.. role:: pyth(code) :language: python
Genespector is a Dash/Plotly app for interactive visualisation of large scRNA-Seq datasets analysed using the scanpy/AnnData packages. Genespector allows responsive plotting of datasets up to 100,000 cells. It remains stable at least up to 250,000 cells but there is a visible delay in plotting.
Genespector is based on the Dash/Plotly framework: https://dash.plot.ly/
Python > 3.4 and pip are required. To install the package:
.. code-block:: text
git clone https://github.com/Iwo-K/genespector.. code-block:: text
pip install -r ./genespector/requirements.txt.. code-block:: text
pip install ./genespector/A simple example of scRNA-Seq dataset displayed with Genespector can be found here: http://128.232.224.252/example/
For anyone interested in haematopoiesis, the Gottgens lab is hosting several Genespector apps displaying following scRNA-Seq datasets:
Genespector simply requires an AnnData object to run. Most adata object should work straight away, but you may want to select features to be shown in the app by removing unnecessary information from the AnnData object. An example jupyter notebook with data preprocessing can be found here: https://nbviewer.jupyter.org/github/Iwo-K/genespector/blob/master/example_genespector.ipynb
The adata objects are imported as follows:
adata.X (or :pyth:adata.raw.X, depending on the :pyth:use_raw argument)adata.var.index (or :pyth:adata.raw.var.index, depending on the :pyth:use_raw argument) and become available from the Color points menu.adata.obs slot are imported and become available from the Color points menu.adata.obs slot are imported and become available from the Color points menu. Plots are rendered in layers according to the selected value in Split data by menu. Note: only categorical columns are imported, columns containing strings are ignored. To change the column type use the method: :pyth:.astype('category')adata.obs slot and become available in the Choose the X and Y axis of the plot menuadata.uns slot, as long as the names are matching. For instance :pyth:adata.obs['louvain'] matches the entry :pyth:adata.uns['louvain_colors'].When dealing with large datasets, to save memory try using sparse matrices with normalised/log-transformed data and avoid using full matrices containing scaled data.
In python, where adata is the AnnData object of choice:
.. code-block:: text
import genespector as gp
gp.make_app(adata)Example output:
.. code-block:: text
.........
.........
* Serving Flask app "app" (lazy loading)
.........
* Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)Copy the address to your browser or click the link.
make_app() accepts the following arguments:
adata - an AnnData objectuse_raw - logical, whether .raw.X slot should be used instead of .X (default: False)make_var_unique - logical, whether .var.index should be converted to unique values using the var_names_make_unique() function from scanpymain_title and :pyth:subtitle - string, titles displayed above the Applayout - function, creating a dictionary which controls the website layout, needs to contain necessary elements for the app. For an example see the layout.py fileserver - logical or name of the server used for deploying the appurl_base_pathname - string, specifies the url address for the app (default: \'/\')assets_folder - string, path to the folder containing static files, e.g. the .css filerun - logical, whether the app should be run (if True) or return a dash.Dash (if False) instance of Flask app, useful for deploying the app on a server.Interface is quite simple. The plot area allows zooming, selection of points and exporting to png files (in this cases removing axes may be useful).
Specific subsets (plotted as layers) of the data can be selected by clicking the legend (double-click to isolate a specific subset)
Subsets are specified from the dropdown menu. To colour-code by subsets select 'categorical data' from the 'Colour points' menu.
Gene expression values can be chosen from the Colour points menu.
The app can also be embedded within an existing website using