Example

Here is an example query that uses the query_node_pair management command:

# Obesity to FTO query
python manage.py query_node_pair \
  --source Disease DOID:9970 \
  --target Gene 79068

The following table is returned:

metapath_id	path_count	dwpc	p_value	source_degree	target_degree	n_dwpcs	n_nonzero_dwpcs	nonzero_mean	nonzero_sd
DaGpBPpG	435	2.81	0.000%	373	32	29,000	29,000	2.10	0.12
DaGeAeG	6,204	2.00	0.000%	373	28	53,000	53,000	1.87	0.02
DpSpDaG	25	4.43	0.013%	17	6	101,000	100,994	2.44	0.45
DrDaG	3	5.14	0.244%	5	6	181,800	32,414	3.92	0.51
DlAlDaG	42	3.74	0.701%	33	6	20,200	20,200	2.73	0.38
DpSpDdG	5	3.38	2.744%	17	2	1,065,000	1,009,309	1.94	0.67
DrDuGiG	1	1.13	3.276%	5	2	2,885,400	124,913	2.00	1.16
DdGuDdG	4	3.84	4.334%	45	2	213,000	142,687	2.94	0.56
DdGeAeG	739	1.57	6.118%	45	28	53,000	53,000	1.49	0.05
DdGcGr>G	1	2.95	6.900%	45	7	69,600	6,278	3.70	1.00
DaG	1	5.27	8.889%	373	6	20,200	3,591	5.27	0.00
DlAdGcG	11	1.96	9.726%	33	6	115,400	105,130	1.15	0.63

Note that we should also look at IRX3 and IRX5 with obesity, as it appears that these genes may actually be causal for the FTO-SNPs-to-obesity GWAS association.

dhimmel commented 5 years ago

As of #20 being merged https://github.com/greenelab/hetmech-backend/commit/d7f6edf3f6156aa9efd41ac34534cdf1e5d8e313, we now have an API endpoint that returns a similar table. So will touch base with @dongbohu on what to do about this management command.

greenelab / connectivity-search-backend

query_node_pair management command #9

Example