2026-09-23T04:48:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1227202026-06-17T14:43:12Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Aviv Regev and Eric S. Lander.
Cleary, Brian(Brian Lowman)
Massachusetts Institute of Technology. Computational and Systems Biology Program.
Massachusetts Institute of Technology. Computational and Systems Biology Program
2019-11-04T20:20:59Z
2019-11-04T20:20:59Z
2019
https://hdl.handle.net/1721.1/122720
1124074039
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019
Cataloged from PDF version of thesis. "June 2019."
Includes bibliographical references.
The development of high-throughput techniques to observe and perturb biological systems has led to remarkable progress in the last several decades. From the tremendous amounts of data being accumulated, new opportunities have emerged, including the possibility of finding latent patterns in high-dimensional variables that are reflective of underlying biological processes. While these methods have led to countless discoveries and innovations, it is clear there is much more we could learn by measuring and perturbing at far greater scales. Here, I advance methods to understand and utilize latent patterns in new types of high-dimensional data. I devise a method of analyzing networks of 'frequency interactions' in 16S/18S time series data, showing that these can be used to identify microbial communities and associated environmental factors.
Then, as part of a highly collaborative project, I show how latent patterns in single cell RNA-Seq can be used together with optimal transport analysis to identify cell types and cell type trajectories, regulatory pathways, and cell-cell interactions in a time-course of developmental reprogramming. I then step back to ask a fundamental question: how do we choose which observations and perturbations to make, and how many of each are necessary? I approach this question on the basis of the inherency of latent structure in biology, and on foundational mathematical results concerning the analysis of highly-structured data. I present the beginnings of a framework to formalize how random composite experiments can make biological discovery more efficient by leveraging latent patterns. I first show how to recover individual genomes using covariance patterns in a series of composite (meta-) genomic data.
I then describe how random composite measurements and compressed sensing can be used to make gene expression profiling more efficient. Finally, I apply this idea to in situ imaging transcriptomics, demonstrating how many individual gene images can be efficiently recovered from a small number of composite gene images.
by Brian Cleary.
Ph.D.
Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program
349 pages
eng
Massachusetts Institute of Technology
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
Computational and Systems Biology Program.
Leveraging latent patterns in the study of living systems
Thesis
application/pdf
2019-11-04T20:20:58Z
Publication
Doctoral
CSB
unknown
unknown
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<Abstract>The development of high-throughput techniques to observe and perturb biological systems has led to remarkable progress in the last several decades. From the tremendous amounts of data being accumulated, new opportunities have emerged, including the possibility of finding latent patterns in high-dimensional variables that are reflective of underlying biological processes. While these methods have led to countless discoveries and innovations, it is clear there is much more we could learn by measuring and perturbing at far greater scales. Here, I advance methods to understand and utilize latent patterns in new types of high-dimensional data. I devise a method of analyzing networks of 'frequency interactions' in 16S/18S time series data, showing that these can be used to identify microbial communities and associated environmental factors.</Abstract>
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