Aligning heterogenous single cell assay datasets

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.

Bibliographic Details
Main Author: Katcoff, Abigail.
Other Authors: Caroline Uhler.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2019
Subjects:
Online Access:https://hdl.handle.net/1721.1/123030
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author Katcoff, Abigail.
author2 Caroline Uhler.
author_facet Caroline Uhler.
Katcoff, Abigail.
author_sort Katcoff, Abigail.
collection MIT
description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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spelling mit-1721.1/1230302019-11-22T03:24:57Z Aligning heterogenous single cell assay datasets Katcoff, Abigail. Caroline Uhler. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 51-53). Pluripotent stem cells offer strong promise for regenerative medicine but the pluripotent cell state is poorly understood. The goal of this thesis is the development of methods to analyze how the multiple facets of cell state-including gene expression, chromosome contacts, and chromatin accessibility-relate in the context of stem cells. The variability of each of these characteristics cannot be deduced from population studies, and while recent advances in single-cell transcriptomics have led to the development of a number of different single-cell assays, datasets that collect multiple types of assays on the same cells are rare. In this thesis, we explore the ability of three methods to integrate datasets from different single-cell assays based on an existing paired single-cell dataset of ATAC-seq and RNA-seq for human A549 cells. We then apply these methods to map the variability between three single-cell datasets-ATAC-seq, RNA-seq, and Hi-C-on pluripotent mouse embryonic stem cells and assess the performance of these methods. by Abigail Katcoff. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-22T00:03:17Z 2019-11-22T00:03:17Z 2019 2019 Thesis https://hdl.handle.net/1721.1/123030 1127649665 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 53 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Katcoff, Abigail.
Aligning heterogenous single cell assay datasets
title Aligning heterogenous single cell assay datasets
title_full Aligning heterogenous single cell assay datasets
title_fullStr Aligning heterogenous single cell assay datasets
title_full_unstemmed Aligning heterogenous single cell assay datasets
title_short Aligning heterogenous single cell assay datasets
title_sort aligning heterogenous single cell assay datasets
topic Electrical Engineering and Computer Science.
url https://hdl.handle.net/1721.1/123030
work_keys_str_mv AT katcoffabigail aligningheterogenoussinglecellassaydatasets