.microbiome-figure-top[ <img src="figures/microbiome-header-top.png"></img> ] <div id="links"> Slides: https://go.wisc.edu/uhka79 <br> Lab Site: https://go.wisc.edu/pgb8nl </div> <br/> <br/> ### Microbiome Data Science **What**: My lab develops data science tools to help better understand microbial ecosystems, often by combining several 'omics views. **Who**: Some of our active collaborations include work with: * Psychologists to understand the gut-brain axis. * Physicians to understand inflammation and HIV transmission. * Microbiologists to understand pathogen invasion in plant roots. .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="data:image/png;base64,#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="data:image/png;base64,#figures/multimodal_flow.png" width=600/> ] --- ### Example 1: 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="data:image/png;base64,#figures/molpad_recording.gif"/> ] --- ### Example 2: Developer Interactivity 1. Simulation can guide experimental design, methods benchmarking, and comparison with synthetic nulls. 1. A modular approach lets researchers experiment more interactively. ```r simulator <- setup_simulator(exper, ~ ns(Age, 3) * Genotype, ~ GaussianLSS()) |> estimate() samples <- sample(simulator) ``` .pull-three-quarters-left[ <img src="data:image/png;base64,#figures/gaussian_fit.png" width=840 style="top: 390px; left: 10px; position: absolute"/> ] .pull-three-quarters-right[ <img src="data:image/png;base64,#figures/pairwise_cors.png" width=220 style="bottom: 25px; right: 120px; position: absolute"/> ] --- ## Example 2: Developer Interactivity ```r simulator <- simulator |> mutate(any_of(nulls), link = ~ ns(Age, 3)) |> estimate() ``` .pull-three-quarters-left[ <img src="data:image/png;base64,#figures/nulls_unaltered.png"/> ] .pull-three-quarters-right[ <img src="data:image/png;base64,#figures/pairwise_cors.png"/> ] --- ## Example 2: Developer Interactivity ```r simulator <- simulator |> mutate(any_of(nulls), link = ~ ns(Age, 3)) |> estimate() ``` .pull-three-quarters-left[ <img src="data:image/png;base64,#figures/altered_ns.png"/> ] .pull-three-quarters-right[ <img src="data:image/png;base64,#figures/pairwise_cors_altered.png"/> ] --- ## Example 2: Developer Interactivity ```r simulator <- simulator |> mutate(any_of(nulls), link = ~ Age) |> estimate() ``` .pull-three-quarters-left[ <img src="data:image/png;base64,#figures/altered_linear.png"/> ] .pull-three-quarters-right[ <img src="data:image/png;base64,#figures/pairwise_cors_altered_linear.png"/> ] --- ## Example 2: Developer Interactivity ```r experiments <- list(methylation = SCGEMMETH_sce, rna = SCGEMRNA_sce) families <- list(~ BI(), ~ GaussianLSS()) sims <- experiments |> map2(families, \(x, y) setup_simulator(x, ~ cell_type, y)) |> join_pamona() ``` <img src="data:image/png;base64,#figures/simulator_join.png" width=1300/> --- ### Reaching Out * You can learn more at [go.wisc.edu/pgb8nl](go.wisc.edu/pgb8nl). * I enjoy working with students with a variety of educational levels and backgrounds. * I encourage you to reach out for any reason -- I'm always happy to talk about statistics. * 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. <https://api.semanticscholar.org/CorpusID:15403312>. [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>.