Feature Selection

Run feature selection module

Assume that we have already run normalization module and selected best matrix processing method based on the UCA score, we can run feature selection module using the following command:

exseek.py feature_selection -d ${dataset}

Output files

Outuput directory

Feature selection results using one combination of parameters are saved in a separate directory:

${output_dir}/cross_validation/${preprocess_method}.${count_method}/${compare_group}/${classifier}.${n_select}.${selector}.${fold_change_filter_direction}

Variables in file patterns

Variable	Descrpition
output_dir	Output directory for the dataset, e.g. output/dataset
preprocess_method	Combination of matrix processing methods
count_method	Type of feature counts, e.g. domains_combined, domains_long, transcript, featurecounts
compare_group	Name of the negative-positive class pair defined in compare_groups.yaml
classifier	Classifier defined in the configuration file
n_select	Maximum number of features to select
selector	Feature selection method, e.g. robust, rfe
fold_change_filter_direction	Direction of fold change for filtering features. Three possible values: up, down and any

Files in output directory

File name pattern	Descrpition
features.txt	Selected features. Plain text with one column: feature names
feature_importances.txt	Plain text with two columns: feature name, feature importance
samples.txt	Sample IDs in input matrix selected for feature selection
classes.txt	Sample class labels selected for feature selection
final_model.pkl	Final model fitted on all samples in Python pickle format
metrics.train.txt	Evaluation metrics on training data. First row is metric names. First column is index of each train-test split
metrics.test.txt	Same format with metrics.train.txt on test data.
cross_validation.h5	Cross-validation details in HDF5 format.

Cross validation details (cross_validation.h5)

Dataset name	Dimension	Description
feature_selection	(n_splits, n_features)	Binary matrix indicating features selected in each cross-validation split
labels	(n_samples,)	True class labels
predicted_labels	(n_splits, n_samples)	Predicted class labels on all samples
predictions	(n_splits, n_samples)	Predicted probabilities of the positive class (or decision function for SVM)
train_index	(n_splits, n_samples)	Binary matrix indicating training samples in each cross-validation split