Direction-Projection-Permutation for High-Dimensional Hypothesis Tests

Wei, Susan; Lee, Chihoon; Wichers, Lindsay; Marron, J. S.

doi:10.1080/10618600.2015.1027773

Cited by 69 publications

(86 citation statements)

References 25 publications

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“…Here, we review and compare the performance of several state-of-the-art statistical tests originally proposed for genetics, ecology and high-dimensional data, in addition to NBS that is familiar to neuroimaging researchers. Specifically, we examine Multivariate Matrix Distance Regression (MDMR) (McArdle and Anderson 2001; Zapala and Schork 2006, 2012), an adaptive sum of powered score (aSPU) test and its weighted version (aSPUw) (Pan et al 2014), and Direction-Projection-Permutation (Wei et al 2014). All of them are omnibus tests assessing global significance of differences across multiple or all edges of the networks to be compared.…”

Section: Introductionmentioning

confidence: 99%

Comparison of statistical tests for group differences in brain functional networks

et al. 2014

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Brain functional connectivity has been studied by analyzing time series correlations in regional brain activities based on resting-state fMRI data. Brain functional connectivity can be depicted as a network or graph defined as a set of nodes linked by edges. Nodes represent brain regions and an edge measures the strength of functional correlation between two regions. Most of existing work focuses on estimation of such a network. A key but inadequately addressed question is how to test for possible differences of the networks between two subject groups, say between healthy controls and patients. Here we illustrate and compare the performance of several state-of-the-art statistical tests drawn from the neuroimaging, genetics, ecology and high-dimensional data literatures. Both real and simulated data were used to evaluate the methods. We found that, Network Based Statistic (NBS) performed well in many but not all situations, and its performance critically depends on the choice of its threshold parameter, which is unknown and difficult to choose in practice. Importantly, two adaptive statistical tests called adaptive sum of powered score (aSPU) and its weighted version (aSPUw) are easy to use and complementary to NBS, being higher powered than NBS in some situations. The aSPU and aSPUw tests can be also applied to adjust for co-variates. Between the aSPU and aSPUw tests, they often, but not always, performed similarly with neither one as a uniform winner. On the other hand, Multivariate Matrix Distance Regression (MDMR) has been applied to detect group differences for brain connectivity; with the usual choice of the Euclidean distance, MDMR is a special case of the aSPU test. Consequently NBS, aSPU and aSPUw tests are recommended to test for group differences in functional connectivity.

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Section: Introductionmentioning

confidence: 99%

Comparison of statistical tests for group differences in brain functional networks

et al. 2014

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“…Because they are very good at separating groups of data, they can also find spurious separations, that are just sampling artifacts, and do not represent important population structure. This point is nicely illustrated in Wei et al (2013) who show that even for two groups selected randomly from standard normal data, when the dimension is very high, DWD finds a (spurious) direction where the groups appear quite separated. That paper proposes the DiProPerm method to address this problem.…”

Section: Data Objects In Image Analysismentioning

confidence: 89%

Overview of object oriented data analysis

Marron

Alonso²

2014

Biometrical J

154

100

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Object oriented data analysis is the statistical analysis of populations of complex objects. In the special case of functional data analysis, these data objects are curves, where a variety of Euclidean approaches, such as principal components analysis, have been very successful. Challenges in modern medical image analysis motivate the statistical analysis of populations of more complex data objects that are elements of mildly non-Euclidean spaces, such as lie groups and symmetric spaces, or of strongly non-Euclidean spaces, such as spaces of tree-structured data objects. These new contexts for object oriented data analysis create several potentially large new interfaces between mathematics and statistics. The notion of object oriented data analysis also impacts data analysis, through providing a framework for discussion of the many choices needed in many modern complex data analyses, especially in interdisciplinary contexts.

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“…For that we used Principal component analysis (PCA) and Distance Weighted Discrimination (DWD)[17], and validate the results with direction-projection-permutation (DiProPerm) [18] hypothesis tests.…”

Section: Methodsmentioning

confidence: 99%

“…DiProPerm[18] is a permutation-based hypothesis test that assesses the chance that the observed degree of separation happened as a result of expected random variation. It was developed with DWD in mind as an area of application, but it represents a general framework of nonparametric hypothesis testing built to discern typical and atypical behavior in high-dimensional settings.…”

Section: Methodsmentioning

confidence: 99%

Detection of bone loss via subchondral bone analysis

Vimort

Ruellas

Prothero

et al. 2018

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging

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To date, there is no single sign, symptom, or test that can clearly diagnose early stages of Temporomandibular Joint Osteoarthritis (TMJ OA). However, it has been observed that changes in the bone occur in early stages of this disease, involving structural changes both in the texture and morphometry of the bone marrow and the subchondral cortical plate. In this paper we present a tool to detect and highlight subtle variations in subchondral bone structure obtained from high resolution Cone Beam Computed Tomography (hr-CBCT) in order to help with detecting early TMJ OA. The proposed tool was developed in ITK and 3DSlicer and it has been disseminated as open-source software tools. We have validated both our texture analysis and morphometry analysis biomarkers for detection of TMJ OA comparing hr-CBCT to μCT. Our initial statistical results using the multidimensional features computed with our tool indicate that it is possible to classify areas of demonstrated loss of trabecular bone in both μCT and hr-CBCT. This paper describes the first steps to alleviate the current inability of radiological changes to diagnose TMJ OA before morphological changes are too advanced by quantifying subchondral bone biomarkers. This paper indicates that texture based and morphometry based biomarkers have the potential to identify OA patients at risk for further bone destruction.

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Direction-Projection-Permutation for High-Dimensional Hypothesis Tests

Cited by 69 publications

References 25 publications

Comparison of statistical tests for group differences in brain functional networks

Comparison of statistical tests for group differences in brain functional networks

Overview of object oriented data analysis

Detection of bone loss via subchondral bone analysis

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