Full Citation
Title: Modeling temporally-regulated effects on distributions
Citation Type: Dissertation/Thesis
Publication Year: 2015
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Abstract: We present a nonparametric framework for modeling an evolving sequence of (es- timated) probability distributions which distinguishes the effects of sequential pro- gression on the observed distribution from extraneous sources of noise (i.e. latent variables which perturb the distributions independently of the sequence-index). To discriminate between these two types of variation, our methods leverage the under- lying assumption that the effects of sequential-progression follow a consistent trend. Our methods are motivated by the recent rise of single-cell RNA-sequencing time course experiments, in which an important analytic goal is the identification of genes relevant to the progression of a biological process of interest at cellular resolution. As existing statistical tools are not suited for this task, we introduce a new regression model for (ordinal-value , univariate-distribution) covariate-response pairs where the class of regression-functions reflects coherent changes to the distributions over increas- ing levels of the covariate, a concept we refer to as trends in distributions. Through simulation study and extensive application of our ideas to data from recent single- cell gene-expression time course experiments, we demonstrate numerous strengths of our framework. Finally, we characterize both theoretical properties of the proposed estimators and the generality of our trend-assumption across diverse types of un- derlying sequential-progression effects, thus highlighting the utility of our framework for a wide variety of other applications involving the analysis of distributions with associated ordinal labels.
Url: https://dspace.mit.edu/bitstream/handle/1721.1/99857/927701697-MIT.pdf?sequence=1&isAllowed=y
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Authors: Mueller, Jonas, W
Institution: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
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Degree: Master of Science in Computer Science and Engineering
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Pages: 97
Data Collections: IPUMS USA
Topics: Population Data Science
Countries: United States