GNNHap

Graph Neural Network based Haplotype Prioritization for inbred mouse.

Citation

Zhuoqing Fang, Gary Peltz, An Automated Multi-Modal Graph-Based Pipeline for Mouse Genetic Discovery, Bioinformatics, 2022;, btac356, https://doi.org/10.1093/bioinformatics/btac356

Installation

• numpy
• pandas
• Pytorch
• Pytorch Geometric
• torchmetrics
• sentencetransformers
• pubtator
• spacy
  • en_ner_bionlp13cg_md

  pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.3.0/en_ner_bionlp13cg_md-0.3.0.tar.gz

Build Graph and Train Model

if you would like to use our pretrain model, go to the prediction step or web interface step.

1. Download files

see Download.md

2. Build Knowlege graph

snakemake -s graph/pubmed_graph_parallel.smk -p -j 32

This step generate the graph file: human_gene_mesh_hetero_nx.gpkl

3. Train your GNN and LinkPredictor

# GNN encoder + Linkpredictor
## hidden_size 50 fits to a 24G GPU card
## hidden_size 64 fits to a 32G GPU card
python GNNHap/train_gnn.py --batch_size 10000 \
                    --hidden_size 64 \
                    --num_epochs 5 \
                    --mesh_embed ${WKDIR}/human_gene_unirep.embeb.csv \
                    --gene_embed ${WKDIR}/mesh.sentencetransformer.embed.csv \
                    --gene_mesh_graph ${WKDIR}/human_gene_mesh_hetero_nx.gpkl

# optional, we won't use it in later steps
# train Link predictor only
python GNNHap/train_mlp.py --batch_size 10000 \
                    --hidden_size 64 \
                    --num_epochs 5 \
                    --mesh_embed ${WKDIR}/human_gene_unirep.embeb.csv \
                    --gene_embed ${WKDIR}/mesh.sentencetransformer.embed.csv \
                    --gene_mesh_graph ${WKDIR}/human_gene_mesh_hetero_nx.gpkl

Prediction

Download the GNNHap_Bundle, which contained necessary files

Replace the graph, and your own trained model if needed.

1. Simple usage case

input only need a text file with at least two columns (gene_symbol, MeSH_ID)

example.txt (first two column are required):

GeneName        MeSH    p_val   avg_log2FC      pct.1   pct.2   p_val_adj
ITGA1   D007694 5.15054414391506e-228   2.01032118407705        1       0.108   1.2017764650997e-223
ITGAE   D007694 3.27480562769949e-160   1.89432929036404        1       0.242   7.64110397111121e-156
ACP5    D007694 5.98597006687121e-154   2.11626850602703        0.851   0.147   1.39670639570306e-149
CSF1    D007694 7.41183069402442e-146   2.14699909313243        0.744   0.102   1.72940245583672e-141
LGALS3  D007694 8.92764452941871e-123   1.6742751831308 0.818   0.182   2.08308729804927e-118

run prediction

python GNNHap/predict_simple.py 
            --bundle /path/to/GNNHap_Bundle 
            --input example.txt 
            --output example.gnn.txt
            --species human # or mouse

2. Run Full GNNHap pepeline (Combined with Haplomap, or HBCGM)

see the full guide to get Haplomap (a.k.a HBCGM) results

An snakemake pipeline in the example folder shows the full commands.

snakemake -s gnnhap.smk --configfile config.yaml -j 12 -p

For HBCGM results,

Case 1: single result file

python GNNHap/predict.py --bundle /path/to/GNNHap_Bundle  \
                  --hbcgm_result_dir ${RESULTS}  \ # parent path to *results.txt
                  --mesh_terms D018919,D009389,D043924,D003315  \ # separate each term with comma
                  --species mouse \
                  --num_cpus 12
            

Case 2: multiple result files NOTE 1: the ${HBCGM_RESULT_DIR} folder looks like this:

|-RESULTS
|--- MPD_000.results.txt
|--- MPD_001.results.txt
...

NOTE 2: provide a json file for --mesh_terms if multiple result file are predicted. the json file records the each files's MeSH term IDs.

python GNNHap/predict.py --bundle /path/to/GNNHap_Bundle  \
                  --hbcgm_result_dir ${RESULTS} \ # parent path to *results.txt
                  --mesh_terms mpd2mesh.json  \
                  --species mouse
                  --num_cpus 12
            

Run this program with a web interface ?

set up correct path for all required files and programs.

run

cd webapp
python app.py

you need to update

• the config.json file for the web server
• the config.yaml for gnnhap.smk to run haplomap

A Snapshot