This repository outlines the methodologies employed to evaluate different scikit-learn feature selection methods within the Qiime2 q2-classifier framework.
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.
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.
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.
- Clone the Repository
git clone https://github.com/Metu-O/Feature-Selection-Qiime2
- Navigate to the Repository
cd Feature-Selection-Qiime2
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Activate Qiime2 environment
Follow the instructions provided in the QIIME 2 installation guide to activate the QIIME 2 Core 2022.8 distribution.
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Run the scripts
Execute the code using command lines or a bash script (customize the bash script according to your path).
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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'
- 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'
- 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'
- 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'
- 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.
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.
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).
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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
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Osele, M. Machine Learning for Biological Data. University of Arkansas for Medical Sciences (2022).
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Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011