Differential interacting nodes

1. network_centrality 

After the above steps, we can obtain information about the genomic features of a given dataset. For differential network analysis, another piece of information we need is the topological feature of the network. Therefore, we next find the centrality of nodes in Hi-C networks.

dna.network_centrality(permutation = 100)

Arguments 

Optional arguments:

permutation: Number of random permurtations for assigning a p-val to each node’s centrality. The default is 100.

Data path 

output data path: The centrality for each node in the networks are deposited in /out_data_folder/hic_data/resolution/hic_community_data.

Note

The following analysis requires completing the above steps with the same parameters for two cohorts!

2. network_sigNodes 

With the results of the above steps for two cohorts prepared, we can now start the differential network analysis! Firstly, nodes with differences in genomic and topological features between the networks of the two cohorts are identified.

dna.network_sigNodes(cohort1, cohort2, chromosome, pval_cutoff = 0.05, permutation = 100, fig_dpi = 300)

Arguments 

Required arguments:

cohort1: Cohort 1 with all the above steps completed.
cohort2: Cohort 2 with all the above steps completed.
chromosome: The chromosome users want to investigate. It should have a format like [‘chr1’, ‘chr2’, …] or ‘whole_genome’.

Optional arguments:

pval_cutoff: P-value for screen for significant nodes. The default is 0.05.
permutation: Number of sampling when doing permutation test in enrichment_permutation. The default is 100.
fig_dpi: Figure resolution in dots per inch. The default is 300.

3. network_comparison 

Then, for the significantly changed nodes betwenn cohort 1 and cohort 2, we summarize both their commons and differences.

dna.network_comparison(cohort1, cohort2, chromosome, pval_cutoff = 0.05)

Arguments 

Required arguments:

cohort1: Cohort 1 with all the above steps completed.
cohort2: Cohort 2 with all the above steps completed.
chromosome: The chromosome users want to investigate. It should have a format like [‘chr1’, ‘chr2’, …] or ‘whole_genome’.

Optional arguments:

pval_cutoff: P-value for screen for significant nodes. It should be consistent with the P-value set in network_sigNodes. The default is 0.05.

Data path 

output data path: The selected nodes and other information are exported to ‘/out_data_folder/differential_network_analysis’.