Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
@inproceedings{10.2312:eurova.20201083,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Turkay, Cagatay and Vrotsou, Katerina},
title = {{Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data}},
author = {Fernstad, Sara Johansson and Macquisten, Alexander and Berrington, Janet and Embleton, Nicholas and Stewart, Christopher},
year = {2020},
publisher = {The Eurographics Association},
ISSN = {2664-4487},
ISBN = {978-3-03868-116-8},
DOI = {10.2312/eurova.20201083}
}