Identify enriched gene sets associated with differentially spliced PTMs

As is commonly done for exon-centric analyses, we have provided the ability to perform gene set enrichment analysis for gene associated with spliced PTMs, using the EnrichR API from the gseapy module. By default, we include gene ontology terms, KEGG pathways, and Reactome pathways, but you can also provide your own gene sets listed in EnrichR.

from ptm_pose import helpers
from ptm_pose.analyze import annotations
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")


# Load spliced ptm and altered flank data
spliced_ptms, altered_flanks = helpers.load_example_data(spliced_ptms = True, altered_flanks=True)

Use the below function, we can identify enriched gene sets associated with spliced ptms, altered flanks, or both. We can also specify the gene sets to assess, alpha value for significance, the minimum change in PSI value to consider, and whether to return only significant gene sets.

genesets = annotations.gene_set_enrichment(spliced_ptms, altered_flanks, alpha = 0.05, min_dPSI = 0.1, gene_sets = ['GO_Biological_Process_2023','Reactome_2022'], return_sig_only = True)
genesets.head()

334 PTMs removed due to insignificant splice event (p < 0.05, dpsi >= 0.2): (69.01%)
Final number of PTMs to be assessed: 150
77 PTMs removed due to insignificant splice event (p < 0.05, dpsi >= 0.2): (51.33%)
Final number of PTMs to be assessed: 73

	Gene_set	Term	Overlap	P-value	Adjusted P-value	Old P-value	Old Adjusted P-value	Odds Ratio	Combined Score	Genes	Type	Genes with Differentially Included PTMs only	Genes with PTM with Altered Flanking Sequence only	Genes with Both
0	GO_Biological_Process_2023	Axon Development (GO:0061564)	5/99	0.000002	0.001424	0	0	27.803751	361.881752	APLP2;DOCK7;BAIAP2;FGFR2;MARK2	Differentially Included + Altered Flanking Seq...	FGFR2	BAIAP2	APLP2;MARK2;DOCK7
1	GO_Biological_Process_2023	Cell Morphogenesis Involved In Neuron Differen...	4/77	0.000022	0.007049	0	0	27.936776	299.153677	APLP2;DOCK7;BAIAP2;FGFR2	Differentially Included + Altered Flanking Seq...	FGFR2	BAIAP2	APLP2;DOCK7
2	GO_Biological_Process_2023	Integrin-Mediated Signaling Pathway (GO:0007229)	4/85	0.000033	0.007049	0	0	25.167458	259.671997	CEACAM1;ADAM15;ITGA6;CD47	Differentially Included + Altered Flanking Seq...	CD47;ITGA6;ADAM15		CEACAM1
3	GO_Biological_Process_2023	Axonogenesis (GO:0007409)	5/188	0.000050	0.008020	0	0	14.217716	140.769042	ENAH;APLP2;DOCK7;BAIAP2;FGFR2	Differentially Included + Altered Flanking Seq...	FGFR2	BAIAP2	APLP2;DOCK7;ENAH
4	GO_Biological_Process_2023	Regulation Of Cell-Cell Adhesion (GO:0022407)	3/43	0.000108	0.013375	0	0	37.344375	341.223477	CEACAM1;CD47;NF2	Differentially Included + Altered Flanking Seq...	CD47	NF2	CEACAM1

You can then plot the enriched gene sets, including the proportion of genes associated with differentially included PTMs and those with altered flanking sequences. Here, let’s restrict to looking at the top 5 enriched gene sets:

annotations.plot_EnrichR_pies(genesets, top_terms = 5)
plt.tight_layout()
plt.show()