wspc 0.0.4

Protein-content-based bacterial pathogenicity classifier

WSPC

Installing the package:

pip install wspc

Dependencies

• Python >=3.6
• Packages: pandas, numpy, scikit-learn, scipy

Command Line

In windows: make sure that the python "Scripts\" directory is added to PATH, so that the package can be executed as a command

Usage:

usage: wspc [-h] [-m {predict,fit}] -i I [-o OUTPUT] [-l LABELS_PATH] [--model_path MODEL_PATH] [-k K] [-t T]

optional arguments:
  -h, --help            show this help message and exit
  -m {predict,fit}, --mode {predict,fit}
  -i I                  input directory with genome *.txt files or a merged input *.fasta file
  -o OUTPUT, --output OUTPUT
                        output directory, default current directory
  -l LABELS_PATH, --labels_path LABELS_PATH
                        path to *.csv file with labels
  --model_path MODEL_PATH
                        path to a saved model in a *.pkl file. If not provided, saved pre-trained model will be used
  -k K                  parameter for training - selecting k-best features using chi2
  -t T                  parameter for training - clustering threshold

Predict:

You can predict the pathogenicity potentials of group of genomes using a saved model in a *.pkl file. If a path is not provided, saved pre-trained model will be used. The WSPC pre-trained model can be found in https://github.com/shakedna1/wspc_rep/blob/main/src/wspc/model/WSPC_model.pkl.

wspc -m predict -i path_to_input_genomes

Train:

Train a new model using the fit command.

You can train a new model using the same k (selecting k-best features using chi2) and t (clustering threshold) values of WSPC (450 and 0.18 respectively) or using a different values of your choice.

wspc -m fit -i path_to_input_genomes -l path_to_labels -k 450 -t 0.18

Reconstruction of Training and Prediction on the dataset from the paper

1. Download and extract the dataset from https://github.com/shakedna1/wspc_rep/raw/main/Data/train_test_datasets.zip
   In Ubuntu:

      wget https://github.com/shakedna1/wspc_rep/raw/main/Data/train_test_datasets.zip
      unzip train_test_datasets.zip
   

2. Train:

       wspc -m fit -i train_genomes.fasta -l train_genomes_info.csv -k 450 -t 0.18 
   

   The file trained_model.pkl will be saved in the same directory (or in the directory provided through the -o argument)

3. Test:

      wspc -m predict -i test_genomes.fasta --model_path trained_model.pkl
   

   The file predictions.csv will contain the predictions

Running wspc as a python module

import wspc

# train
X_train = wspc.read_genomes(path_to_genomes)
y = wspc.read_labels(path_to_labels, X_train)

model = wspc.fit(X_train, y, k=450, t=0.18)

# predict

X_test = wspc.read_genomes(path_to_genomes)
predictions = wspc.predict(X_test, model)