Speaker
Description
How should we best model multinomial tradeoffs? When there are only two options that sum to 100 percent, it can be straightforward to employ options like GLM to address research questions with binomial response data, but when there are more than two groups, these multinomial models become too complex to perform and too convoluted for our audience to understand. However, using simplexes to define the experimental outcome space instead of trying to fit the data into conventional statistical space brings the inherent trade-offs of the data to the forefront and hypothesis testing becomes intuitive. In this talk I will demonstrate how two sets of data benefit from simplex analysis and how we can visualize the results easily. First, by making small modifications to the ‘Ternary’ package, I will show an experiment examining the outcome of gene knockdowns modifying the probability that a cell is arrested in different stages of the cell division cycle (G1, G2, S), and how that can be modeled using ternary diagrams. Then, using the ‘klaR’ package, I will show how this idea can be scaled into larger dimensions by highlighting an experiment examining four possible types of instruction observed in college classrooms. Finally, we will explore how the ‘plotly’ package can be modified to show tetrahedrons in three dimensions, allowing us to make interactive simplex diagrams.
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No AI tools/services were used.
Additional Material or Paper
We published results using this approach earlier this year: https://www.lifescied.org/eprint/SWNC4KAFBUAZUCYHXEYH/full
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Simplex models, |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | This will be my first time attending this conference, but if my input will still be valuable, I am happy to help: lomueller@ucsd.edu |