{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/8898"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/8898","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Quantile Regression Approach For Analyzing Gene Expression Data","abstract":"Temporal gene expression data contains ample information to characterize gene function and is now widely used in bio-medical research. A dense temporal gene ex- pression usually shows various patterns in expression levels under dfferent biological conditions. Existing literature models the gene trajectory using the mean function. Also, temporal gene expression curves generally show a strong degree of heterogeneity between multiple conditions. As a result, rate of change of gene expression may be dfferent in non-central locations and a mean model can not capture the non-central location of the gene expression distribution. However, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical to analyze gene data. In this research, we use linear quantile mixed model to analyze gene expression data. One of the notable advantages for quantile re- gression is that models error term do not assume any specific parametric distribution. This method enables the study of gene expression change over time by estimating a family of quantile functions. A statistical test is proposed to test the equality of two dfferent gene expres- sions based on estimated quantile regression parameters. Then, we investigate the performance of the proposed test statistic through extensive simulation studies. Sim- ulation studies demonstrate the good statistical performance of this proposed test statistic and conclude that this method is robust to violations of error assumptions. As an illustration, this proposed method is used to analyze a data set of 18 genes in P. aeruginosa, expressed in 24 biological conditions. Furthermore, minimum Maha- lanobis distance is used to find the clustering tree for gene expressions. ii","abstract_html":"Temporal gene expression data contains ample information to characterize gene function and is now widely used in bio-medical research. A dense temporal gene ex- pression usually shows various patterns in expression levels under dfferent biological conditions. Existing literature models the gene trajectory using the mean function. Also, temporal gene expression curves generally show a strong degree of heterogeneity between multiple conditions. As a result, rate of change of gene expression may be dfferent in non-central locations and a mean model can not capture the non-central location of the gene expression distribution. However, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical to analyze gene data. In this research, we use linear quantile mixed model to analyze gene expression data. One of the notable advantages for quantile re- gression is that models error term do not assume any specific parametric distribution. This method enables the study of gene expression change over time by estimating a family of quantile functions. A statistical test is proposed to test the equality of two dfferent gene expres- sions based on estimated quantile regression parameters. Then, we investigate the performance of the proposed test statistic through extensive simulation studies. Sim- ulation studies demonstrate the good statistical performance of this proposed test statistic and conclude that this method is robust to violations of error assumptions. As an illustration, this proposed method is used to analyze a data set of 18 genes in P. aeruginosa, expressed in 24 biological conditions. Furthermore, minimum Maha- lanobis distance is used to find the clustering tree for gene expressions. ii","abstract_has_math":false,"creators":["Chowdhury, Mashfiqul Huq"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Science (MSc)","degree_level":"Master's","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":[],"advisors":["Deng, DianLiang"],"committee_chairs":[],"committee_members":["Volodin, Andrei"],"year":2018,"date_issued":"2018-11","date_published":"2018-11","updated_at":"2026-07-24T04:03:34Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4240"],"render_values":[{"text":"https://doi.org/10.82465/4240","href":"https://doi.org/10.82465/4240","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/8898","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Deng, DianLiang"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Volodin, Andrei"]},{"key":"dc:creator","label":"Author","values":["Chowdhury, Mashfiqul Huq"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-06-21T20:13:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-21T20:13:01Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-11"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4240"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/8898"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. xviii, 122 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Temporal gene expression data contains ample information to characterize gene function and is now widely used in bio-medical research. A dense temporal gene ex- pression usually shows various patterns in expression levels under dfferent biological conditions. Existing literature models the gene trajectory using the mean function. Also, temporal gene expression curves generally show a strong degree of heterogeneity between multiple conditions. As a result, rate of change of gene expression may be dfferent in non-central locations and a mean model can not capture the non-central location of the gene expression distribution. However, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical to analyze gene data. In this research, we use linear quantile mixed model to analyze gene expression data. One of the notable advantages for quantile re- gression is that models error term do not assume any specific parametric distribution. This method enables the study of gene expression change over time by estimating a family of quantile functions. A statistical test is proposed to test the equality of two dfferent gene expres- sions based on estimated quantile regression parameters. Then, we investigate the performance of the proposed test statistic through extensive simulation studies. Sim- ulation studies demonstrate the good statistical performance of this proposed test statistic and conclude that this method is robust to violations of error assumptions. As an illustration, this proposed method is used to analyze a data set of 18 genes in P. aeruginosa, expressed in 24 biological conditions. Furthermore, minimum Maha- lanobis distance is used to find the clustering tree for gene expressions. ii"]},{"key":"dc:title","label":"Title","values":["Quantile Regression Approach For Analyzing Gene Expression Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Deng, DianLiang"],"dc:contributor.committeemember":["Volodin, Andrei"],"dc:creator":["Chowdhury, Mashfiqul Huq"],"dc:date.accessioned":["2019-06-21T20:13:01Z"],"dc:date.available":["2019-06-21T20:13:01Z"],"dc:date.issued":["2018-11"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. xviii, 122 p."],"dc:description.abstract":["Temporal gene expression data contains ample information to characterize gene function and is now widely used in bio-medical research. A dense temporal gene ex- pression usually shows various patterns in expression levels under dfferent biological conditions. Existing literature models the gene trajectory using the mean function. Also, temporal gene expression curves generally show a strong degree of heterogeneity between multiple conditions. As a result, rate of change of gene expression may be dfferent in non-central locations and a mean model can not capture the non-central location of the gene expression distribution. However, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical to analyze gene data. In this research, we use linear quantile mixed model to analyze gene expression data. One of the notable advantages for quantile re- gression is that models error term do not assume any specific parametric distribution. This method enables the study of gene expression change over time by estimating a family of quantile functions. A statistical test is proposed to test the equality of two dfferent gene expres- sions based on estimated quantile regression parameters. Then, we investigate the performance of the proposed test statistic through extensive simulation studies. Sim- ulation studies demonstrate the good statistical performance of this proposed test statistic and conclude that this method is robust to violations of error assumptions. As an illustration, this proposed method is used to analyze a data set of 18 genes in P. aeruginosa, expressed in 24 biological conditions. 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