This repo contains an implementation of pathway2vec, a software package consisting of six representational learning-based modules used to automatically generate features for the downstream pathway inference task. Specifically, pathway2vec builds a three-layered network composed of compounds, enzymes, and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. The algorithms in pathway2vec were benchmarked based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. Remarkably, in the pathway prediction task, results indicate that it is possible to leverage embeddings to improve pathway prediction outcomes.
The codebase is tested to work under Python 3.5. To install the necessary requirements, run the following commands:
pip install -r requirements.txt
Basically, pathway2vec requires the following distribution and packages:
- Anaconda
- tensorflow (== 1.10)
- NumPy (>= 1.15)
- scikit-learn (>= 0.20)
- pandas (>= 0.23)
- NetworkX (>= 2.2)
- gensim (== 3.7)
- scipy (==1.2)
Please download the following preprocessed files from Zenodo.
- The link contains following preprocessed graphs:
- "ec_graph.pkl": the EC graph, which is a set of ECs with interactions.
- "compound_graph.pkl": the compound graph, which is a set of compounds with interactions.
- "pathway_graph.pkl": the pathway graph, which is a set of pathways with interactions.
- "ec2compound.pkl": mapping file from the EC layer onto the compound layer.
- "compound2pathway.pkl": mapping file from the compound layer onto the pathway layer.
- "ec2pathway.pkl": mapping file from the EC layer onto the pathway layer.
- We also provided pretrained models and samples for testing:
- "hin.pkl": a sample of heterogeneous information network, which is used to generate walks. Based on your tests, you need to generate a heterogeneous information network during preprocessing step. You many use "hin.pkl" to peek into the structure.
- "X_hin.txt": a sample of generated walks, which is used to learn embeddings. Each line encode a walk rooted at a node beginning of the line. Based on your tests, you need to generate walks during random walks step. You many use "hin.pkl" as a test sample.
- "pathway2vec_embeddings.npz": a sample of embeddings (nodes, dimension size). Based on your tests, you need to learn embeddings walks during training step. You many use "hin.pkl" and "X_hin.txt" as test samples.
Run the following commands to clone the repository to an appropriate location:
git clone https://github.com/hallamlab/pathway2vec.git
For all experiments, navigate to src folder then run the commands of your choice. For example, to display *
pathway2vec*'s running options use: python main.py --help. It should be self-contained.
To preprocess graphs, we provide few examples. For all examples: --hin-file corresponds to the desired generated file name, ending with .pkl.
Please do not use the sample "hin.pkl" during this step, and change the name of the generated hin file or store the provided "hin.pkl" in a different folder to avoid conflict.
To preprocess three layer graph all connected, execute the following command:
python main.py --preprocess-dataset --first-graph-name "ec_graph.pkl" --second-graph-name "compound_graph.pkl" --third-graph-name "pathway_graph.pkl" --first-mapping-file-name "ec2compound.pkl" --second-mapping-file-name "compound2pathway.pkl" --hin-file "[Name of the hin file].pkl" --ospath "[path to all object files]" --logpath "[path to the log directory]" --num-jobs 2
To preprocess three layer graph excluding the connection of the first graph, execute the following command:
python main.py --preprocess-dataset --first-graph-not-connected --first-graph-name "ec_graph.pkl" --second-graph-name "compound_graph.pkl" --third-graph-name "pathway_graph.pkl" --first-mapping-file-name "ec2compound.pkl" --second-mapping-file-name "compound2pathway.pkl" --hin-file "[Name of the hin file].pkl" --ospath "[path to all object files]" --logpath "[path to the log directory]" --num-jobs 2
where --first-graph-not-connected enables exclusion of connection among the nodes in the first layer.
To preprocess three layer graph while removing isolates, execute the following command:
python main.py --preprocess-dataset --remove-isolates --first-graph-name "ec_graph.pkl" --second-graph-name "compound_graph.pkl" --third-graph-name "pathway_graph.pkl" --first-mapping-file-name "ec2compound.pkl" --second-mapping-file-name "compound2pathway.pkl" --hin-file "[Name of the hin file].pkl" --ospath "[path to all object files]" --logpath "[path to the log directory]" --num-jobs 2
where --remove-isolates enables the isolation of nodes less than 2 connectivity.
To preprocess two layers graph, execute the following command:
python main.py --preprocess-dataset --exclude-third-graph --first-graph-name "ec_graph.pkl" --second-graph-name "pathway_graph.pkl" --first-mapping-file-name "ec2pathway.pkl" --hin-file "[Name of the hin file].pkl" --ospath "[path to all object files]" --logpath "[path to the log directory]" --num-jobs 2
where --exclude-third-graph enables the including two layers only.
To generate walks, we provide few examples.
Description about arguments in all of given examples: --burn-in-phase is the burn in phase time to compute transition probability prior to generating walks, --burn-in-input-size is subsampling size of the number of walks and length for burn in phase. These two arguments are set by defualt to 1 and 0.5. The arguments --walk-length corresponds length of walk per source while --num-walks is number of generated walks per source node. --file-name corresponds to the desired graph file name and generated walks, excluding any EXTENSION (e.g. "hin"). Two files will be resulted one will have .txt suffix and X_ prefix while the graph whill have .pkl extension.
Please do not use the sample "X_hin.txt" during this step, and change the name of the generated walks or store the provided "X_hin.txt" in a different folder to avoid conflict.
To generate node2vec random walks, execute the following command:
python main.py --extract-instance --burn-in-phase 1 --burn-in-input-size 0.3 --q 0.5 --walk-length 10 --num-walks 5 --hin-file "[Name of the hin file].pkl" --file-name "[Name of the file without extension]" --ospath "[path to the hin file]" --dspath "[path where random walks would be stored]" --logpath "[path to the log directory]" --num-jobs 2
where --q represents in-out parameter that allows the search to differentiate between "inward" and "outward" nodes. The return parameter that controls the likelihood of immediately revisiting a node in the walk will be automatically adjusted.
To generate metapath2vec random walks, execute the following command:
python main.py --extract-instance --burn-in-phase 1 --burn-in-input-size 0.3 --walk-length 10 --num-walks 5 --metapath-scheme "ECTCE" --use-metapath-scheme --hin-file "[Name of the hin file].pkl" --file-name "[Name of the file without extension]" --ospath "[path to the hin file]" --dspath "[path where random walks would be stored]" --logpath "[path to the log directory]" --num-jobs 2
To generate JUST random walks, execute the following command:
python main.py --extract-instance --burn-in-phase 1 --burn-in-input-size 0.3 --walk-length 10 --num-walks 5 --just-type --just-memory-size 2 --hin-file "[Name of the hin file].pkl" --file-name "[Name of the file without extension]" --ospath "[path to the hin file]" --dspath "[path where random walks would be stored]" --logpath "[path to the log directory]" --num-jobs 2
To generate RUST random walks, execute the following command with :
python main.py --extract-instance --burn-in-phase 3 --burn-in-input-size 0.3 --q 0.3 --walk-length 10 --num-walks 5 --just-type --just-memory-size 3 --hin-file "[Name of the hin file].pkl" --file-name "[Name of the file without extension]" --ospath "[path to the hin file]" --dspath "[path where random walks would be stored]" --logpath "[path to the log directory]" --num-jobs 2
For RUST, it is better to use --burn-in-phase = 3. --file-name corresponds to the desired file name, excluding any * EXTENSION*. The file will have .txt extension. The argument --q represents the probability to explore within layer nodes (breadth-search). The in-depth search will be automatically adjusted based on unit circle equation.
To learn embeddings using random walks, we provide few examples.
Description about arguments in all of given examples: --file-name corresponds to the .txt generate walks and * --model-name* corresponds the name of the models (excluding any EXTENSION). The model name will have .npz extension where each row indicate a node, having some predefined dimension size. --constraint-type enables the normalized skip gram model and fit-by-word2vec enables to train using gensim package.
Please do not use the sample "pathway2vec_embeddings.npz" during this step, and change the name of the embeddings file or store the provided "pathway2vec_embeddings.npz" in a different folder to avoid conflict.
To learn embeddings using dimension size --embedding-dim 128, context size --window-size 3, Number of samples to be considered within defined context size --num-skips 2, execute the following command:
python main.py --train --embedding-dim 128 --num-skips 2 --window-size 3 --hin-file "[Name of the generated hin file].pkl" --file-name "[Name of the .txt file]" --model-name "[Model name without extension]" --mdpath "[path where embeddings would be stored]" --rspath "[path to storing the cost values]" --ospath "[path to all object files]" --logpath "[path to the log directory]" --num-epochs 3 --num-jobs 2
To learn embeddings using the same above parameter settings but with metapath2vec++, execute the following command:
python main.py --train --constraint-type --embedding-dim 128 --num-skips 2 --window-size 3 --hin-file "[Name of the hin file].pkl" --file-name "[Name of the .txt file]" --model-name "[Model name without extension]" --mdpath "[path where embeddings would be stored]" --rspath "[path to storing the cost values]" --ospath "[path to all object files]" --logpath "[path to the log directory]" --num-epochs 3 --num-jobs 2
To learn embeddings using the same above parameter settings but with metapath2vec++ and trained using gensim package, execute the following command:
python main.py --train --fit-by-word2vec --constraint-type --embedding-dim 128 --num-skips 2 --window-size 3 --hin-file "[Name of the hin file].pkl" --file-name "[Name of the .txt file]" --model-name "[Model name without extension]" --mdpath "[path where embeddings would be stored]" --rspath "[path to storing the cost values]" --ospath "[path to all object files]" --logpath "[path to the log directory]" --num-epochs 3 --num-jobs 2
If you find pathway2vec useful in your research, please consider citing the following paper:
- M. A. Basher, Abdur Rahman, and Hallam, Steven J.. "Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction" , Bioinformatics (2020).
For any inquiries, please contact: arbasher@student.ubc.ca