Browsing by Author "Nagorski, John"
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Item Computation, Visualization, and Applications of Convex Clustering(2018-10-04) Nagorski, John; Allen, Genevera IClustering is a ubiquitous tool for exploratory data analysis across the sciences, with the general aim of identifying groups of similar objects. Recent work has recast the clustering problem within the framework of convex optimization, addressing many shortcomings of traditional methods such as interpretability, stability, and parameter selection. The method of Convex Clustering has proven to be a canonical example of such an approach, and its extensions and applications will be the focus of this work. We begin by considering the application of Convex Clustering in the novel setting of region detection for high-throughput genomic data. We illustrate the versatility of Convex Clustering by developing a novel extension, Spatial Convex Clustering (SpaCC), specifically catered to multivariate spatially correlated genomics data. We demonstrate SpaCC to achieve state-of-the-art performance on the well-studied prob- lem of Copy Number Segmentation, and show it to be similarly successful in the novel setting of DNA Methylation region detection. Next, we address several shortcomings of Convex Clustering including slow computation and lack of familiar visualizations relative to its traditional counterparts. To do so, we introduce algorithms for the fast approximation of the Convex Clustering solution path and provide both theoretical guarantees of error control as well as empirical investigations. Next, we provide a suite of visualization techniques to aid in the interpretation of the clustering solutioniii path, exploring their insights via several real data examples. Finally we introduce the R-package, clustRviz, which gives practitioners direct access to the fast computation and dynamic visualizations introduced throughout.Item Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease(Elsevier, 2016) Allen, Genevera I.; Amoroso, Nicola; Anghel, Catalina; Balagurusamy, Venkat; Bare, Christopher J.; Beaton, Derek; Bellotti, Roberto; Bennett, David A.; Boehme, Kevin L.; Boutros, Paul C.; Caberlotto, Laura; Caloian, Cristian; Campbell, Frederick; Neto, Elias Chaibub; Chang, Yu-Chuan; Chen, Beibei; Chen, Chien-Yu; Chien, Ting-Ying; Clark, Tim; Das, Sudeshna; Davatzikos, Christos; Deng, Jieyao; Dillenberger, Donna; Dobson, Richard J.B.; Dong, Qilin; Doshi, Jimit; Duma, Denise; Errico, Rosangela; Erus, Guray; Everett, Evan; Fardo, David W.; Friend, Stephen H.; Frӧhlich, Holger; Gan, Jessica; St George-Hyslop, Peter; Ghosh, Satrajit S.; Glaab, Enrico; Green, Robert C.; Guan, Yuanfang; Hong, Ming-Yi; Huang, Chao; Hwang, Jinseub; Ibrahim, Joseph; Inglese, Paolo; Iyappan, Anandhi; Jiang, Qijia; Katsumata, Yuriko; Kauwe, John S.K.; Klein, Arno; Kong, Dehan; Krause, Roland; Lalonde, Emilie; Lauria, Mario; Lee, Eunjee; Lin, Xihui; Liu, Zhandong; Livingstone, Julie; Logsdon, Benjamin A.; Lovestone, Simon; Ma, Tsung-wei; Malhotra, Ashutosh; Mangravite, Lara M.; Maxwell, Taylor J.; Merrill, Emily; Nagorski, John; Namasivayam, Aishwarya; Narayan, Manjari; Naz, Mufassra; Newhouse, Stephen J.; Norman, Thea C.; Nurtdinov, Ramil N.; Oyang, Yen-Jen; Pawitan, Yudi; Peng, Shengwen; Peters, Mette A.; Piccolo, Stephen R.; Praveen, Paurush; Priami, Corrado; Sabelnykova, Veronica Y.; Senger, Philipp; Shen, Xia; Simmons, Andrew; Sotiras, Aristeidis; Stolovitzky, Gustavo; Tangaro, Sabina; Tateo, Andrea; Tung, Yi-An; Tustison, Nicholas J.; Varol, Erdem; Vradenburg, George; Weiner, Michael W.; Xiao, Guanghua; Xie, Lei; Xie, Yang; Xu, Jia; Yang, Hojin; Zhan, Xiaowei; Zhou, Yunyun; Zhu, Fan; Zhu, Hongtu; Zhu, Shanfeng; Alzheimer’s Disease Neuroimaging InitiativeIdentifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.Item Genomic region detection via Spatial Convex Clustering(Public Library of Science, 2018) Nagorski, John; Allen, Genevera I.Several modern genomic technologies, such as DNA-Methylation arrays, measure spatially registered probes that number in the hundreds of thousands across multiple chromosomes. The measured probes are by themselves less interesting scientifically; instead scientists seek to discover biologically interpretable genomic regions comprised of contiguous groups of probes which may act as biomarkers of disease or serve as a dimension-reducing pre-processing step for downstream analyses. In this paper, we introduce an unsupervised feature learning technique which maps technological units (probes) to biological units (genomic regions) that are common across all subjects. We use ideas from fusion penalties and convex clustering to introduce a method for Spatial Convex Clustering, or SpaCC. Our method is specifically tailored to detecting multi-subject regions of methylation, but we also test our approach on the well-studied problem of detecting segments of copy number variation. We formulate our method as a convex optimization problem, develop a massively parallelizable algorithm to find its solution, and introduce automated approaches for handling missing values and determining tuning parameters. Through simulation studies based on real methylation and copy number variation data, we show that SpaCC exhibits significant performance gains relative to existing methods. Finally, we illustrate SpaCC’s advantages as a pre-processing technique that reduces large-scale genomics data into a smaller number of genomic regions through several cancer epigenetics case studies on subtype discovery, network estimation, and epigenetic-wide association.Item Quantifying cognitive resilience in Alzheimer’s Disease: The Alzheimer’s Disease Cognitive Resilience Score(Public Library of Science, 2020) Yao, Tianyi; Sweeney, Elizabeth; Nagorski, John; Shulman, Joshua M.; Allen, Genevera I.Even though there is a clear link between Alzheimer’s Disease (AD) related neuropathology and cognitive decline, numerous studies have observed that healthy cognition can exist in the presence of extensive AD pathology, a phenomenon sometimes called Cognitive Resilience (CR). To better understand and study CR, we develop the Alzheimer’s Disease Cognitive Resilience Score (AD-CR Score), which we define as the difference between the observed and expected cognition given the observed level of AD pathology. Unlike other definitions of CR, our AD-CR Score is a fully non-parametric, stand-alone, individual-level quantification of CR that is derived independently of other factors or proxy variables. Using data from two ongoing, longitudinal cohort studies of aging, the Religious Orders Study (ROS) and the Rush Memory and Aging Project (MAP), we validate our AD-CR Score by showing strong associations with known factors related to CR such as baseline and longitudinal cognition, non AD-related pathology, education, personality, APOE, parkinsonism, depression, and life activities. Even though the proposed AD-CR Score cannot be directly calculated during an individual’s lifetime because it uses postmortem pathology, we also develop a machine learning framework that achieves promising results in terms of predicting whether an individual will have an extremely high or low AD-CR Score using only measures available during the lifetime. Given this, our AD-CR Score can be used for further investigations into mechanisms of CR, and potentially for subject stratification prior to clinical trials of personalized therapies.