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A single page Flask app with a GUI for performing differential expression with scvi-tools. Returns a volcano plot and downloadable tables with DE results.

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scdefg: scvi-tools Differential Expression Flask GUI

This app offer a single page Flask UI that allows you to quickly select cell groups and perform differential expression on single cell RNA sequencing data using scvi-tools

You can see a deployment with 3 C. elegans datasets at scdefg.textpressolab.com. For more information on this see single-cell.wormbase.org.

How to launch

The app takes in a pretrained scVI model saved with and the corresponding anndata (using the option save_anndata = True). You need to specify the path where the model is saved at when launching the app.

The adata anndata should have the fields adata.var.gene_name and adata.var.gene_id as strings containing the respective gene names and gene IDs, so that results will be properly displayed. If the field adata.var.gene_description is also present, they will be shown during the mousover on the interactive plots. Text files with gene descriptions can be downloaded here

Additionally, at least one column should be present in the adata.obs, such as for example cell_type. The cell selection menu can be stratified according to any number of columns that are present in the adata file. When launching the app, you just need to provide the name of each column with the -s flag , eg -s cell_type. So for example, to offer the user the option to group cells by cell_type, tissue and experiment_code you would launch the app using the following arguments:

scdefg/app.py ./model -s tissue -s cell_type -s experiment_code

Alternatively you could provide the columns by which to stratify data may be provided in the field adata.uns['selection_columns'] in a list, e.g. adata.uns['selection_columns']=['sample','cell_type'].

You should try provide the names of tha adata.obs columns that contain relevant conditions to stratify the data by. Otherwise the app defaults to using all columns, and the selection tables will become extremely slow with 100k+ rows.

Below is an example for a trained model that you can try to download and launch. It show how to stratify the data using the columns adata.obs['tissue'], adata.obs['cell_type'] and adata.obs['experiment_code']. T

## Example deploy with data from the CeNGEN project 2020 data release (www.cengen.org)

## clone the repo
git clone https://github.com/Munfred/scdefg.git
cd scdefg
## download example trained model and rename the folder to just `model`
wget -q -O tmp.zip https://github.com/Munfred/scvi-de-flask/releases/download/taylor2020/taylor2020_100955cells_11569genes_20210129_scvi.zip && unzip tmp.zip && rm tmp.zip
mv taylor2020_100955cells_11569genes_20210129_scvi model

## launch the app and stratify the selection menu with 3 columns: `cell_type`, `tissue`, `experiment_code`
scdefg/app.py ./model -s tissue -s cell_type -s experiment_code

Here is what the selection menu would look like in this case

Showing the three fields: tissue, cell_type, experiment_code

A full view of the page after results are returned

screenshot2