Thema der Dissertation:
Correspondence analysis based biclustering and joint visualization of cells and genes for single cell transcriptomic data Thema der Disputation:
Community structure dection algorithms
Correspondence analysis based biclustering and joint visualization of cells and genes for single cell transcriptomic data Thema der Disputation:
Community structure dection algorithms
Abstract: Cell clustering is a crucial step in current single-cell RNA sequencing (scRNA-seq) methods, where marker genes are identified and used for cell type annotation. However, this process can be timeconsuming and laborious. To address this, biclustering algorithms have been developed to simultaneously identify functional gene sets and cell clusters. However, most existing biclustering algorithms are designed for microarray and bulk RNA sequencing data, and only a few are suitable for scRNA-seq analysis. These algorithms often suffer from issues such as limited scalability and accuracy. In this study, we propose Correspondence Analysis based biclustering on Networks (CAbiNet), a graphbased biclustering approach specifically designed for scRNA-seq data. CAbiNet integrates multiple analysis steps by efficiently co-clustering cells and their marker genes, and visualizing the biclustering results in a non-linear embedding. We introduce two visualization approaches that enable the joint display of genes and cells in a two-dimensional space. Additionally, a random forest regression model is trained to predict the quality of clustering results, facilitating the selection of optimal parameters. CAbiNet fills the gap for a high-performing biclustering algorithm in scRNA-seq and spatial transcriptomics data analysis. It streamlines existing workflows and offers an intuitive and interactive visual exploration of cells and their marker genes in a single plot for efficient cell type annotation. CAbiNet is available as an R package on GitHub at https://github.com/VingronLab/CAbiNet .
Zeit & Ort
26.07.2024 | 15:00
Seminarraum 006
(Fachbereich Mathematik und Informatik, Takustr. 9, 14195 Berlin)