#!/usr/bin/env python
# coding: utf-8

# # Single Cell RNAseq Visualization Example
# This notebook is an example of how to use the Jupyter interactive widget, [Clustergrammer-widget](https://github.com/MaayanLab/clustergrammer-widget), to visualize single cell RNAseq data from the publication 
# 
# "Single-cell analysis of mixed-lineage states leading to a binary cell fate choice"
# Olsson A, Venkatasubramanian M, et.al. 
# Nature August 9th 2016
# [link-to-paper](http://www.nature.com/nature/journal/v537/n7622/full/nature19348.html)
# 
# The figure below is a slightly adapted version of [Figure 2A](http://www.nature.com/nature/journal/v537/n7622/fig_tab/nature19348_F2.html) made fromt the available source data Excel file. 
# 

# In[4]:


# import the widget
from clustergrammer_widget import *
from copy import deepcopy

# load data into new network instance and cluster
net = deepcopy(Network())
net.load_file('single_cell_RNAseq_Aronow.txt')
net.make_clust()

# view the results as a widget
clustergrammer_widget(network = net.export_net_json())


# The above heatmap shows single-cell-clusters (see publication for details) as columns and differentially expressed genes as rows. Clustergrammer uses hierarchical clustering to identify clusters of cells and genes. We can see that single-cell-clusters largely cluster according to their categories (e.g. Gfi1-high). 

# In[ ]: