Machine learning model training

machine-learning-model-training

Summary

Train a supervised machine learning classifier to predict DIA file quality from 15 extracted mass spectrometry metrics (e.g., precursor intensity, MS/MS count, dynamic range) across timsTOF, TripleTOF, and Orbitrap instruments. This skill produces a deployable quality-prediction model that enables automated monitoring and flagging of instrument performance issues.

When to use

You have a labeled dataset of DIA raw files (.raw, .d, .wiff) with known quality annotations and have extracted the 15 iDIA-QC metrics (raw file characteristics from timsTOF, TripleTOF, or Orbitrap instruments). Apply this skill when you need to build a generalizable predictor to automatically classify new incoming DIA files as pass/fail or quality-tier without manual inspection.

When NOT to use

• The input DIA files have not yet been processed through metric extraction (15 metrics are not yet computed); extract metrics first using the iDIA-QC workflow.
• Quality labels are missing or highly imbalanced (<<5% minority class) without rebalancing strategies; model training requires sufficient labeled examples.
• New DIA files originate from instrument types (e.g., newer Bruker timsTOF models) not represented in the training data; retraining or domain adaptation may be needed.

Inputs

• Feature matrix: 15 extracted DIA metrics per file (timsTOF, TripleTOF, or Orbitrap origin)
• Target vector: quality labels for training files (binary or multiclass)
• Training dataset: labeled DIA files (.raw, .d, .wiff formats or pre-extracted metric tables)

Outputs

• Trained machine learning model (serialized, e.g., pickle/joblib in Python)
• Quality predictions for new DIA files (file ID, predicted label, confidence scores)
• Model performance report (accuracy, precision, recall, F1, ROC-AUC on validation/test set)
• Longitudinal quality summary and visualizations for administrator notification

How to apply

Load the 15 extracted DIA metrics and corresponding quality labels from your training dataset. Prepare a feature matrix (rows = files, columns = 15 metrics) and target vector (quality labels); handle missing values and outliers (e.g., log-scaling or winsorization of extreme metric values). Partition data into training (e.g., 70–80%) and validation/test sets using stratified splitting to preserve class balance. Train a supervised classifier (algorithm and hyperparameters should match the iDIA-QC paper's reported approach) on the training set using the 15 metrics as input features. Evaluate performance on held-out validation/test data using classification metrics (accuracy, precision, recall, F1-score, ROC-AUC). Once validated, apply the trained model to new DIA files to generate quality predictions; output results with file identifiers, predicted labels, and optional confidence scores to enable longitudinal quality monitoring and admin notification.

Related tools

• DIA-NN (Provides protein qualitative and quantitative algorithms that inform the 15 DIA metrics used as model input features)
• msConvert (Converts raw DIA files to extracted precursor ion chromatogram (PIC) format, upstream of metric extraction)
• Python (scikit-learn, pandas, numpy) (Primary environment for data preparation, model training, evaluation, and prediction in iDIA-QC) — https://github.com/guomics-lab/iDIA-QC

Evaluation signals

• Model achieves >85% accuracy (or project-specified threshold) on held-out test set; F1-score reflects balance between precision and recall for the quality-prediction task.
• Feature importance or coefficients confirm that the 15 extracted metrics have non-zero, interpretable influence on predictions (e.g., precursor intensity, MS/MS count, dynamic range are top predictors).
• Predictions on new DIA files align with manual quality inspection or longitudinal instrument performance trends (e.g., predicted 'fail' files later show instrument degradation).
• Output predictions include file identifiers and confidence scores; results are shareable via email/WeChat to administrators without requiring post-processing.
• Model generalizes across timsTOF, TripleTOF, and Orbitrap instruments without retraining (if pooled in training) or degradation in cross-instrument validation.

Limitations

• Model performance may degrade on DIA files from instrument types or configurations not represented in the training data; periodic retraining recommended as instrument settings evolve.
• The 15 metrics assume standard DIA experimental protocols and file formats (.raw, .d, .wiff); non-standard DIA modes or preprocessing may yield unreliable predictions.
• Quality label definition (pass/fail or multiclass tier) is not detailed in the article; mismatch between training labels and operational quality criteria will reduce practical utility.
• Analysis time per file is under 5 minutes (reported in README), but model training time on large datasets is not specified; scalability to thousands of files not quantified.

Evidence

• [other] Load the 15 extracted DIA metrics and corresponding quality labels from the training dataset; prepare feature matrix and target labels, handling missing values or outliers.: "Load the 15 extracted DIA metrics and corresponding quality labels from the training dataset. 2. Prepare feature matrix and target labels, handling any missing values or outliers."
• [readme] Based on 15 metrics extracted from the DIA files, which characterize raw files from timsTOF, TripleTOF, and Orbitrap instruments.: "Based on 15 metrics extracted from the DIA files, which describe the characteristics of the raw files from timsTOF serial instruments, TripleTOF instruments, and Orbitrap serial instruments"
• [other] Train a machine learning model on the training set using the 15 metrics as features; evaluate model performance on held-out data using appropriate classification metrics.: "Train a machine learning model (algorithm and hyperparameters derived from paper's reported approach) on the training set using the 15 metrics as features. 5. Evaluate model performance on held-out"
• [other] Apply the trained model to new DIA files to generate quality predictions and save results with file identifiers and predicted labels.: "Apply the trained model to new DIA files to generate quality predictions and save results with file identifiers and predicted labels."
• [readme] iDIA-QC promptly shares prediction results and visualization with administrators via email and WeChat.: "it promptly shares the prediction results and visualization with administrators via email and WeChat"
• [readme] Machine learning outputs feature results and status information of the original file, along with potential instrument issues.: "Machine learning outputs feature results and status information of the original file, along with potential instrument issues."