class: center, middle, inverse, title-slide .title[ # Interactive exploratory analysis with iSEE ] .author[ ### <a href="https://csoneson.github.io">Charlotte Soneson</a> ] .institute[ ### Computational Biology Platform, Friedrich Miescher Institute for Biomedical Research ] .date[ ### June 20, 2022 ] --- layout: true --- background-image: url("data:image/png;base64,#images/why-interactive-visualization/why-interactive-visualization.001.jpeg") background-position: center background-size: cover # Why (interactive) visualization? <div class="my-footer"><span>Images from Pixabay, available for commercial use, no attribution required</span></div> --- # What is iSEE? * An open-source R/Bioconductor package that allows you to easily create customized interactive interfaces for exploring your data and visualize any **pre-calculated** results.  <div class="my-footer"><span>http://shiny.imbei.uni-mainz.de:3838/iSEE_covidIT/</span></div> --- # What is iSEE? * An open-source R/Bioconductor package that allows you to easily create customized interactive interfaces for exploring your data and visualize any **pre-calculated** results. * Essentially data type agnostic - any rectangular data can be explored (but it was designed with high-throughput biological data in mind). -- * Easy to run (for a suitably formatted data set, more on that shortly): ```r library(iSEE) iSEE(sce) ``` -- * Highly configurable, flexible interface. -- * Strong focus on reproducibility, all code to generate displayed figures can be downloaded. --- # What about other tools? https://github.com/federicomarini/awesome-expression-browser  ??? Over 130 entries --- # With what data can iSEE be used? * Any form of (numeric) rectangular-shaped data (measurements for some form of **features** across a range of **samples**). * Both features and samples can come with **annotations**/additional information. * Data must be stored in a [**`SummarizedExperiment`**](https://bioconductor.org/packages/SummarizedExperiment/) container (or one of its derivatives, including [`SingleCellExperiment`](https://bioconductor.org/packages/SingleCellExperiment/) or `DESeqDataSet`). <center>  </center> <div class="my-footer"><span>Modified from https://bioconductor.github.io/BiocWorkshops/analysis-of-single-cell-rna-seq-data-dimensionality-reduction-clustering-and-lineage-inference.html</span></div> --- # With what data can iSEE be used? * Any form of (numeric) rectangular-shaped data (measurements for some form of **features** across a range of **samples**). * Both features and samples can come with **annotations**/additional information. * Data must be stored in a [`SummarizedExperiment`](https://bioconductor.org/packages/SummarizedExperiment/) container (or one of its derivatives, including [**`SingleCellExperiment`**](https://bioconductor.org/packages/SingleCellExperiment/) or `DESeqDataSet`). <center>  </center> <div class="my-footer"><span>Modified from https://bioconductor.github.io/BiocWorkshops/analysis-of-single-cell-rna-seq-data-dimensionality-reduction-clustering-and-lineage-inference.html</span></div> --- # How to get to a SummarizedExperiment/SingleCellExperiment object? * Use the constructor functions: `SummarizedExperiment()`, `SingleCellExperiment()`, `DESeqDataSet()`, `DESeqDataSetFromMatrix()`, ... * Many Bioconductor packages directly import data into a `SingleCellExperiment` object (e.g., `DropletUtils::read10xCounts()`) or a `SummarizedExperiment` object (e.g., `tximeta::tximeta()`). * From an `AnnData` object: use e.g. the [zellkonverter](https://bioconductor.org/packages/zellkonverter/) Bioconductor package. * From a `Seurat` object: use e.g. `Seurat::as.SingleCellExperiment()`. --- # Default panels         --- # Some additional iSEEu panels (1) <center>   </center> --- # Some additional iSEEu panels (2) <center>   </center> --- # Some additional iSEEu panels (3) <center>  </center> --- # Some additional iSEEu panels (4) <center>  </center> --- # Additional resources * The bookdown book on _Extending iSEE_: https://isee.github.io/iSEE-book/ * Bioconductor landing page: https://bioconductor.org/packages/iSEE/ * Publication (F1000Research, 2018): https://f1000research.com/articles/7-741/v1 * Deployed examples: https://marionilab.cruk.cam.ac.uk/, code at https://github.com/iSEE/iSEE2018 * Further deployments in the https://github.com/iSEE/iSEE_instances repo * `iSEE` in production: http://www.teichlab.org/singlecell-treg, https://libd.shinyapps.io/tran2020_Amyg/, http://shiny.imbei.uni-mainz.de:3838/iSEE_covidIT/ * Development version (bug reports etc): https://github.com/iSEE/iSEE * The `#iSEE` channel in the [Bioconductor slack workspace](https://bioc-community.herokuapp.com/) * Additional panels and modes in `iSEEu`: https://bioconductor.org/packages/iSEEu/ <br> <center>     <br> Kevin Rue-Albrecht, Federico Marini, Charlotte Soneson, Aaron Lun </center>