Dataset Information

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids.

ABSTRACT: In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects.

SUBMITTER: Cury C

PROVIDER: S-EPMC6241313 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids.

Cury Claire C Glaunès Joan A JA Toro Roberto R Chupin Marie M Schumann Gunter G Frouin Vincent V Poline Jean-Baptiste JB Colliot Olivier O

Frontiers in neuroscience 20181112

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested ...[more]

PMID: 30483045

Similar Datasets

Project description:ObjectiveThe aim of this study was to identify the care pathway and organisational factors that predict patient experience.DesignStatistical analysis of large National Health Service (NHS) datasets.Setting andparticipantsEngland; acute NHS organisational-level data.Primary and secondary outcome measuresThe relationship of care pathway and organisational variables to organisation-level patient experience.ResultsA framework of 18 care pathway and organisational variables were created based on the existing literature. 11 of these correlated to patient experience in univariate analyses. Multicollinearity tests resulted in 1 of the 11 variables holding a correlation to another variable larger than r=0.70. A significant multilinear regression equation, including the final 10 variables, was found (F(10,108)=6.214, p<0.00), with an [Formula: see text] of 0.365. Two variables were significant in predicting better in patient experience: Amount of support to clinical staff (beta=0.2, p=0.02) and the proportion of staff who would recommend the trust as a place to work or receive treatment (beta=0.26, p=0.01). Two variables were significant in predicting a negative impact on the patient's rating of their experience: Number of patients spending over 4 hours from decision to admit to admission (beta=-1.99, p=0.03) and the percentage of estates and hotel services contracted out (beta=-0.23, p=0.01).ConclusionsThese results indicate that augmenting clinical support and investing in the mechanisms that facilitate positive staff experience is essential to delivering appropriate, informative and patient-centric care. Reducing wait times and the extent of external contracting within hospitals is also likely to improve patient ratings of experience. Understanding the relationship between patient experience and objective, measurable organisational features promote a more patient-centric interpretation of quality and compel a better use of patient experience feedback to drive improvement.

Project description:BackgroundThe ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.ResultsA new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA.ConclusionsThe EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca.

Project description:BackgroundDrug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools.ResultsWe describe a statistical and visualization framework for the analysis of large-scale drug combination screens. We developed an approach suitable for datasets with large number of drugs pairs even if small number of data points are available per drug pair. We demonstrate our approach using a systematic screen of all possible pairs among 108 cancer drugs applied to melanoma cell lines. In this dataset only two dose-response data points per drug pair and two data points per single drug test were available. We used a Bliss-based linear model, effectively borrowing data from the drug pairs to obtain robust estimations of the singlet viabilities, consequently yielding better estimates of drug synergy. Our method improves data consistency across dosing thus likely reducing the number of false positives. The approach allows to compute p values accounting for standard errors of the modeled singlets and combination viabilities. We further develop a synergy specificity score that distinguishes specific synergies from those arising with promiscuous drugs. Finally, we developed a summarized interactive visualization in a web application, providing efficient access to any of the 439,000 data points in the combination matrix ( http://www.cmtlab.org:3000/combo_app.html ). The code of the analysis and the web application is available at https://github.com/arnaudmgh/synergy-screen .ConclusionsWe show that statistical modeling of single drug response from drug combination data can help determine significance of synergy and antagonism in drug combination screens with few data point per drug pair. We provide a web application for the rapid exploration of large combinatorial drug screen. All codes are available to the community, as a resource for further analysis of published data and for analysis of other drug screens.

Dataset Information

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids.

Publications

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets