|Grant Number:||5R01CA107304-07 Interpret this number|
|Primary Investigator:||Morris, Jeffrey|
|Organization:||University Of Tx Md Anderson Can Ctr|
|Project Title:||Adaptive Methodology for Functional Biomedical Data|
DESCRIPTION (provided by applicant): An ever-increasing number of biomedical studies yield functional data sampled on a fine grid. These type of data are frequently high dimensional and complex with many irregular features like peaks and change points. There is currently a dearth of existing rigorous statistical methods for analyzing this type of data. The goal of this research program is to develop new Bayesian methodology that provides a unified framework for modeling and performing inference on samples of curves that is flexible enough to apply to a variety of applications, from various experimental designs, and can answer a broad range of research questions. 1. We will develop new methodology within the wavelet-based functional mixed model framework that accommodates outlying curves, a broader class of within- curve covariance structures, and higher dimensional functional data, making it applicable to a broad range of functional data. 2. We will develop methods to classify individuals based on their functional data, e.g. proteomic profiles, in a way that allows us to combine information across functional and scalar factors of multiple sources. We will develop methods to perform Bayesian functional hypothesis testing. 3. We will develop adaptive methods for relating functional predictors to functional responses. 4. We will develop methods for adaptive functional principal components analysis and for principal component-based functional mixed models, which represents a data-driven modeling framework that is extremely flexible in taking into account the complex structure that may be present in the functional data. 5. We will apply the methods to a number of cancer-related studies yielding functional data, including various types of proteomics and genomics data. 6. We will develop efficient, easy-to-use, freely available code to fit the methods described in this proposal.
Bayesian function-on-function regression for multilevel functional data.
Authors: Meyer MJ, Coull BA, Versace F, Cinciripini P, Morris JS
Source: Biometrics, 2015 Mar 18;null, p. null.
EPub date: 2015 Mar 18.
Automatic quantitative analysis of ultrasound tongue contours via wavelet-based functional mixed models.
Authors: Lancia L, Rausch P, Morris JS
Source: J Acoust Soc Am, 2015 Feb;137(2), p. EL178-83.
Human scleral structural stiffness increases more rapidly with age in donors of African descent compared to donors of European descent.
Authors: Fazio MA, Grytz R, Morris JS, Bruno L, Girkin CA, Downs JC
Source: Invest Ophthalmol Vis Sci, 2014 Sep 18;55(11), p. 7189-98.
EPub date: 2014 Sep 18.
A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series.
Authors: Martinez JG, Bohn KM, Carroll RJ, Morris JS
Source: J Am Stat Assoc, 2013 Jun 1;108(502), p. 514-526.
Age-related changes in human peripapillary scleral strain.
Authors: Fazio MA, Grytz R, Morris JS, Bruno L, Gardiner SK, Girkin CA, Downs JC
Source: Biomech Model Mechanobiol, 2014 Jun;13(3), p. 551-63.
EPub date: 2013 Jul 30.
iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data.
Authors: Wang W, Baladandayuthapani V, Morris JS, Broom BM, Manyam G, Do KA
Source: Bioinformatics, 2013 Jan 15;29(2), p. 149-59.
EPub date: 2012 Nov 9.
Robust classification of functional and quantitative image data using functional mixed models.
Authors: Zhu H, Brown PJ, Morris JS
Source: Biometrics, 2012 Dec;68(4), p. 1260-8.
EPub date: 2012 Jun 6.
Robust, Adaptive Functional Regression in Functional Mixed Model Framework.
Authors: Zhu H, Brown PJ, Morris JS
Source: J Am Stat Assoc, 2011 Sep 1;106(495), p. 1167-1179.
Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.
Authors: Morris JS
Source: Stat Interface, 2012 Jan 1;5(1), p. 117-135.
Reproducibility of SELDI Spectra Across Time and Laboratories.
Authors: Diao L, Clarke CH, Coombes KR, Hamilton SR, Roth J, Mao L, Czerniak B, Baggerly KA, Morris JS, Fung ET, Bast RC Jr
Source: Cancer Inform, 2011 Mar 14;10, p. 45-64.
EPub date: 2011 Mar 14.
Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data.
Authors: Baladandayuthapani V, Ji Y, Talluri R, Nieto-Barajas LE, Morris JS
Source: J Am Stat Assoc, 2010 Dec;105(492), p. 1358-1375.
AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.
Authors: Morris JS, Baladandayuthapani V, Herrick RC, Sanna P, Gutstein H
Source: Ann Appl Stat, 2011 Jan 1;5(2A), p. 894-923.
Image analysis tools and emerging algorithms for expression proteomics.
Authors: Dowsey AW, English JA, Lisacek F, Morris JS, Yang GZ, Dunn MJ
Source: Proteomics, 2010 Dec;10(23), p. 4226-57.
Statistical contributions to proteomic research.
Authors: Morris JS, Baggerly KA, Gutstein HB, Coombes KR
Source: Methods Mol Biol, 2010;641, p. 143-66.
Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.
Authors: Malloy EJ, Morris JS, Adar SD, Suh H, Gold DR, Coull BA
Source: Biostatistics, 2010 Jul;11(3), p. 432-52.
EPub date: 2010 Feb 15.
Informatics and statistics for analyzing 2-d gel electrophoresis images.
Authors: Dowsey AW, Morris JS, Gutstein HB, Yang GZ
Source: Methods Mol Biol, 2010;604, p. 239-55.
Evaluating the performance of new approaches to spot quantification and differential expression in 2-dimensional gel electrophoresis studies.
Authors: Morris JS, Clark BN, Wei W, Gutstein HB
Source: J Proteome Res, 2010 Jan;9(1), p. 595-604.
Youth destinations associated with objective measures of physical activity in adolescents.
Authors: Cradock AL, Melly SJ, Allen JG, Morris JS, Gortmaker SL
Source: J Adolesc Health, 2009 Sep;45(3 Suppl), p. S91-8.
Microproteomics: analysis of protein diversity in small samples.
Authors: Gutstein HB, Morris JS, Annangudi SP, Sweedler JV
Source: Mass Spectrom Rev, 2008 Jul-Aug;27(4), p. 316-30.
Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data.
Authors: Morris JS, Clark BN, Gutstein HB
Source: Bioinformatics, 2008 Feb 15;24(4), p. 529-36.
EPub date: 2008 Jan 14.
Laser capture sampling and analytical issues in proteomics.
Authors: Gutstein HB, Morris JS
Source: Expert Rev Proteomics, 2007 Oct;4(5), p. 627-37.
Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models.
Authors: Morris JS, Brown PJ, Herrick RC, Baggerly KA, Coombes KR
Source: Biometrics, 2008 Jun;64(2), p. 479-89.
EPub date: 2007 Sep 20.
Characteristics of school campuses and physical activity among youth.
Authors: Cradock AL, Melly SJ, Allen JG, Morris JS, Gortmaker SL
Source: Am J Prev Med, 2007 Aug;33(2), p. 106-113.
Clustering of time-course gene expression data using functional data analysis.
Authors: Song JJ, Lee HJ, Morris JS, Kang S
Source: Comput Biol Chem, 2007 Aug;31(4), p. 265-74.
EPub date: 2007 Jun 2.
Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study.
Authors: Morris JS, Arroyo C, Coull BA, Ryan LM, Herrick R, Gortmaker SL
Source: J Am Stat Assoc, 2006 Dec 1;101(476), p. 1352-1364.
PrepMS: TOF MS data graphical preprocessing tool.
Authors: Karpievitch YV, Hill EG, Smolka AJ, Morris JS, Coombes KR, Baggerly KA, Almeida JS
Source: Bioinformatics, 2007 Jan 15;23(2), p. 264-5.
EPub date: 2006 Nov 22.
Wavelet-based functional mixed models.
Authors: Morris JS, Carroll RJ
Source: J R Stat Soc Series B Stat Methodol, 2006 Apr 1;68(2), p. 179-199.
Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform.
Authors: Coombes KR, Tsavachidis S, Morris JS, Baggerly KA, Hung MC, Kuerer HM
Source: Proteomics, 2005 Nov;5(16), p. 4107-17.
The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales.
Authors: Hu J, Coombes KR, Morris JS, Baggerly KA
Source: Brief Funct Genomic Proteomic, 2005 Feb;3(4), p. 322-31.
Serum proteomics profiling--a young technology begins to mature.
Authors: Coombes KR, Morris JS, Hu J, Edmonson SR, Baggerly KA
Source: Nat Biotechnol, 2005 Mar;23(3), p. 291-2.
Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer.
Authors: Baggerly KA, Morris JS, Edmonson SR, Coombes KR
Source: J Natl Cancer Inst, 2005 Feb 16;97(4), p. 307-9.
Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum.
Authors: Morris JS, Coombes KR, Koomen J, Baggerly KA, Kobayashi R
Source: Bioinformatics, 2005 May 1;21(9), p. 1764-75.
EPub date: 2005 Jan 26.
Understanding the characteristics of mass spectrometry data through the use of simulation.
Authors: Coombes KR, Koomen JM, Baggerly KA, Morris JS, Kobayashi R
Source: Cancer Inform, 2005;1, p. 41-52.
Bias, randomization, and ovarian proteomic data: a reply to "producers and consumers".
Authors: Baggerly KA, Coombes KR, Morris JS
Source: Cancer Inform, 2005;1, p. 9-14.
Cutaneous angiosarcoma of the scalp: a multidisciplinary approach.
Authors: Pawlik TM, Paulino AF, McGinn CJ, Baker LH, Cohen DS, Morris JS, Rees R, Sondak VK
Source: Cancer, 2003 Oct 15;98(8), p. 1716-26.
Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization.
Authors: Coombes KR, Fritsche HA Jr, Clarke C, Chen JN, Baggerly KA, Morris JS, Xiao LC, Hung MC, Kuerer HM
Source: Clin Chem, 2003 Oct;49(10), p. 1615-23.
A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples.
Authors: Baggerly KA, Morris JS, Wang J, Gold D, Xiao LC, Coombes KR
Source: Proteomics, 2003 Sep;3(9), p. 1667-72.