Grant Number:	5R01CA157528-06 Interpret this number
Primary Investigator:	Schwartzman, Armin
Organization:	University Of California, San Diego
Project Title:	Multiple Testing Methods for Random Fields and High-Dimensional Dependent Data
Fiscal Year:	2017

DESCRIPTION (provided by applicant): Large-scale multiple testing has become ubiquitous in the search for disease and health risk markers using high-throughput technologies. While statistical methods for multiple testing often assume independence between the tests, many real situations exhibit dependence and an underlying structure. Examples of spatial structure are one-dimensional (1D) in the case of proteomic data; 2D in the case of environmental data; and 3D in the case of brain imaging data. Ignoring correlation in the analysis may lead to a different set and ordering of discovered features, resulting in increased error rates and potential missing of important features. There is a need to characterize the effect of correlation in multiple testing and incorporate it into the analysis. The goal of this proposal is to develop multiple testing methods that incorporate the correlation in the data in order to increase statistical power, control error rates and obtain appropriately interpretable results. This is done in two different ways. (1) In Aims 1 and 2, we assume a spatial structure and stationary ergodic correlation, where the signal of interest consists of a relatively small number of unimodal peaks. We use random field theory to compute p-values for testing the heights of local maxima of the observed data after smoothing. We develop these methods in complexity from 1D to 3D domains, and from peaks of equal width to peaks of unequal width. We then adapt and apply these methods to various types of data obtained from high-throughput technologies, specifically: mass- spectrometry data for identifying protein biomarkers of cancer; climate model output data for identification of geographical regions at risk for heat stress as a result of climate change; and brain imaging data for identification of anatomical regions involved in abnormal cognitive development. (2) In Aim 3, we assume a general correlation structure, not necessarily stationary or ergodic, and propose a conditional marginal analysis, where correlation is incorporated through conditioning on the observed marginal distribution of likely null cases. Although not exclusively, emphasis throughout is placed on false discovery rate inference. This proposal provides a unified view of signal detection for random fields that applies broadly to a large class of problems ranging from proteomics to medical imaging to environmental monitoring. From a statistical point of view, it provides a new answer to the problem of controlling FDR in random fields. By taking advantage of the dependence structure, the methods developed in this proposal offer higher statistical power in the search for markers, so that a smaller number of false markers will be tested in follow-up studies.




Spatial confidence sets for raw effect size images.
Authors: Bowring A. , Telschow F. , Schwartzman A. , Nichols T.E. .
Source: Neuroimage, 2019 12; 203, p. 116187.
EPub date: 2019-09-15 00:00:00.0.
PMID: 31533067
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Peak p-values and false discovery rate inference in neuroimaging.
Authors: Schwartzman A. , Telschow F. .
Source: Neuroimage, 2019-08-15 00:00:00.0; 197, p. 402-413.
EPub date: 2019-04-24 00:00:00.0.
PMID: 31028923
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Expected Number and Height Distribution of Critical Points of Smooth Isotropic Gaussian Random Fields.
Authors: Cheng D. , Schwartzman A. .
Source: Bernoulli : Official Journal Of The Bernoulli Society For Mathematical Statistics And Probability, 2018 Nov; 24(4B), p. 3422-3446.
EPub date: 2018-04-18 00:00:00.0.
PMID: 31511762
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Confidence regions for spatial excursion sets from repeated random field observations, with an application to climate.
Authors: Sommerfeld M. , Sain S. , Schwartzman A. .
Source: Journal Of The American Statistical Association, 2018; 113(523), p. 1327-1340.
EPub date: 2018-06-12 00:00:00.0.
PMID: 31452557
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A Geospatial Epidemiologic Analysis of Nontuberculous Mycobacterial Infection: An Ecological Study in Colorado.
Authors: Lipner E.M. , Knox D. , French J. , Rudman J. , Strong M. , Crooks J.L. .
Source: Annals Of The American Thoracic Society, 2017-06-08 00:00:00.0; , .
EPub date: 2017-06-08 00:00:00.0.
PMID: 28594574
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MULTIPLE TESTING OF LOCAL MAXIMA FOR DETECTION OF PEAKS IN RANDOM FIELDS.
Authors: Cheng D. , Schwartzman A. .
Source: Annals Of Statistics, 2017 Apr; 45(2), p. 529-556.
EPub date: 2019-05-16 00:00:00.0.
PMID: 31527989
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Assessing NARCCAP climate model effects using spatial confidence regions.
Authors: French J.P. , McGinnis S. , Schwartzman A. .
Source: Advances In Statistical Climatology, Meteorology And Oceanography, 2017; 3(2), p. 67-92.
EPub date: 2017-07-14 00:00:00.0.
PMID: 28936474
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autoimage: Multiple Heat Maps for Projected Coordinates.
Authors: French J.P. .
Source: The R Journal, 2017; 9(1), p. 284-297.
EPub date: 2017-05-10 00:00:00.0.
PMID: 29147579
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Nonparametric Bootstrap of Sample Means of Positive-Definite Matrices with an Application to Diffusion-Tensor-Imaging Data Analysis.
Authors: Ellingson L. , Groisser D. , Osborne D. , Patrangenaru V. , Schwartzman A. .
Source: Communications In Statistics: Simulation And Computation, 2017; 46(6), p. 4851-4879.
EPub date: 2017-02-03 00:00:00.0.
PMID: 31452576
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FDR control of detected regions by multiscale matched filtering.
Authors: Kachouie N.N. , Lin X. , Schwartzman A. .
Source: Communications In Statistics: Simulation And Computation, 2017; 46(1), p. 127-144.
EPub date: 2014-12-23 00:00:00.0.
PMID: 31501637
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The Empirical Distribution of a Large Number of Correlated Normal Variables.
Authors: Azriel D. , Schwartzman A. .
Source: Journal Of The American Statistical Association, 2015-09-01 00:00:00.0; 110(511), p. 1217-1228.
EPub date: 2015-09-01 00:00:00.0.
PMID: 26858467
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Distribution of the Height of Local Maxima of Gaussian Random Fields.
Authors: Cheng D. , Schwartzman A. .
Source: Extremes, 2015-06-01 00:00:00.0; 18(2), p. 213-240.
EPub date: 2015-06-01 00:00:00.0.
PMID: 26478714
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False Discovery Control in Large-Scale Spatial Multiple Testing.
Authors: Sun W. , Reich B.J. , Cai T.T. , Guindani M. , Schwartzman A. .
Source: Journal Of The Royal Statistical Society. Series B, Statistical Methodology, 2015-01-01 00:00:00.0; 77(1), p. 59-83.
PMID: 25642138
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Detection of Local DNA Copy Number Changes in Lung Cancer Population Analyses Using A Multi-Scale Approach.
Authors: Kachouie N.N. , Lin X. , Christiani D.C. , Schwartzman A. .
Source: Communications In Statistics. Case Studies, Data Analysis And Applications, 2015; 1(4), p. 206-216.
EPub date: 2016-07-18 00:00:00.0.
PMID: 31489360
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Voxelwise multivariate analysis of multimodality magnetic resonance imaging.
Authors: Naylor M.G. , Cardenas V.A. , Tosun D. , Schuff N. , Weiner M. , Schwartzman A. .
Source: Human Brain Mapping, 2014 Mar; 35(3), p. 831-46.
PMID: 23408378
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MULTIPLE TESTING OF LOCAL MAXIMA FOR DETECTION OF PEAKS IN CHIP-SEQ DATA.
Authors: Schwartzman A. , Jaffe A. , Gavrilov Y. , Meyer C.A. .
Source: The Annals Of Applied Statistics, 2013; 7(1), p. 471-494.
PMID: 25411587
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Paradoxical Results Of Adaptive False Discovery Rate Procedures In Neuroimaging Studies
Authors: Reiss P.T. , Schwartzman A. , Lu F. , Huang L. , Proal E. .
Source: Neuroimage, 2012 Dec; 63(4), p. 1833-40.
PMID: 22842214
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Comment on "Estimating False Discovery Proportion Under Arbitrary Covariance Dependence" by Fan et al.
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Source: Journal Of The American Statistical Association, 2012; 107(499), p. 1039-1041.
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MULTIPLE TESTING OF LOCAL MAXIMA FOR DETECTION OF PEAKS IN 1D.
Authors: Schwartzman A. , Gavrilov Y. , Adler R.J. .
Source: Annals Of Statistics, 2011-12-01 00:00:00.0; 39(6), p. 3290-3319.
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