Feature-Selection-Qiime2

This repository outlines the methodologies employed to evaluate different scikit-learn feature selection methods within the Qiime2 q2-classifier framework.

Embedded Feature Selection

1. SelectFromModel Using Multinomial Naive Bayes Estimator

The sklearn Multinomial Naive Bayes (MultinomialNB) classifier is utilized as the estimator for feature selection within the classification pipeline. The feature_log_prob_ attribute from RFC is employed as the importance_getter parameter. For more details about sklearn's MultinomialNB, refer to this link.

2. SelectFromModel Using Random Forest Estimator

In this approach, the sklearn Random Forest Classifier (RFC) serves as the estimator for feature selection within the classification pipeline. The feature_importances_ attribute from RFC is used as the importance_getter parameter. For further information on sklearn's SelectFromModel, visit this link. Detailed information about sklearn's RFC is available here.

3. SelectFromModel Using Stochastic Gradient Descent Estimator

The sklearn Stochastic Gradient Descent (SGD) classifier is harnessed as the base estimator for feature selection within the classification pipeline. The coef_ attribute from RFC is used as the importance_getter parameter. Learn more about sklearn's SGDClassifier by referring to https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html.

Getting Started

1. Clone the Repository

git clone https://github.com/Metu-O/Feature-Selection-Qiime2

2. Navigate to the Repository

cd Feature-Selection-Qiime2

3. Activate Qiime2 environment

   Follow the instructions provided in the QIIME 2 installation guide to activate the QIIME 2 Core 2022.8 distribution.

4. Run the scripts

   Execute the code using command lines or a bash script (customize the bash script according to your path).

Usage

1. Naive Bayes Parameters Script

   To run the Naive Bayes classifier with default parameters, use:

python Naive_Bayes_Parameters.py

For custom settings:

python Naive_Bayes_Parameters.py -n 'database_name' -s 'path_to_sequences.qza' -t 'path_to_taxonomy.qza'

Example:

python Naive_Bayes_Parameters.py -n 'greengenes' -s '/path/to/sequences.qza' -t '/path/to/taxonomy.qza'

2. SelectFromModel MultinomialNB Script

To run the classifier with the SelectFromModel method using MultinomialNB estimator:

python SelectFromModel_MultinomialNB.py

For custom settings:

python SelectFromModel_MultinomialNB.py -n 'database_name' -s 'path_to_sequences.qza' -t 'path_to_taxonomy.qza'

3. SelectFromModel RandomForest Script

To run the classifier with the SelectFromModel method using RandomForestClassifier estimator:

python SelectFromModel_RandomForest.py

For custom settings:

python SelectFromModel_RandomForest.py -n 'database_name' -s 'path_to_sequences.qza' -t 'path_to_taxonomy.qza'

4. SelectFromModel Stochastic Gradient Descent Script

To run the classifier with the SelectFromModel method using Stochastic Gradient Descent (SGD) estimator:

python SelectFromModel_SDG.py

For custom settings:

python SelectFromModel_SDG.py -n 'database_name' -s 'path_to_sequences.qza' -t 'path_to_taxonomy.qza'

5. Evaluate Method Accuracy Script

To compare method accuracy:

python Evaluate_Method_Accuracy.py

For custom output directory:

python Evaluate_Method_Accuracy.py -p 'output_plots_directory'

Note: Running these scripts can be time-consuming and resource-intensive.

Bash Script (Optional)

The provided bash script can automate the execution of all codes. However, ensure you customize the script according to your environment and filepaths before use.

Conclusion

The Feature-Selection-Qiime2 repository has demonstrated significant improvement over standard Qiime2 in more than 70% of cases. For further details on the evaluation metrics, refer to the thesis (to be cited upon publication).

Citation

1. Bokulich, N.A., Kaehler, B.D., Rideout, J.R. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018). https://doi.org/10.1186/s40168-018-0470-z

2. Osele, M. Machine Learning for Biological Data. ... (not yet available).

3. Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011