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

# *First compiled: September 16, 2017.*

# # Graph Abstraction for Deep Learning

# The graph of neighborhood relations of data points in pixel space is useless if computed with a simple distance metric (euclidian, cosine, correlation-based, etc.). Deep Learning generates a feature space in which data points are positioned according to biological similarity and hence generates a distance metric that is much more valuable. Here, we demonstrate how graph abstraction is applied in this case.
# 
# [Eulenberg, Köhler, *et al.*, Nat. Commun. (2017)](https://doi.org/10.1101/081364) showed that continuous biological processes can be reconstructed using deep learning. Their results can be reproduced from https://github.com/theislab/deepflow. A video on how the deep learning based feature space organizes data according to biological similarity is here: https://youtu.be/eyWcHIiCazE. We'll use their analysis as a starting point.

# In[1]:


import numpy as np
import pandas as pd
import scipy as sp
import scanpy.api as sc

sc.settings.set_figure_params(dpi=70)
sc.settings.verbosity = 1
sc.logging.print_version_and_date()


# Download the features of [Eulenberg, Köhler, *et al.*, Nat. Commun. (2017)](https://doi.org/10.1101/081364) [here](http://falexwolf.de/scanpy_usage/170529_images/).

# In[6]:


adata = sc.read('./data/G1SG2/stain_G1SG2_features.csv', cache=True)


# In[36]:


annotation = pd.read_csv('./data/G1SG2/stain_G1SG2.lst', delimiter='\t', header=None)


# In[7]:


annotation.head()


# In[43]:


adata.smp['cell_cycle_stages'] = annotation[2].str.lstrip('../images').str.split('/').str[0]
adata.smp['DNA_content'] = annotation[1]
# just a random cell (the first) within G1
adata.add['iroot'] = np.arange(adata.n_smps)[adata.smp['cell_cycle_stages'] == 'G1'][0]
adata.add['highlights'] = [adata.add['iroot']]


# In[11]:


sc.tl.tsne(adata)
sc.write('deepflow', adata)


# In[3]:


adata = sc.read('deepflow')
axs = sc.pl.tsne(adata, legend_loc='on data', 
                 title='Cell cycle stages.',
                 legend_fontsize=16, legend_fontweight='bold',
                 color='cell_cycle_stages')


# In[45]:


sc.tl.aga(adata, resolution=0.5, recompute_louvain=True)
sc.write('deepflow', adata)


# In[20]:


adata = sc.read('deepflow')
axs = sc.pl.aga(adata, palette=sc.pl.palettes.godsnot_64, color='aga_groups', groups_graph='aga_groups', 
                node_size_power=0.2, node_size_scale=2,
          legend_fontsize=16, legend_fontweight='bold',
          title='Louvain partitions of $\mathcal{G}$.',
          title_graph='Abstracted graph $\mathcal{G}^*$.',
          root=2, layout='rt', save=True)


# In[18]:


asso_groups, asso_matrix = sc.utils.compute_association_matrix_of_groups(adata, 'aga_groups', 'cell_cycle_stages')
asso_colors = sc.utils.get_associated_colors_of_groups(sc.pl.palettes.vega_20_scanpy, asso_matrix)


# In[21]:


axs = sc.pl.aga(adata, color='cell_cycle_stages', node_size_power=0.2, node_size_scale=2,
                title_graph='Abstracted graph $\mathcal{G}^*$ colored by cell cycle.',
                legend_fontsize=16, legend_fontweight='bold', 
                color_graph=asso_colors,
                groups_graph=asso_groups, title='Cell cycle stages.', 
                root=2, layout='rt', frameon=True, show=True, save='_cell_cycle_stages')


# Study the progression along the single-cell graph.

# In[49]:


adata = sc.read('deepflow')
axs = sc.pl.tsne(adata, legend_loc='on data', legend_fontsize=16, legend_fontweight='bold',
                 color=['aga_pseudotime', 'DNA_content'], color_map='viridis')


# The absracted graph tells us to consider a path along nodes 2, 3 and 1.

# In[3]:


adata = sc.read('deepflow')
ax = sc.pl.aga_path(adata, nodes=[2, 3, 1], keys=['DNA_content'], n_avg=50,
                    as_heatmap=False, show_nodes_twin=False, save=True, show=True)


# If we ignore the abstracted graph and consider a path that includes the cluster of damaged cells, we obtain a non-smoooth increase.

# In[4]:


ax = sc.pl.aga_path(adata, nodes=[2, 0, 3, 1], keys=['DNA_content'], n_avg=50,
                    as_heatmap=False, show_nodes_twin=False, save='_kink', show=True)


# # Supplemental Note: Analyzing all 7 cell cycle phases

# Let us now analyze all 7 cell cycle phases.

# In[3]:


adata = sc.read('./data/all_phases/features/acts.csv', cache=True)
annotation = pd.read_csv('./data/all_phases/features/va.lst', delimiter='\t', header=None)


# In[6]:


print(annotation.head())


# In[7]:


adata.smp['cell_cycle_stages'] = annotation[2].str.lstrip('../images').str.split('/').str[0]


# In[8]:


sc.tl.tsne(adata)
sc.write('deepflow_all', adata)


# In[11]:


from matplotlib import rcParams
rcParams['figure.figsize'] = (8, 4)


# In[12]:


adata = sc.read('deepflow_all')
axs = sc.pl.tsne(adata, legend_loc='on data', legend_fontsize=16, legend_fontweight='bold',
                 color='cell_cycle_stages')


# The cluster of damaged cells is detected, but the mitotic phases not.

# In[14]:


sc.tl.aga(adata, resolution=0.5, recompute_louvain=True)
sc.pl.aga(adata, palette=sc.pl.palettes.godsnot_64, color='aga_groups', groups_graph='aga_groups', 
          legend_fontsize=16, legend_fontweight='bold',
          title_graph='abstracted graph',
          root=2, layout='rt', frameon=True, save=True, show=True)


# Increasing the resolution still yields a consistent graph, but the mitotic phases are still not detected.

# In[19]:


sc.tl.aga(adata, resolution=1.0, recompute_louvain=True)
sc.pl.aga(adata, palette=sc.pl.palettes.godsnot_64, color='aga_groups', groups_graph='aga_groups', 
          legend_fontsize=16, legend_fontweight='bold',
          title_graph='abstracted graph',
          root=2, layout='rt', frameon=True, save=True, show=True)


# In[23]:


sc.pl.reset_rcParams()