https://github.com/wanchunnie/PB-LKS
A python package for phage-host interaction prediction
https://github.com/wanchunnie/PB-LKS
Last synced: about 2 months ago
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A python package for phage-host interaction prediction
- Host: GitHub
- URL: https://github.com/wanchunnie/PB-LKS
- Owner: wanchunnie
- License: apache-2.0
- Created: 2023-11-26T06:43:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-18T16:58:41.000Z (over 1 year ago)
- Last Synced: 2025-02-09T20:36:34.781Z (3 months ago)
- Language: Python
- Size: 9.29 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-virome - PB-LKS - K-mer profiles for phage-bacteria prediction. [Python] (Host Prediction / RNA Virus Identification)
README
# PB-LKS
This is the official code for our paper "PB-LKS: a python package for predicting **P**hage-**B**acteria interaction through **L**ocal **K**-mer **S**trategy"
```
+--------------------------------------------+
| |---\ |----\ | | / /----\ |
| | | | | | | / | |
| |___/ |____/ -- | |/\ \----\ |
| | | \ | | \ | |
| | |_____/ |_____ | \ \____/ |
+--------------------------------------------+
```
## Local environment setup
1. install conda(to manage environment)
2. Change directory to the path of this project
```bash
cd {your_path_to_PBLKS}
```
3. Run following codes in your terminal
```shell
conda create -n PB-LKS python=3.10
conda deactivate (if base environment is activated)
conda activate PB-LKS
pip install -r requirements.txt
```
## How to use
Both a command line tool and a python package is provided in this project. Developers and researchers may select according to needs after setting local environment.
### Command line interface
A command line interface is implemented and can be used by running following commands in your terminal.
```bash
cd {your_path_to_PBLKS}
# display help message of PB-LKS CLI
python PB-LKS.py -h
# run and display result with example input
python PB-LKS.py -e
```
other detailed arguments and their usages are listed as follows
```Shell
usage: python PB-LKS.py [-h] [-p PHAGE] [-b BAC] [-e] [-xgb] [-fea] [-ba] [-o OUTPUT] [-d]
options:
-h, --help show this help message and exit
-p PHAGE, --phage PHAGE
path/folder to your phage sequence file(in fasta format)
-b BAC, --bac BAC path/folder to your bacteria sequence file(in fasta format)
-e, --example run model with example input and quit
-xgb, --xgboost run prediction with xgboost(default model is based on RandomForest)
-fea, --feature show 10 features of most importance and exit
-ba, --batch run prediction in batch, -p,-b should be folder if set True
-o OUTPUT, --output OUTPUT
the folder you want to save batch prediction results (results will be printed into terminal by default)
-d, --detail if set true, print detailed prediction results(single prediction), including important features and decision path.
```
### python package
To make it more convenient for researchers and developers to use our model in more personalized tasks, a python package is also implemented.
following functions are available
example code:
```python
import PBLKS
bacteria_path = 'example1.fasta'
phage_path = 'example2.fasta'
# directly gives the result of prediction
result, prob = PBLKS.predict(bacteria_path, phage_path)
# gets descriptors from files
# can be used to reconstruct or train other type of models
feas = PBLKS.get_descriptor(bacteria_path, phage_path)
# predicts the interaction of each bacteria-phage pair in both folder
bac_folder = "your_folder_path"
phage_folder = "your_folder_path"
out_put_dir = "your_dest_folder"
# output'll be directed to dest folder, creating 2 result files
PBLKS.predict_in_batch(bac_folder, phage_folder, output_dir=out_put_dir)
# printing results into terminal
PBLKS.predict_in_batch(bac_folder, phage_folder, output_dir=None)
# get decision path
decision_path = PBLKS.show_decision_path(bacteria_path, phage_path)
# get top 10 important features
# result is orgnized in dict like: {kmer: importance}
features = PBLKS.show_feature_importance(top_cnt=10)
```
## decision path visualization
Interpertability is a key feature in decision tree based alogrithms, several interfaces to visualize how our model make decisions are thus implemented.
Except calling `-d` or `PBLKS.show_decision_path` to show decision path with 01, we implemented a script to visualize decision trees in our model.
Users can run following command in termianl
```bash
python make_tree_plots.py
```
This'll create a detailed visualization of every decision tree into `/PBLKS/Trees`, providing a more straightforward way to visualize decision.
## other models
A xgboost based model is also included in `/models`.
can be used by adding `-xgb` argument in CLI or adding parameter `use_xgboost=True` when calling methods in our package
## Source code
Visit this git repository to get source code of PB-LKS:
https://github.com/wanchunnie/PB-LKS