{"658834":{"#nid":"658834","#data":{"type":"event","title":"PhD Defense by Hong Seo Lim","body":[{"value":"\u003Cp\u003EHong Seo Lim\u003Cbr \/\u003E\r\nBME PhD Defense Presentation\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EDate\u003C\/strong\u003E:2022-06-27\u003Cbr \/\u003E\r\n\u003Cstrong\u003ETime\u003C\/strong\u003E: 10AM - 12PM\u003Cbr \/\u003E\r\n\u003Cstrong\u003ELocation \/ Meeting Link\u003C\/strong\u003E: EBB Krone 1005 CHOA Seminar Room \/ \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/98561785488\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/98561785488\u003C\/a\u003E\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003ECommittee Members:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nPeng Qiu, Ph.D. (Advisor), Edward Botchwey, Ph.D. Kavita Dhodapkar, M.D. Eva Dyer, Ph.D. Eberhard Voit, Ph.D.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003ETitle\u003C\/strong\u003E: Developing Graph-based Computational Algorithms for Single-cell Data Science\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nExplosive advances in single-cell measurement technologies allow in-depth analysis of the cellular heterogeneity of the biological systems of interest. Single-cell profiling through flow cytometry, mass cytometry, and single-cell RNA sequencing (scRNA-seq) has led to novel discoveries in immunology, virology, neuroscience, and cancer biology. Single-cell data science is a new discipline encompassing the usage of statistics, mathematics, or machine learning for various computational challenges arising in single-cell profiling data and subsequent analysis steps. In this thesis, we have identified several single-cell related challenges that need proper attention: (1) proper integration of single-cell datasets acquired from different technologies or affected by batch effect, (2) quantification of cluster-like and trajectory-like characteristics of scRNA-seq datasets for proper algorithm choice, and (3) quantification of cell-type-specific differences across the single-cell dataset. We provide graph-based computational tools to tackle these challenges. The novel computational tools we developed are as follows: (1) We propose a new algorithm, JSOM, to align two datasets through jointly evolved self-organizing maps. We demonstrated that the JSOM maps could be used to identify related clusters between the two datasets, and we demonstrated the alignment of various single-cell profiling datasets. (2) We present five scoring metrics and a new pipeline to quantify geometric characteristics of scRNA-seq data, more specifically, the clusterness and trajectoriness of the data. The proposed scoring metrics are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley\u0026#39;s K, and degrees of separation, and we demonstrated that our pipeline could quantify clusterness and trajectoriness of scRNA-seq data. (3) We present a new pipeline to quantify cell-type-specific differences and identify features driving the variation. Our pipeline exploits the quantifiable differences seen in the low-dimensional UMAP and used SHAP analysis to measure the differences, and we demonstrated the algorithm\u0026rsquo;s utility in interpreting and quantifying differences in various single-cell profiling data. Overall, the developed computational tools would improve various steps of the single-cell data analysis pipeline, contributing to solving computational challenges posed in the field of single-cell data science.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Developing Graph-based Computational Algorithms for Single-cell Data Science"}],"uid":"27707","created_gmt":"2022-06-13 16:30:31","changed_gmt":"2022-06-24 12:17:30","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-06-27T11:00:00-04:00","event_time_end":"2022-06-27T13:00:00-04:00","event_time_end_last":"2022-06-27T13:00:00-04:00","gmt_time_start":"2022-06-27 15:00:00","gmt_time_end":"2022-06-27 17:00:00","gmt_time_end_last":"2022-06-27 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}