<br/> <br/> .microbiome-figure-top[ <img src="figures/microbiome-header-top.png"></img> ] <div id="links"> Slides: https://go.wisc.edu/02jao8 <br> Lab Site: https://go.wisc.edu/pgb8nl </div> ### Interactive Multiview Data Science My lab develops interactive tools to help researchers understand microbial ecosystems. We make sure that our methods can be adapted to complex experimental designs and combinations of 'omics. .pull-left[ Why is multi-omics helpful? If the microbiome is a symphony, then each data source lets us hear a new set of instruments. ] .pull-right[ <img src="figures/symphony-score-2.png"/> ] <br/> <br/> .microbiome-figure-bottom[ <img src="figures/microbiome-header-2.png"></img> ] --- ### The Future is Interactive If you’ve taken any of my classes, you know that I use live coding to teach data analysis methods: 1. There are often mistakes and dead ends that we manage to work past. 2. We can gradually improve our analysis through critical re-evaluation. 3. We can easily check and refine our models, in the sense of [1; 2]. .center[ <img src="figures/data_flow.png" width=600/> ] --- ### The Future is Interactive **Why**: My dream is to have a similarly fluid, interactive workflow for multi-omics. Interacting with data and models at all stages will promote both rigor and imagination in data analysis. **How**: Build modular, user-centric software for multimodal data transformation, modeling, and visualization. .center[ <img src="figures/multimodal_flow.png" width=600/> ] --- ### Example: Visual Interactivity .pull-left[ 1. Shneiderman’s Mantra: "Overview first, zoom and filter, then details-on-demand" [3] 2. Lab member Kaiyan Ma has written an R package applying this logic to longitudinal multi-omics data visualization. ] .pull-right[ <img src="figures/molpad_recording.gif"/> ] --- ### Conclusion Interactive computing goes beyond visualization, and we have also written software to support interactive generative modeling and inference in microbiome problems. * You can learn more at [go.wisc.edu/pgb8nl](go.wisc.edu/pgb8nl) and at our booth. * Email: [ksankaran@wisc.edu](mailto:ksankaran@wisc.edu) --- ### References [1] A. Gelman. "Exploratory Data Analysis for Complex Models". In: _Journal of Computational and Graphical Statistics_ 13 (2004), pp. 755-779. [2] H. Wickham and G. Grolemund. "R for Data Science: Import, Tidy, Transform, Visualize, and Model Data". In: _O'Reily_ (2016). <https://api.semanticscholar.org/CorpusID:196030436>. [3] B. Shneiderman. "The eyes have it: a task by data type taxonomy for information visualizations". In: _Proceedings 1996 IEEE Symposium on Visual Languages_ (1996), pp. 336-343. <https://api.semanticscholar.org/CorpusID:2281975>.