Next-generation DNA- and RNA-sequencing (RNA-seq) technologies have expanded rapidly in both throughput and accuracy within the last decade. The momentum continues as emerging techniques become increasingly capable of profiling molecular content at the level of individual cells. One goal of this research is to put forward best practices in the design of single-cell RNA-sequencing (scRNA-seq) experiments, specifically as it relates to choices regarding the trade-off between sequencing depth and sample size. In addition to general guidelines, an interactive tool is presented to aid researchers in making experiment-specific decisions that are informed by real data and practical constraints. Further, a new approach to the modeling and testing of differential gene expression in scRNA-seq data is proposed, which notably incorporates salient features (e.g. highly zero-inflated expression values) of single-cell transcription that are otherwise obscured at the tissue level. As single-cell technologies offer an unprecedented window into cell-to-cell heterogeneity and its biological consequences, it is essential that suitable approaches are adopted for both the design and analysis of these experiments.
","abstract_html":"<p>Next-generation DNA- and RNA-sequencing (RNA-seq) technologies have expanded rapidly in both throughput and accuracy within the last decade. The momentum continues as emerging techniques become increasingly capable of profiling molecular content at the level of individual cells. One goal of this research is to put forward best practices in the design of single-cell RNA-sequencing (scRNA-seq) experiments, specifically as it relates to choices regarding the trade-off between sequencing depth and sample size. In addition to general guidelines, an interactive tool is presented to aid researchers in making experiment-specific decisions that are informed by real data and practical constraints. Further, a new approach to the modeling and testing of differential gene expression in scRNA-seq data is proposed, which notably incorporates salient features (<em>e.g.</em> highly zero-inflated expression values) of single-cell transcription that are otherwise obscured at the tissue level. As single-cell technologies offer an unprecedented window into cell-to-cell heterogeneity and its biological consequences, it is essential that suitable approaches are adopted for both the design and analysis of these experiments.</p>","abstract_has_math":false,"creators":["Zheng, Faye H"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Rebecca W. Doerge","Hyonho Chun","Bruce A. Craig","Gayla R. Olbricht","Timothy L. Ratliff"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-08-01T07:00:00Z","date_published":"2016-08-01T07:00:00Z","updated_at":"2026-07-24T03:54:02Z","subjects":["Pure Sciences","Statistics and Probability"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://docs.lib.purdue.edu/open_access_dissertations/897","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rebecca W. Doerge","Hyonho Chun","Bruce A. Craig","Gayla R. Olbricht","Timothy L. Ratliff"]},{"key":"dc:creator","label":"Author","values":["Zheng, Faye H"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pure Sciences","Statistics and Probability"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://docs.lib.purdue.edu/open_access_dissertations/897"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Next-generation DNA- and RNA-sequencing (RNA-seq) technologies have expanded rapidly in both throughput and accuracy within the last decade. The momentum continues as emerging techniques become increasingly capable of profiling molecular content at the level of individual cells. One goal of this research is to put forward best practices in the design of single-cell RNA-sequencing (scRNA-seq) experiments, specifically as it relates to choices regarding the trade-off between sequencing depth and sample size. In addition to general guidelines, an interactive tool is presented to aid researchers in making experiment-specific decisions that are informed by real data and practical constraints. Further, a new approach to the modeling and testing of differential gene expression in scRNA-seq data is proposed, which notably incorporates salient features (e.g. highly zero-inflated expression values) of single-cell transcription that are otherwise obscured at the tissue level. As single-cell technologies offer an unprecedented window into cell-to-cell heterogeneity and its biological consequences, it is essential that suitable approaches are adopted for both the design and analysis of these experiments.
"]},{"key":"dc:title","label":"Title","values":["The design and statistical analysis of single-cell RNA-sequencing experiments"]}]}],"canonical_facts":{"dc:contributor":["Rebecca W. Doerge","Hyonho Chun","Bruce A. Craig","Gayla R. Olbricht","Timothy L. Ratliff"],"dc:creator":["Zheng, Faye H"],"dc:description.abstract":["Next-generation DNA- and RNA-sequencing (RNA-seq) technologies have expanded rapidly in both throughput and accuracy within the last decade. The momentum continues as emerging techniques become increasingly capable of profiling molecular content at the level of individual cells. One goal of this research is to put forward best practices in the design of single-cell RNA-sequencing (scRNA-seq) experiments, specifically as it relates to choices regarding the trade-off between sequencing depth and sample size. In addition to general guidelines, an interactive tool is presented to aid researchers in making experiment-specific decisions that are informed by real data and practical constraints. Further, a new approach to the modeling and testing of differential gene expression in scRNA-seq data is proposed, which notably incorporates salient features (e.g. highly zero-inflated expression values) of single-cell transcription that are otherwise obscured at the tissue level. As single-cell technologies offer an unprecedented window into cell-to-cell heterogeneity and its biological consequences, it is essential that suitable approaches are adopted for both the design and analysis of these experiments.
"],"dc:identifier":["https://docs.lib.purdue.edu/open_access_dissertations/897"],"dc:subject":["Pure Sciences","Statistics and Probability"],"dc:title":["The design and statistical analysis of single-cell RNA-sequencing experiments"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:54:02Z"}