a phenotype-driven model for gene prioritization.The heterogeneous graph embedding algorithm is used to learn the embedding representation of heterogeneous graph nodes, and the score of candidate causal genes is calculated according to the embedding, so as to prioritize.
The data used in the training model are placed in ./data/ .The edge relation, weight and trained embedding information have been encapsulated by pickle module. In addition, the test data used are also included. In ./models/, it includes the method of constructing weighted and unweighted graphs, node coding method, training method, test code, etc
In folder ./models/prioritize/Het2Gene/ ,run the following command(The same goes for weighted graphs) can use Het2Gene:
python het2gene.py --hps [parameter] --out_dir [-options][parameter] --topn [-options][parameter]
hps: Abnormal phenotype set from patients，required
out_dir: Directory for outputting results，default RankResult
topn: Output candidate genes in the top n,default 1000
An example command:
python het2gene.py --hps HP:0000573,HP:0001102,HP:0003115,HP:0001681,HP:0008067,HP:0004417 --out_dir myResult --topn 5
Output Example：
Rank EntrezID Symbol Score
1 368 ABCC6 11.56
2 64132 XYLT2 1.983
3 5167 ENPP1 -0.815
4 64131 XYLT1 -0.936
5 4000 LMNA -3.271
CADA:https://cada.gene-talk.de/webservice
AMELIE:https://amelie.stanford.edu
Phen2Gene:https://phen2gene.wglab.org
GADO:https://genenetwork.nl/gado
See the LICENSE file for license rights and limitations