Enrichment and network analysis

1. diegs_enrichment

In this section, differentially interacting and expressed genes (DIEGs) need to be identified from the selected significant nodes. Gene enrichment analysis should be performed based on these DIEGs.

dna.diegs_enrichment(cohort1, cohort2, method = 'relativeRatio', pval_cutoff = 0.05, fig_dpi = 300)

Arguments

Required arguments:

  • cohort1: Cohort 1 with all the above steps completed.

  • cohort2: Cohort 2 with all the above steps completed.

  • method: Methods for selecting differentially expressed genes (‘relativeRatio’: relative ratio; ‘foldChange’: fold change). The default is ‘relativeRatio’.

Optional arguments:

  • pval_cutoff: P-value for screen for significant nodes in network_sigNodes. The default is 0.05.

  • fig_dpi: Figure resolution in dots per inch. The default is 300.

Attention

After obtaining DIEGs, users should go to DAVID website for gene enrichment analysis based on these DIEGs if they want to get figures of enrichment analysis.

2. diegs_subnetwork

Then, subnetworks based on the selected DIEGs and genomic information within each node can be obtained.

dna.diegs_subnetwork(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: Methods for selecting differentially expressed genes (‘relativeRatio’: relative ratio; ‘foldChange’: fold change). The default is ‘relativeRatio’.

Optional arguments:

  • pval_cutoff: P-value for screen for significant nodes in network_sigNodes. The default is 0.05.

Data path

output data path: The subnetworks, genomic features (histone markers of enhancer/repressor, gene expression) as well as gene names within each node are exported to /out_data_folder/differential_network_analysis.