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Mathematical and statistical analysis of high-throughput biological data

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Title: Mathematical and statistical analysis of high-throughput biological data
Authors: Tang, Wenhao
Item Type: Thesis or dissertation
Abstract: With the fast development of biological techniques, high-throughput omics data is available, which also raises many big problems in other subjects like mathematics: how to deal with such big data so that meaningful biological insights can be found. Among the various omics data, I mainly work on Matrix-Assisted Laser Desorption Ionization Mass Spectrometry and Single Cell RNA Sequencing data. Although there already exist many mature analysis pipelines and machine learning algorithms for analysing these kinds of omics data, there remains improvement space. In this thesis, I firstly describe the relevant challenges of omics data analysis in detail. Then I explain popular analysis pipelines and algorithms which are frequently utilized during the analysis. Finally, I illustrate the modified analysis pipeline, math models and corresponding results.
Content Version: Open Access
Issue Date: Sep-2020
Date Awarded: Dec-2020
URI: http://hdl.handle.net/10044/1/86025
DOI: https://doi.org/10.25560/86025
Copyright Statement: Creative Commons Attribution NonCommercial Licence
Supervisor: Shahrezaei, Vahid
Larrouy-Maumus, Gerald
Sponsor/Funder: Imperial College London
Medical Research Council (Great Britain)
Wellcome Trust (London, England)
Funder's Grant Number: MRC-Confidence in Concept grant number 105603/Z/14/Z
UK Medical Research Council, a Leverhulme Research Project Grant [RPG-2014-408]
UK Medical Research Council [grant number MR/L01632X/1]
MRC Confidence in Concept Fund and ISSF Wellcome Trust grant 105603/Z/14/Z (to G.L.-M)
Department: Department of Mathematics
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Mathematics PhD theses

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