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Statistical Methods for Cell Heterog...

Liu, Yiyi.

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Statistical Methods for Cell Heterogeneity and Cell Drug-Response Study.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methods for Cell Heterogeneity and Cell Drug-Response Study./
Author:	Liu, Yiyi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	182 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
Contained By:	Dissertations Abstracts International80-02B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10927832
ISBN:	9780438194021

Statistical Methods for Cell Heterogeneity and Cell Drug-Response Study.
Liu, Yiyi.

Statistical Methods for Cell Heterogeneity and Cell Drug-Response Study. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 182 p.

Source: Dissertations Abstracts International, Volume: 80-02, Section: B.

Thesis (Ph.D.)--Yale University, 2018.

This item must not be added to any third party search indexes.

The rapid development of biotechnologies has enabled us to study cells and their functionalities with unprecedented details. While the large amounts of experimental data have provided rich information to exploit, the challenges in extracting useful insights from them and transforming the insights into biological knowledge should not be overlooked. My Ph.D. research aims at developing statistical methods and tools to overcome some of the unique challenges posed by such data, better analyze and interpret them, and ultimately assist in revealing the mechanisms underlying complex molecular and cellular processes. The first part of my research focuses on delineating cell heterogeneities from high-throughput gene expression data. Specifically, we have proposed a method for cell subtype identification using single-cell RNA-sequencing (scRNA-seq) data. By modeling dropout events explicitly and dichotomizing gene expression status, our new method better identifies cell subtypes and estimates expression patterns underlying the noisy scRNA-seq data, on both simulated and real experimental datasets. In addition, we have developed a variable importance-weighted Random Forests for more general classification and regression tasks, which incorporates the variable importance scores into the random feature selection step to better utilize informative features. We have applied it to solve biological problems such as cancer subtype classification and demonstrated its effectiveness. The second part of my dissertation studies interactions between cells and drugs, and how the heterogeneities of cells in transcriptome could be used to predict their differences in drug-response. Specifically, we have developed a new modeling framework for chemosensitivity data. Unlike conventional sigmoid dose-response curve fitting approaches, our method tackles the problem from a mechanistic perspective of cell growth, and can estimate cell birth and death rates under drug intervention separately. Motivated by the great potentials of combinatorial therapies in cancer treatment, we have also explored statistical methods for cell line-specific compound combination effects prediction. We have found both compounds' dissimilarity in chemical structure and similarity in induced gene expression changes to be predictive features of compound synergistic effects, and more importantly, these two types of features provide complementary information that when they are utilized together the predictive power further improves.

ISBN: 9780438194021Subjects--Topical Terms:

1002712
Biostatistics.

Statistical Methods for Cell Heterogeneity and Cell Drug-Response Study.
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500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Zhao, Hongyu.
502 $a Thesis (Ph.D.)--Yale University, 2018.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a The rapid development of biotechnologies has enabled us to study cells and their functionalities with unprecedented details. While the large amounts of experimental data have provided rich information to exploit, the challenges in extracting useful insights from them and transforming the insights into biological knowledge should not be overlooked. My Ph.D. research aims at developing statistical methods and tools to overcome some of the unique challenges posed by such data, better analyze and interpret them, and ultimately assist in revealing the mechanisms underlying complex molecular and cellular processes. The first part of my research focuses on delineating cell heterogeneities from high-throughput gene expression data. Specifically, we have proposed a method for cell subtype identification using single-cell RNA-sequencing (scRNA-seq) data. By modeling dropout events explicitly and dichotomizing gene expression status, our new method better identifies cell subtypes and estimates expression patterns underlying the noisy scRNA-seq data, on both simulated and real experimental datasets. In addition, we have developed a variable importance-weighted Random Forests for more general classification and regression tasks, which incorporates the variable importance scores into the random feature selection step to better utilize informative features. We have applied it to solve biological problems such as cancer subtype classification and demonstrated its effectiveness. The second part of my dissertation studies interactions between cells and drugs, and how the heterogeneities of cells in transcriptome could be used to predict their differences in drug-response. Specifically, we have developed a new modeling framework for chemosensitivity data. Unlike conventional sigmoid dose-response curve fitting approaches, our method tackles the problem from a mechanistic perspective of cell growth, and can estimate cell birth and death rates under drug intervention separately. Motivated by the great potentials of combinatorial therapies in cancer treatment, we have also explored statistical methods for cell line-specific compound combination effects prediction. We have found both compounds' dissimilarity in chemical structure and similarity in induced gene expression changes to be predictive features of compound synergistic effects, and more importantly, these two types of features provide complementary information that when they are utilized together the predictive power further improves.
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