{"id":12659,"date":"2026-06-29T17:07:59","date_gmt":"2026-06-29T08:07:59","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=12659"},"modified":"2026-06-29T17:07:59","modified_gmt":"2026-06-29T08:07:59","slug":"topological-identification-and-interpretation-for-single-cell-gene-regulation-elucidation-across-multiple-platforms-using-scmgca-yun-min-song","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/topological-identification-and-interpretation-for-single-cell-gene-regulation-elucidation-across-multiple-platforms-using-scmgca-yun-min-song\/","title":{"rendered":"Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA – Yun Min Song"},"content":{"rendered":"

In this talk, we discuss the paper \u201cTopological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA\u201d by Zhuohan Yu et al., nature communications<\/em>, 2023.<\/p>\n

Abstract:<\/p>\n

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Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.<\/p>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

In this talk, we discuss the paper \u201cTopological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA\u201d by Zhuohan Yu et al., nature communications, 2023. Abstract: … <\/p>\n

Continue reading “Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA – Yun Min Song”<\/span><\/a><\/p>\n","protected":false},"author":13,"featured_media":0,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[219],"class_list":["post-12659","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-journal-club","cat_journal-club"],"yoast_head":"\nTopological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA - Yun Min Song - Biomedical Mathematics Group<\/title>\n<meta name=\"description\" content=\"Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/topological-identification-and-interpretation-for-single-cell-gene-regulation-elucidation-across-multiple-platforms-using-scmgca-yun-min-song\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA - Yun Min Song - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. 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Abstract: … Continue reading \"Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA – Yun Min Song\"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12659","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/users\/13"}],"version-history":[{"count":1,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12659\/revisions"}],"predecessor-version":[{"id":12660,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12659\/revisions\/12660"}],"wp:attachment":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/media?parent=12659"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tags?post=12659"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events_cat?post=12659"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}