{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/291806"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/291806","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Quantifying expression variability in single-cell RNA sequencing data","abstract":"Transcriptional noise is an intrinsic feature of cell populations and plays a driving role in mammalian development, tissue homoeostasis and immune function. While expression heterogeneity, a phenotypic readout of transcriptional noise, has been broadly studied in prokaryotic model systems or by profiling individual genes, few whole-transcriptome studies in mammalian systems have been reported. The development of single-cell RNA sequencing technologies introduced powerful tools to investigate transcriptional differences between individual cells, therefore allowing the in-depth characterisation of expression variability. In this thesis, I computationally analysed single-cell RNA sequencing data to understand transcriptional variability and expanded a statistical model to avoid confounding effects when quantifying such variability. First, I profiled individual transcriptomes of CD4$^+$ T cells, identifying a global decrease in transcriptional variability upon immune activation. By extending this analysis across two sub-species of mice, I identified an evolutionarily conserved set of immune response genes for which transcriptional variability increases during ageing. I used a Bayesian modelling framework to quantify mean expression and transcriptional variability but due to a strong confounding effect between these two parameters, variability analysis was restricted to genes that are similarly expressed across the tested conditions. To address this problem, I extended the computational framework allowing the parallel assessment of changes in mean expression and variability. Within this Bayesian framework, I introduced a joint prior linking mean expression and variability parameters, which allowed a residual over-dispersion to be measured for each gene. This measure allowed me to statistically assess changes in variability even for genes with differences in mean expression between conditions. Finally, I applied the model to identify temporal changes in variability over the time-course of spermatogenesis. This unidirectional differentiation process involves several complex steps before mature sperm form from spermatogonial stem cells. When profiling changes in variability across this developmental time-course, peaks in variability are caused by rapid changes in gene expression along the differentiation trajectory. This thesis provides a deeper understanding of technical and biological factors that drive transcriptional variability and offers a basis for future research to characterise its role in health and disease.","abstract_html":"Transcriptional noise is an intrinsic feature of cell populations and plays a driving role in mammalian development, tissue homoeostasis and immune function. While expression heterogeneity, a phenotypic readout of transcriptional noise, has been broadly studied in prokaryotic model systems or by profiling individual genes, few whole-transcriptome studies in mammalian systems have been reported. The development of single-cell RNA sequencing technologies introduced powerful tools to investigate transcriptional differences between individual cells, therefore allowing the in-depth characterisation of expression variability. In this thesis, I computationally analysed single-cell RNA sequencing data to understand transcriptional variability and expanded a statistical model to avoid confounding effects when quantifying such variability. First, I profiled individual transcriptomes of CD4<span class=\"etd-inline-math\"><sup>+</sup></span> T cells, identifying a global decrease in transcriptional variability upon immune activation. By extending this analysis across two sub-species of mice, I identified an evolutionarily conserved set of immune response genes for which transcriptional variability increases during ageing. I used a Bayesian modelling framework to quantify mean expression and transcriptional variability but due to a strong confounding effect between these two parameters, variability analysis was restricted to genes that are similarly expressed across the tested conditions. To address this problem, I extended the computational framework allowing the parallel assessment of changes in mean expression and variability. Within this Bayesian framework, I introduced a joint prior linking mean expression and variability parameters, which allowed a residual over-dispersion to be measured for each gene. This measure allowed me to statistically assess changes in variability even for genes with differences in mean expression between conditions. Finally, I applied the model to identify temporal changes in variability over the time-course of spermatogenesis. This unidirectional differentiation process involves several complex steps before mature sperm form from spermatogonial stem cells. When profiling changes in variability across this developmental time-course, peaks in variability are caused by rapid changes in gene expression along the differentiation trajectory. This thesis provides a deeper understanding of technical and biological factors that drive transcriptional variability and offers a basis for future research to characterise its role in health and disease.","abstract_has_math":true,"creators":["Eling, Nils"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Marioni, John"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-19","date_published":"2019-07-19","updated_at":"2026-07-24T01:33:13Z","subjects":["single-cell RNA sequencing","transcriptional noise","Bayesian statistics","spermatogenesis","immune system","ageing","T cells","regression"],"languages":["en"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/50839745-04f0-48a6-8572-62392c66242b/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.38966","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Marioni, John"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Funding was provided via the EMBL international PhD programme"]},{"key":"dc:creator","label":"Author","values":["Eling, Nils"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2019-07-19"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/291806"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["single-cell RNA sequencing","transcriptional noise","Bayesian statistics","spermatogenesis","immune system","ageing","T cells","regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/50839745-04f0-48a6-8572-62392c66242b/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.17863/CAM.38966"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/31db545a-85bc-4aa1-a9c4-1ca61a7bf222/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Transcriptional noise is an intrinsic feature of cell populations and plays a driving role in mammalian development, tissue homoeostasis and immune function. 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By extending this analysis across two sub-species of mice, I identified an evolutionarily conserved set of immune response genes for which transcriptional variability increases during ageing. I used a Bayesian modelling framework to quantify mean expression and transcriptional variability but due to a strong confounding effect between these two parameters, variability analysis was restricted to genes that are similarly expressed across the tested conditions. To address this problem, I extended the computational framework allowing the parallel assessment of changes in mean expression and variability. Within this Bayesian framework, I introduced a joint prior linking mean expression and variability parameters, which allowed a residual over-dispersion to be measured for each gene. This measure allowed me to statistically assess changes in variability even for genes with differences in mean expression between conditions. Finally, I applied the model to identify temporal changes in variability over the time-course of spermatogenesis. This unidirectional differentiation process involves several complex steps before mature sperm form from spermatogonial stem cells. When profiling changes in variability across this developmental time-course, peaks in variability are caused by rapid changes in gene expression along the differentiation trajectory. 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By extending this analysis across two sub-species of mice, I identified an evolutionarily conserved set of immune response genes for which transcriptional variability increases during ageing. I used a Bayesian modelling framework to quantify mean expression and transcriptional variability but due to a strong confounding effect between these two parameters, variability analysis was restricted to genes that are similarly expressed across the tested conditions. To address this problem, I extended the computational framework allowing the parallel assessment of changes in mean expression and variability. Within this Bayesian framework, I introduced a joint prior linking mean expression and variability parameters, which allowed a residual over-dispersion to be measured for each gene. This measure allowed me to statistically assess changes in variability even for genes with differences in mean expression between conditions. Finally, I applied the model to identify temporal changes in variability over the time-course of spermatogenesis. This unidirectional differentiation process involves several complex steps before mature sperm form from spermatogonial stem cells. When profiling changes in variability across this developmental time-course, peaks in variability are caused by rapid changes in gene expression along the differentiation trajectory. 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