ゲノム情報科学研究教育機構  アブストラクト
Date 2:00pm May 18, 2022
Speaker Keita Iida
Osaka University
Title Clustering single-cell and spatial transcriptomes through multifaceted biological aspects
Abstract
Single-cell transcriptomics have deepened our knowledge of biological complexity in terms of molecular heterogeneity in cell populations. However, conventional signature gene-based approaches may be insufficient in capturing such complexity as genes can interact with each other to regulate a certain biological function. Here, we introduce ASURAT, a computational pipeline for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process, and signaling pathway activity for single-cell transcriptomic data. Mimicking Saussure’s idea for defining “meaning", we introduce correlation structures into functionally categorized gene sets defined in knowledge-based databases, such as Cell Ontology, Disease Ontology, Gene Ontology databases, and Kyoto Encyclopedia of Genes and Genomes. Then, we apply a novel correlation graph decomposition to each gene set, producing a triplet we term "sign" containing biological description, subset of genes, and correlation structure, which is the central concept. To validate usability and clustering performance, we apply ASURAT on single-cell RNA sequencing and spatial transcriptome datasets of human pancreatic ductal adenocarcinoma (Moncada et al., Nat. Biotechnol. 38, 2020). We demonstrate that ASURAT is able not only to cluster spatial transcriptomic data into functionally distinct tissue regions but also identify de novo atypical regions.

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