{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1432"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1432","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Comparative analysis of parametric, nonparametric and permutation methods for differential expression","abstract":"DNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells. Microarray experiments typically give us expression measurements on a large number of genes. Increasing popularity of microarray technology has resulted in a number of tests being proposed to detect differentials expression. The purpose of study is to compare the parametric, non parametric and permutation tests when applied to microarray data for differential expression analysis. t test (parametric), Mann Whitney test (nonparametric) and Significance of analysis (permutation ) test are compared. The study focused on comparison of tests based on the ranking of genes by different tests. Biological and simulation data was used to test compare the performance of statistical tests. The result shows that the SAM test outperform the other two tests, under Normal as well as Lognormal data simulation in case of both low and high number of replicates. Application to simulated data also brings out the fact that with increase in number of replicates the performance all the tests improves.","abstract_html":"DNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells. Microarray experiments typically give us expression measurements on a large number of genes. Increasing popularity of microarray technology has resulted in a number of tests being proposed to detect differentials expression. The purpose of study is to compare the parametric, non parametric and permutation tests when applied to microarray data for differential expression analysis. t test (parametric), Mann Whitney test (nonparametric) and Significance of analysis (permutation ) test are compared. The study focused on comparison of tests based on the ranking of genes by different tests. Biological and simulation data was used to test compare the performance of statistical tests. The result shows that the SAM test outperform the other two tests, under Normal as well as Lognormal data simulation in case of both low and high number of replicates. Application to simulated data also brings out the fact that with increase in number of replicates the performance all the tests improves.","abstract_has_math":false,"creators":["Patil, Rahul"],"institution":null,"degree_name":"Master of Science in Computational Biology - (M.S.)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Michael Recce","Qun Ma","Usman W. Roshan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-05-31T07:00:00Z","date_published":"2006-05-31T07:00:00Z","updated_at":"2026-07-24T03:23:22Z","subjects":["Microarray data","Differential expression analysis","Biostatistics","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/433","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Michael Recce","Qun Ma","Usman W. Roshan"]},{"key":"dc:creator","label":"Author","values":["Patil, Rahul"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computational Biology - (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Microarray data","Differential expression analysis","Biostatistics","Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.njit.edu/theses/433"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["DNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells. Microarray experiments typically give us expression measurements on a large number of genes. Increasing popularity of microarray technology has resulted in a number of tests being proposed to detect differentials expression. The purpose of study is to compare the parametric, non parametric and permutation tests when applied to microarray data for differential expression analysis. t test (parametric), Mann Whitney test (nonparametric) and Significance of analysis (permutation ) test are compared. The study focused on comparison of tests based on the ranking of genes by different tests. Biological and simulation data was used to test compare the performance of statistical tests. The result shows that the SAM test outperform the other two tests, under Normal as well as Lognormal data simulation in case of both low and high number of replicates. Application to simulated data also brings out the fact that with increase in number of replicates the performance all the tests improves."]},{"key":"dc:title","label":"Title","values":["Comparative analysis of parametric, nonparametric and permutation methods for differential expression"]}]}],"canonical_facts":{"dc:contributor":["Michael Recce","Qun Ma","Usman W. Roshan"],"dc:creator":["Patil, Rahul"],"dc:description.abstract":["DNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells. Microarray experiments typically give us expression measurements on a large number of genes. Increasing popularity of microarray technology has resulted in a number of tests being proposed to detect differentials expression. The purpose of study is to compare the parametric, non parametric and permutation tests when applied to microarray data for differential expression analysis. t test (parametric), Mann Whitney test (nonparametric) and Significance of analysis (permutation ) test are compared. The study focused on comparison of tests based on the ranking of genes by different tests. Biological and simulation data was used to test compare the performance of statistical tests. The result shows that the SAM test outperform the other two tests, under Normal as well as Lognormal data simulation in case of both low and high number of replicates. Application to simulated data also brings out the fact that with increase in number of replicates the performance all the tests improves."],"dc:identifier":["https://digitalcommons.njit.edu/theses/433"],"dc:subject":["Microarray data","Differential expression analysis","Biostatistics","Computer Sciences"],"dc:title":["Comparative analysis of parametric, nonparametric and permutation methods for differential expression"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science in Computational Biology - (M.S.)"]},"updated_at":"2026-07-24T03:23:22Z"}