amc-heme / scExploreR

Shiny app for single cell omics data visualization
https://amc-heme.github.io/scExploreR/
MIT License
3 stars 0 forks source link

scExploreR

Welcome to the single cell visualization tool you didn't know you were looking for! This app is designed to make exploration of highly complex data sets easy for anyone, regardless of informatics background. Whether you're a researcher looking to make sense of your single cell data, or a bioinformatician looking to present your results interactively, you will find value in this app. scExplorer bridges the gap between domain (disease and clinical) specific knowledge and informatics expertise by providing a no-code platform for biologists to analyze data.

scExploreR further facilitates analysis through compatability with most common single cell data formats! Seurat, SingleCellExpreiment, or Anndata objects can be used, and Seurat v5 objects with BP Cells assays are also supported.

Some bioinformatics experience is required to install the app and configure each single-cell dataset, but once set up, the app can be used by anyone.

Requirements

If using anndata objects, reticulate must be installed with the following Python packages:

Installation and Use

  1. Install from Github using Remotes.
    remotes::install_github("amc-heme/scExploreR")

If this is your first time setting up scExploreR, we reccomend you view the App Setup Walkthrough, which applies the process in steps 3-5 to an example object.

  1. Process or obtain a finalized single cell object.

  2. Configure an object for the browser by using the configuration app provided with the package. For more information on the config app, see the Full Config App Documentation or the App Setup Walkthrough.

    scExploreR::run_config(
    object_path = "path_to_your_seurat_object.rds",
    # The config path will be blank the first time you use the config app for an object
    config_path = "previously_loaded_config_file"
    )
  3. To set up a browser for others to use, create a browser config YAML file (If you are using the browser locally for your own use, skip to step 4). The file will contain a list of datasets with the path to the objects and config files for each, along with browser specific settings.

  4. Run scExploreR. There are multiple ways to do this:

Future Goals