/Het2Gene

Primary LanguagePythonApache License 2.0Apache-2.0

Het2Gene: a phenotype-driven model for gene prioritization by Heterogeneous graph embedding

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.

Folder description

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 graphs, node coding method, training method, test code, etc

PreSelect Random Gene

Randomly select 999 genes from your own genetic range, and add pathogenic genes to form a gene set of size 1000. In this way, create ten gene sets and calculate the ranking of pathogenic genes in the corresponding gene set each time. Take the median of the ten gene sets as the final ranking

Usage

In folder ./models/prioritize/Het2Gene/ ,run the following command 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

Web Resources for Comparison Methods

CADA:https://cada.gene-talk.de/webservice

AMELIE:https://amelie.stanford.edu

Phen2Gene:https://phen2gene.wglab.org

GADO:https://genenetwork.nl/gado

PhenoApt:[https://www.phenoapt.org/]

License

See the LICENSE file for license rights and limitations