dc.creator | Tasoulis S.K., Vrahatis A.G., Georgakopoulos S.V., Plagianakos V.P. | en |
dc.date.accessioned | 2023-01-31T10:06:54Z | |
dc.date.available | 2023-01-31T10:06:54Z | |
dc.date.issued | 2019 | |
dc.identifier | 10.1109/BigData.2018.8622170 | |
dc.identifier.isbn | 9781538650356 | |
dc.identifier.uri | http://hdl.handle.net/11615/79632 | |
dc.description.abstract | Recent sequencing technology breakthroughs have resulted in a dramatic increase in the amount of available sequencing data, enabling major scientific advances in biology and medicine. Nowadays, sequencing transcriptome data of single cells (scRNA-seq) are growing rapidly, posing new challenges in their analysis, mostly due to their high dimensionality. In this paper, we study the problem of visualizing such high-dimensional scRNA-seq data. A new visualization scheme is presented based on a customized distance matrix retrieved by applying independently Nearest Neighbors search through multiple Random Projections. The proposed method is compared against well-known dimensionality reduction and visualization techniques showing its capabilities and performance. © 2018 IEEE. | en |
dc.language.iso | en | en |
dc.source | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062644775&doi=10.1109%2fBigData.2018.8622170&partnerID=40&md5=82bfb985fba5031e96f449ff51d624c7 | |
dc.subject | Big data | en |
dc.subject | Flow visualization | en |
dc.subject | RNA | en |
dc.subject | Visualization | en |
dc.subject | Biology and medicine | en |
dc.subject | Dimensionality reduction | en |
dc.subject | High dimensionality | en |
dc.subject | Random projections | en |
dc.subject | RNA-Seq datum | en |
dc.subject | Scientific advances | en |
dc.subject | Transcriptome datum | en |
dc.subject | Visualization technique | en |
dc.subject | Data visualization | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Visualizing High-dimensional single-cell RNA-sequencing data through multiple Random Projections | en |
dc.type | conferenceItem | en |