Scaled matrix completion and cell deconvolution with NanoString data

dc.contributor.advisor	Simon, Noah
dc.contributor.author	Zhang, Yichen
dc.date.accessioned	2018-11-28T03:15:47Z
dc.date.available	2018-11-28T03:15:47Z
dc.date.issued	2018-11-28
dc.date.submitted	2018
dc.description	Thesis (Master's)--University of Washington, 2018
dc.description.abstract	This thesis explores two research problems in Chapters 1 and 2. Chapter 1 combines pivotal penalized estimation, with matrix completion, to introduce a new matrix completion problem, where the optimal tuning parameter does not depend on the variance of the noise. We consider this new "scaled matrix completion" problem, and compare it to standard matrix completion problem. Chapter 2 develops a new cell deconvolution method for data from NanoString Technologies nCounter platform, a relatively new sequencing platform. We assess the performance of this cell deconvolution method with simulated data and experimental data.
dc.embargo.terms	Open Access
dc.format.mimetype	application/pdf
dc.identifier.other	Zhang_washington_0250O_19116.pdf
dc.identifier.uri	http://hdl.handle.net/1773/42982
dc.language.iso	en_US
dc.rights	none
dc.subject
dc.subject	Biostatistics
dc.subject.other	Biostatistics
dc.title	Scaled matrix completion and cell deconvolution with NanoString data
dc.type	Thesis

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