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dc.contributor.advisorCaroline Uhler.en_US
dc.contributor.authorKatcoff, Abigail.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-11-22T00:03:17Z
dc.date.available2019-11-22T00:03:17Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123030
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 51-53).en_US
dc.description.abstractPluripotent 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.en_US
dc.description.statementofresponsibilityby Abigail Katcoff.en_US
dc.format.extent53 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAligning heterogenous single cell assay datasetsen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1127649665en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-11-22T00:03:16Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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