Peak classification validation

peak-classification-validation

Summary

Validate a trained neural network classifier's optimal decision threshold on a labelled peak dataset by computing true vs. false positive rates across probability thresholds and selecting the threshold that maximizes (TP − FP). This skill ensures the classifier is correctly tuned before deployment on unlabelled LCMS data.

When to use

After training or loading a NeatMS neural network model, before applying it to filter false positive MS1 peaks in a new dataset. Specifically when you have a labelled validation dataset with 'High_quality' and other class annotations, and need to determine the optimal classification threshold that balances true positives against false positives for your experimental context.

When NOT to use

• Input validation dataset is unlabelled or lacks 'High_quality' annotations—get_threshold() requires labelled data to compute true/false positive rates.
• You are tuning hyperparameters during model training—threshold optimization should occur after model convergence, not during iterative training.
• The peak detection pipeline has already been manually filtered or curated with domain knowledge—get_threshold() assumes ground truth labels reflect the classification task, not downstream filtering decisions.

Inputs

• Trained NeatMS neural network model (Keras/TensorFlow)
• Labelled peak dataset with binary annotations (e.g., 'High_quality' vs. other classes)
• NN_handler object initialized with model and validation data

Outputs

• Optimal classification threshold scalar value (float, e.g., 0.22)
• True vs. false positive rate table (DataFrame with thresholds and corresponding TP/FP counts)

How to apply

Load a trained NeatMS neural network model and its associated labelled validation dataset using NN_handler.create_model(). Prepare labelled peak batches and call nn_handler.get_threshold(), which internally invokes get_true_vs_false_positive_df(label='High_quality') to compute TP and FP rates across a range of probability thresholds. The method selects the threshold that maximizes the (TP − FP) criterion and returns this optimal scalar value. Verify the returned threshold value matches expected reference values (e.g., 0.22 for the default NeatMS model) as a sanity check before applying this threshold to new data.

Related tools

• NeatMS (Provides NN_handler class and get_threshold() method to compute and select optimal classification threshold from labelled peak batches.) — https://github.com/bihealth/NeatMS
• scikit-learn (Used internally by NeatMS for computing receiver operating characteristic (ROC) metrics and area under curve (AUC) calculations.)
• pandas (Used to construct and manipulate the true vs. false positive rate DataFrame returned by get_true_vs_false_positive_df().)
• NumPy (Underlying numerical computation library for threshold and rate calculations.)

Examples

nn_handler = NeatMS.NN_handler.create_model(model_path='path/to/model'); batches = nn_handler.create_batches(labelled_peaks_df); optimal_threshold = nn_handler.get_threshold(batches, label='High_quality'); print(f'Optimal threshold: {optimal_threshold}')

Evaluation signals

• Returned threshold value is a scalar float within the valid probability range [0.0, 1.0].
• Returned threshold matches reference value from literature or original model publication (e.g., 0.22 for the default NeatMS model).
• True vs. false positive DataFrame shows monotonic decrease in TP count and monotonic increase in FP count as threshold increases across probability values.
• The selected threshold corresponds to the row in the TP/FP table with maximum (TP − FP) value.
• Applying the returned threshold to the same validation dataset produces peak classifications with expected sensitivity and specificity ranges for high-quality MS1 peak detection.

Limitations

• The optimal threshold is dataset-specific: the (TP − FP) criterion may not suit all applications; clinical or high-precision use cases may require receiver operating characteristic (ROC) curve inspection and manual threshold selection.
• Threshold optimization is sensitive to class imbalance in the labelled dataset; if 'High_quality' peaks are rare, the optimizer may select a threshold that favours false negatives.
• The method assumes that labels in the validation dataset accurately reflect the ground truth classification task; systematic annotation errors or reviewer drift (noted in the article's 'Review mode' section) will bias the returned threshold.
• No automated stopping or validation-set hold-out is mentioned; if the same labelled dataset is used for both training and threshold selection, threshold values may be optimistic (overfit to the training cohort).

Evidence

• [other] Call nn_handler.get_threshold() which internally invokes get_true_vs_false_positive_df(label='High_quality') to compute true vs. false positive rates across probability thresholds.: "Call nn_handler.get_threshold() which internally invokes get_true_vs_false_positive_df(label='High_quality') to compute true vs. false positive rates across probability thresholds."
• [other] The method selects the threshold that maximizes (True positives minus False positives) and returns the optimal scalar value.: "The method selects the threshold that maximizes (True positives minus False positives) and returns the optimal scalar value."
• [other] Verify the returned threshold matches the expected reference value (0.22 for the default model).: "Verify the returned threshold matches the expected reference value (0.22 for the default model)."
• [intro] NeatMS relies on neural network based classification to enable automated filtering of false positive MS1 peaks reported by commonly used LCMS data processing pipelines.: "NeatMS relies on neural network based classification to enable automated filtering of false positive MS1 peaks reported by commonly used LCMS data processing pipelines."
• [methods] A type of peak that was considered High quality can slowly change into a Low quality as we go along, even with careful attention, it will most certainly happen.: "A type of peak that was considered High quality can slowly change into a Low quality as we go along, even with careful attention, it will most certainly happen."