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dc.contributor.authorLim, Chee Yee
dc.date.accessioned2017-10-09T08:34:01Z
dc.date.available2017-10-09T08:34:01Z
dc.date.issued2017-10-05
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/267786
dc.description.abstractGene expression is tightly regulated by complex transcriptional regulatory mechanisms to achieve specific expression patterns, which are essential to facilitate important biological processes such as embryonic development. Dysregulation of gene expression can lead to diseases such as cancers. A better understanding of the transcriptional regulation will therefore not only advance the understanding of fundamental biological processes, but also provide mechanistic insights into diseases. The earlier versions of high-throughput expression profiling techniques were limited to measuring average gene expression across large pools of cells. In contrast, recent technological improvements have made it possible to perform expression profiling in single cells. Single-cell expression profiling is able to capture heterogeneity among single cells, which is not possible in conventional bulk expression profiling. In my PhD, I focus on developing new algorithms, as well as benchmarking and utilising existing algorithms to study the transcriptomes of various biological systems using single-cell expression data. I have developed two different single-cell specific network inference algorithms, BTR and SPVAR, which are based on two different formalisms, Boolean and autoregression frameworks respectively. BTR was shown to be useful for improving existing Boolean models with single-cell expression data, while SPVAR was shown to be a conservative predictor of gene interactions using pseudotime-ordered single-cell expression data. In addition, I have obtained novel biological insights by analysing single-cell RNAseq data from the epiblast stem cells reprogramming and the leukaemia systems. Three different driver genes, namely Esrrb, Klf2 and GY118F, were shown to drive reprogramming of epiblast stem cells via different reprogramming routes. As for the leukaemia system, FLT3-ITD and IDH1-R132H mutations were shown to interact with each other and potentially predispose some cells for developing acute myeloid leukaemia.
dc.description.sponsorshipWellcome Trust and Cambridge Trust
dc.language.isoen
dc.rightsAttribution-ShareAlike 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subjectBioinformatics
dc.subjectSingle-cell RNAseq
dc.subjectGene network inference
dc.subjectBoolean model
dc.subjectAutoregression model
dc.subjectEpiblast stem cell
dc.subjectLeukaemia
dc.subjectPseudotime inference
dc.titleUnderstanding transcriptional regulation through computational analysis of single-cell transcriptomics
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.departmentCambridge Institute of Medical Research
dc.date.updated2017-10-08T21:03:23Z
dc.identifier.doi10.17863/CAM.13717
dc.contributor.orcidLim, Chee Yee [0000-0003-3553-3950]
dc.publisher.collegeWolfson College
dc.type.qualificationtitlePhD in Medical Science
cam.supervisorGottgens, Berthold
cam.supervisorFisher, Jasmin
rioxxterms.freetoread.startdate2017-10-08


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Except where otherwise noted, this item's licence is described as Attribution-ShareAlike 4.0 International