:54 PST

Proteomics holds the promise of evaluating global changes in protein expression and post-translational modificaiton in response to environmental stimuli. However, difficulties in achieving cellular anatomic resolution and extracting specific types of proteins from cells have limited the efficacy of these techniques. Laser capture microdissection has provided a solution to the problem of anatomical resolution in tissues. New extraction methodologies have expanded the range of proteins identified in subsequent analyses. This review will examine the application of laser capture microdissection to proteomic tissue sampling, and subsequent extraction of these samples for differential expression analysis. Statistical and other quantitative issues important for the analysis of the highly complex datasets generated are also reviewed.

Wavelet-based functional mixed model analysis: Computational considerations

Richard C. Herrick et al. — Wed, 04 Apr 2007 12:48:45 PDT

Wavelet-based Functional Mixed Models is a new Bayesian method extending mixed models to irregular functional data (Morris and Carroll, JRSS-B, 2006). These data sets are typically very large and can quickly run into memory and time constraints unless these issues are carefully dealt with in the software. We reduce runtime by 1.) identifying and optimizing hotspots, 2.) using wavelet compression to do less computation with minimal impact on results, and 3.) dividing the code into multiple executables to be run in parallel using a grid computing resource. We discuss rules of thumb for estimating memory requirements and computation times in terms of model and data set parameters. We present examples and benchmarks demonstrating that it is practical to analyze very large data sets with readily available computing resources. This code is freely available on our website.

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 et al. — Thu, 14 Dec 2006 14:30:55 PST

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 function of a covariate for studies where both responses are measured on the same experimental units but not the same subsampling units. Parametric and nonparametric methods are developed and applied to a dataset from an ongoing study, leading to potentially important and surprising biological results. Theoretical calculations suggest that the nonparametric method, based on nonparametric regression, should in fact have statistical properties nearly the same as if the functions nonparametrically estimated were known. The methodology used in this article can be applied to other settings when the goal of the study is to model the correlation of two continuous repeated measurement responses as a function of a covariate, whereas the two responses of interest can be measured on the same experimental units but not on the same subsampling units. In our example, the two responses were measured in two different regions of the colon.