bioRxiv preprint doi: https://doi.org/10.1101/411595; this version posted May 19, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. OLIVER: A Tool for Visual Data Analysis on Longitudinal Plant Phenomics Data Oliver L Tessmer David M Kramer∗ Jin Chen∗ Dept. of Energy Plant Research Lab Dept. of Energy Plant Research Lab Inst. for Biomedical Informatics Michigan State University Michigan State University University of Kentucky East Lansing, USA East Lansing, USA Lexington, USA
[email protected] [email protected] [email protected] Abstract—There is a critical unmet need for new tools to phenotyping make it possible to probe plant growth, photo- analyze and understand “big data” in the biological sciences synthesis and other properties under dynamic environmental where breakthroughs come from connecting massive genomics conditions [7], [11], [18], [31], [44]. Similar approaches are data with complex phenomics data. By integrating instant data visualization and statistical hypothesis testing, we have developed impacting other fields, such as biochemistry, drug development a new tool called OLIVER for phenomics visual data analysis and behavior studies [3], [5], [6], [40]. with a unique function that any user adjustment will trigger real- Despite these major phenotyping technological advances, time display updates for any affected elements in the workspace. biomedical scientists are facing the difficulty of analyzing By visualizing and analyzing omics data with OLIVER, biomed- longitudinal phenomics data, for the nonlinear temporal pat- ical researchers can quickly generate hypotheses and then test their thoughts within the same tool, leading to efficient knowledge terns in a high-dimensional space are difficult to detect.