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Identifier 000419430
Title Learning pathway dynamics from single-cell proteomic data
Alternative Title Μάθηση δυναμικών βιολογικών μονοπατιών από single cell δεδομένα
Author Βέρρου, Κλειώ-Μαρία
Thesis advisor Τσαμαρδίνος, Ι
Νικολάου, Χ.
Στρουμπούλης, Ι
Abstract Single-cell platforms provide data with statistically large samples of snapshot observations capable of resolving intercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. The trajectory inference methodology has not yet been used to address the challenging problem of learning the dynamics of signaling protein systems. In this work we assess this prospect. We test the performance of this novel class of methods on two of proteomic temporal datasets that were produced using different signaling perturbations on the same population of cells. To evaluate the learning quality we employ four different evaluation metrics that quantify the performance of each algorithm according to (a) the biological meaning of the output, (b) the robustness, (c) the correlation with the initial dataset and (d) the roughness of the phosphorylation levels though the biological time. We show that experimental time alone is insufficient to provide knowledge about the order of proteins during signal transduction. Accordingly, we show that the inferred trajectories provide richer information about the underlying dynamics. Our results also highlight that none of the already developed algorithms is universally applicable to this problem because the learning quality highly depends on the signaling perturbation.
Language English
Subject Mass cytometry
Proteomics
Ψευδοχρόνος
Issue date 2018-12-05
Collection   School/Department--School of Medicine--Department of Medicine--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/4/e/b/metadata-dlib-1543577806-995359-30058.tkl Bookmark and Share
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