dc.contributor.author
Zhao, Yan
dc.date.accessioned
2024-11-04T14:14:48Z
dc.date.available
2024-11-04T14:14:48Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/45333
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-45045
dc.description.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 time-consuming and laborious. To address this, biclustering algo- rithms 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 (CAb- iNet), a graph-based 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 parame- ters. 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.
en
dc.format.extent
xviii, 135 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Biclustering
en
dc.subject
Single-cell RNA sequencing
en
dc.subject
Bicluster visualization
en
dc.subject
Correspondence Analysis
en
dc.subject.ddc
500 Natural sciences and mathematics::510 Mathematics::519 Probabilities and applied mathematics
dc.subject.ddc
500 Natural sciences and mathematics::570 Life sciences::570 Life sciences
dc.subject.ddc
000 Computer science, information, and general works::000 Computer Science, knowledge, systems::000 Computer science, information, and general works
dc.title
Correspondence analysis based biclustering and joint visualization of cells and genes for single cell transcriptomic data
dc.contributor.gender
female
dc.contributor.firstReferee
Vingron, Martin
dc.contributor.furtherReferee
Beißbarth, Tim
dc.date.accepted
2024-07-26
dc.identifier.urn
urn:nbn:de:kobv:188-refubium-45333-9
dc.title.translated
Auf Korrespondenzanalyse basierendes Biclustering und gemeinsame Visualisierung von Zellen und Genen für transkriptomische Einzel
ger
refubium.affiliation
Mathematik und Informatik
dcterms.accessRights.dnb
free
dcterms.accessRights.openaire
open access
dcterms.accessRights.proquest
accept