dc.creator | Vrahatis A.G., Tasoulis S.K., Dimitrakopoulos G.N., Plagianakos V.P. | en |
dc.date.accessioned | 2023-01-31T11:37:17Z | |
dc.date.available | 2023-01-31T11:37:17Z | |
dc.date.issued | 2019 | |
dc.identifier | 10.1109/CIBCB.2019.8791482 | |
dc.identifier.isbn | 9781728114620 | |
dc.identifier.uri | http://hdl.handle.net/11615/80762 | |
dc.description.abstract | The recent advent in Next Generation Sequencing has created a huge data source which offers a great potential for elucidating complex disease mechanisms and biological processes. A recent technology is the single-cell RNA sequencing, which allows transcriptomics measurements in individual cells, having promising results. However, such studies measure the entire genome for thousands of cells, creating datasets with extremely high dimensionality and complexity. Following this perspective, we propose a dimensionality reduction approach, called RGt-SNE, which visualizes single-cell RNA-seq data in two dimensions. Initially, RGt-SNE defines a cell-cell distance matrix based on Random Projections and Geodesic Distances. The first is used to define the pairwise cells distances in a low dimensional projected space avoiding the difficulties that exist in data with ultra-high dimensionality. The latter is used to better define the large pairwise cells distances. Subsequently, the t-SNE method is applied in the customized distance matrix for two dimensional visualization. RGt-SNE was evaluated in two real experimental single-cell RNA-seq data against three well-known methods, such as t-SNE, Multidimensional scaling, and ISOMAP. Outcomes provide the superiority of RGt-SNE suggesting it as a reliable tool for single-cell RNA-seq data analysis and visualization. © 2019 IEEE. | en |
dc.language.iso | en | en |
dc.source | 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071493422&doi=10.1109%2fCIBCB.2019.8791482&partnerID=40&md5=acd1b2e7566a21f9fb344d9e6bec2275 | |
dc.subject | Artificial intelligence | en |
dc.subject | Bioinformatics | en |
dc.subject | Cells | en |
dc.subject | Clustering algorithms | en |
dc.subject | Data visualization | en |
dc.subject | Flow visualization | en |
dc.subject | Geodesy | en |
dc.subject | Matrix algebra | en |
dc.subject | RNA | en |
dc.subject | Visualization | en |
dc.subject | Biological process | en |
dc.subject | Dimensionality reduction | en |
dc.subject | High dimensional data | en |
dc.subject | High dimensionality | en |
dc.subject | Multi-dimensional scaling | en |
dc.subject | Next-generation sequencing | en |
dc.subject | Rna-seq data analysis | en |
dc.subject | Single cells | en |
dc.subject | Cytology | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Visualizing High-Dimensional Single-Cell RNA-seq Data via Random Projections and Geodesic Distances | en |
dc.type | conferenceItem | en |