See Clustergrammer2 for Latest Widget¶
Clustergrammer2 is the new WebGL widget that is being developed to handle larger datasets (e.g. scRNA-seq data). This widget will be the focus of future development and feature additions. The original Clustergrammer-Widget will still be maintained, but users are encouraged to migrate to Clustergrammer2.
Jupyter notebooks are ideal for generating reproducible workflows and analysis. They are also the best way to share Clustergrammer’s interactive visualizations while providing context, analysis, and the underlying data to enable reproducibility (see Sharing with nbviewer). The Clustergrammer Widget enables users to easily produce interactive visualizations within a Jupyter notebook that can be shared with collaborators (using nbviewer). Clustergrammer-Widget can be used to visualize a matrix of data from a file or from a Pandas DataFrame (see Matrix Formats and Input/Output for more information). The library is free and open-source and can be found on GitHub.
Clustergrammer has been applied to a wide variety of biological and non-biological data. See the Jupyter notebook examples below and Case Studies and Tutorials for more information:
Jupyter Widget Dependencies¶
Clustergrammer-Widget works with Python 2 and 3.
To use the Clustergrammer-Widget users need to install: Python, Jupyter notebook, the widget dependencies (see Jupyter Widget Dependencies), and ipywidgets version 6.0.0 (to save the notebook with widgets, version 6.0.0 is recommended). Users can install Anaconda, a Python distribution that includes the Jupyter notebook as well as other scientific computing libraries, to easily obtain the necessary dependencies (except ipywidgets version 6.0.0). The
clustergrammer_widget can the be installed (with pip) and enabled using the following commands:
pip install --upgrade clustergrammer_widget jupyter nbextension enable --py --sys-prefix widgetsnbextension jupyter nbextension enable --py --sys-prefix clustergrammer_widget
Clustergrammer-Widget Workflow Example¶
The Jupyter notebook Running_clustergrammer_widget.ipynb (which is rendered using nbviewer) shows how to visualize: a matrix from a file and a Pandas DataFrame. The following examples are taken from this notebook.
Here we are visualizing a matrix of data from a file (e.g.
rc_two_cats.txt). We start by instantiating the
net, and passing it the widget class, clustergrammer_widget as an argument. The net object is used to load data, filter, normalize, cluster, and render the widget. For more information about the
Network class, refer to the Clustergrammer-PY API.
Load Data from File
# make imports and instantiate a Network instance with the widget class as an argument from clustergrammer_widget import * net = Network(clustergrammer_widget) # load matrix file net.load_file('rc_two_cats.txt') # cluster using default parameters net.cluster() # make interactive widget net.widget()
General Purpose DataFrame Viewer
Clustergrammer-Widget can also be used as a general purpose Pandas DataFrame viewer. Below is an example of how to visualize a Pandas DataFrame,
df, by loading it into the
# load DataFrame net.load_df(df) # cluster using default parameters net.cluster() # make interactive widget net.widget()
Loading new data into
net removes any old data, which allows the
net object to be easily reused within the same notebook.
Filtering, Downsampling, and Normalizing
net object can also be used to filter and normalize your data before visualizing (note that filtering and normalization are permanent and irreversible). The example below performs Z-score normalization on the columns, filters to keep the top 200 rows based on their absolute value sum, calculates clustering, and finally renders the widget:
# Z-score normalize columns net.normalize(axis='col', norm_type='zscore', keep_orig=True) # filter for the top 200 rows based on their absolute value sum net.filter_N_top('row', 200, 'sum') # cluster using default parameters net.cluster() # make interactive widget net.widget()
Two-way Widget Communication
df_widget method below for an example:
# After modifying the visualization (e.g. dendrogram cropping) we can export the # modified matrix to the back end using the df_widget method df_new = net.df_widget()
For more information about the
Network object and additional options refer to the Clustergrammer-PY API.