Network analysis

This notebook shows examples of how to perform network analysis on TF-COMB co-occurrence results.

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Load CombObj containing rules

First, we will load the data as created in the “select rules”-notebook.

import tfcomb
from tfcomb import CombObj
C = CombObj().from_pickle("../data/GM12878_selected.pkl")

C

<CombObj: 83705 TFBS (86 unique names) | Market basket analysis: 282 rules>

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Visualize network

We can now use the CombObj to directly visualize the network. The first time the function is run, the .network attribute will be created within the CombObj:

C.plot_network()

WARNING: The .network attribute is not set yet - running build_network().
INFO: Finished! The network is found within <CombObj>.network.

You can also change the colors of the nodes and edges via edge_cmap and node_cmap:

C.plot_network(edge_cmap="Blues", node_cmap="viridis")

By default, the nodes are colored by count of binding sites, and edges are colored by the ‘cosine’ score. However, this is easily adjusted via the ‘color__by’ parameters:

C.plot_network(color_node_by="TF1", color_edge_by="zscore")

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Different network layouts

It is possible to change the network layout via the “engine” parameter of plot_network:

C.plot_network(engine="circo", legend_size=70)

C.plot_network(engine="fdp", size_node_by="TF1_count")

C.plot_network(engine="dot")

#The available layouts are:
import graphviz; graphviz.ENGINES

{'circo', 'dot', 'fdp', 'neato', 'osage', 'patchwork', 'sfdp', 'twopi'}

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Cluster network nodes

It is often of interest to partition individual TFs into groups of highly connected TFs (highly co-occurring). This is possible using the cluster_network() function. Below we show different settings for performing TF clustering based on co-occurring networks.

Using louvain clustering (default)

The default partition_network uses louvain clustering of the python-louvain package (https://github.com/taynaud/python-louvain):

C.cluster_network()

INFO: Added 'cluster' attribute to the network attributes

The partition was added to the network attributes as well as to the internal .TF_table of the CombObj:

C.TF_table

	TF1	TF1_count	TF2	TF2_count	cluster
CTCF	CTCF	2432	CTCF	2432	1
RAD21	RAD21	2241	RAD21	2241	1
SMC3	SMC3	1638	SMC3	1638	1
IKZF1	IKZF1	2922	IKZF1	2922	2
IKZF2	IKZF2	2324	IKZF2	2324	2
...	...	...	...	...	...
ELK1	ELK1	237	ELK1	237	9
CEBPB	CEBPB	273	CEBPB	273	6
RCOR1	RCOR1	415	RCOR1	415	6
TCF7	TCF7	512	TCF7	512	6
CBX5	CBX5	887	CBX5	887	6

86 rows × 5 columns

This enables plotting of the partition using plot_network:

C.plot_network(color_node_by="cluster")

Louvain clustering with weights

Louvain clustering can also work with weighted edges, as seen here for cosine:

C.cluster_network(weight="cosine")

INFO: Added 'cluster' attribute to the network attributes

C.plot_network(color_node_by="cluster")

#It is also possible to plot the network without label by overwriting labels via node_attributes:
C.plot_network(color_node_by="cluster", legend_size=0, node_attributes={"label": ""})

Using block model

The third option is using the graph-tool ‘minimize.minimize_blockmodel_dl’-function (https://graph-tool.skewed.de/static/doc/inference.html#graph_tool.inference.minimize.minimize_blockmodel_dl):

C.cluster_network(method="blockmodel")

C.plot_network(color_node_by="cluster")