Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data
| dc.contributor.author | Fernstad, Sara Johansson | en_US |
| dc.contributor.author | Macquisten, Alexander | en_US |
| dc.contributor.author | Berrington, Janet | en_US |
| dc.contributor.author | Embleton, Nicholas | en_US |
| dc.contributor.author | Stewart, Christopher | en_US |
| dc.contributor.editor | Turkay, Cagatay and Vrotsou, Katerina | en_US |
| dc.date.accessioned | 2020-05-24T13:31:30Z | |
| dc.date.available | 2020-05-24T13:31:30Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Studies of genome sequenced data are increasingly common in many domains. Technological advances enable detection of hundreds of thousands of biological entities in samples, resulting in extremely high dimensional data. To enable exploration and understanding of such data, efficient visual analysis approaches are needed that take domain and data specific requirements into account. Based on a survey with bioscience experts, this paper suggests a categorisation and a set of quality metrics to identify patterns of interest, which can be used as guidance in visual analysis, as demonstrated in the paper. | en_US |
| dc.description.sectionheaders | Visual Analysis of High Dimensional and Temporal Data | |
| dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | |
| dc.identifier.doi | 10.2312/eurova.20201083 | |
| dc.identifier.isbn | 978-3-03868-116-8 | |
| dc.identifier.issn | 2664-4487 | |
| dc.identifier.pages | 31-35 | |
| dc.identifier.uri | https://doi.org/10.2312/eurova.20201083 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurova20201083 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | ] |
| dc.subject | Human centered computing | |
| dc.subject | Visual analytics | |
| dc.subject | Applied computing | |
| dc.subject | Bioinformatics | |
| dc.title | Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data | en_US |