Shiny Single Cell Browser

Open-source interactive visualization of single cell RNAseq datasets. Features include:

• Visualize cluster distribution, marker gene expression and cluster-averaged expression of gene lists.
• Interactive visualization: clicking on individual genes in the gene list plot shows their expression on t-SNE/UMAP plot.
• Specify pre-analyzed datasets (Seurat format) in the JSON config file for automatic data loading.

Setting up and launch the App

• Download the App, git clone https://github.com/yueqiw/shiny_cell_browser.git
• Install dependencies as listed below.
• To update, cd shiny_cell_browser then git pull
• Store Seurat data objects as .rds files
• Optionally, store cluster colors as a vector in seurat_data@misc[[sprintf("%s_colors", cluster_name)]]
• Specify the file paths and clustering results by creating data/config.json file. Follow the example in data/example_config.json. The App will automatically open the files specified in startup_data1/2/3.
• To launch Single Cell Browser locally, run the following code.

cd shiny_cell_browser
R -e "shiny::runApp('./', port=1234)
## or store the lunch script in run_app.sh and run the following
./run_app.sh 

• This should launch the web browser at http://127.0.0.1:1234/
• If you want other computers in the local network to access the web app, run R -e "shiny::runApp('./', host='0.0.0.0' port=1234). Then visit your-ip-address:1234

Example config.json file:

{
    "data": [
        {
            "name": "My favorite sample",
            "file": "path/to/seurat/data.rds",
            "clusters": "res.1",
            "embedding": "umap_1"
        },
        {
            "name": "My 2nd favorite sample",
            "file": "path/to/seurat/data.rds",
            "clusters": "res.1",
            "embedding": "tsne_1"
        }
    ],
    "config": {
        "startup_data1": 1,
        "startup_data2": 2,
        "startup_data3": "none",
        "default_layout": "horizontal",
        "default_viz_mode": "interactive",
        "default_single_gene": "none"
    }
}

Dependencies

Check the Dockerfile.

Updates

08-15-2018

• Full application rewrite for speed and simplicity in viewing only 1 dataset at a time. Table added for differential expression navigation. Download features still need to be re-implemented.

07-17-2018

• Moved data loading to the global environment rather than per user session. Added more config options.

07-15-2018

• Visualize up to 3 datasets simultaneously and interactively in the same window.
• Both horizontal and vertical layouts are available.

07-14-2018

• Add Plotly interactive mode. Click on a gene in the dot plot to show it's distribution on t-SNE/UMAP.
• The interactive mode requires the new ggplot2 v3.0.0 and the dev branch of plotly.
• Non-interactive mode works for both ggplot2 v2.2.1 and v3.0.0

07-13-2018

• Visualize two datasets simultaneously. Can easily switch beteen more datasets from the dropdown menu.
• Plot cluster distribution on UMAP/t-SNE plots.
• Plot the expression pattern of individual marker genes on UMAP/t-SNE embeddings.
• Plot cluster-averaged expression of marker gene lists using dot plots.
• Automatic resizing/scaling of figures to fit different browser windows and screen resolutions.
• Export publication-quality figures in PDF and PNG formats. (recommend using manual resizing for consistency)
• Specify pre-analyzed datasets in the JSON config file (see example_config.json).
• Currently support Seurat data format.