Multi instrument data integration

multi-instrument-data-integration

Summary

Integrate and standardize mass spectrometry DIA files from heterogeneous instrument platforms (timsTOF, TripleTOF, Orbitrap) into a unified analytical pipeline by converting raw formats to a common intermediate representation (PIC) and extracting instrument-agnostic quality metrics. This skill enables downstream quality prediction and longitudinal monitoring across a diverse instrument fleet.

When to use

You have DIA mass spectrometry raw files from multiple instrument types (timsTOF, TripleTOF, Orbitrap) in their native formats (.raw, .d, .wiff) and need to apply a single machine learning quality classifier or generate comparative quality visualizations across instruments without re-training separate models per platform.

When NOT to use

• Input files are already in PIC or mzML format — skip msConvert conversion and proceed directly to metric extraction.
• You need instrument-specific quality models — if instrument differences in quality drivers are significant, train separate classifiers per platform rather than forcing a unified model.
• Raw files are from a single instrument type — the integration overhead (instrument-agnostic feature engineering, format conversion validation) is unnecessary; use instrument-specific pipelines instead.

Inputs

• Raw DIA mass spectrometry files in native formats (.raw for Orbitrap, .d for timsTOF, .wiff for TripleTOF)
• msConvert parameter configuration for PIC extraction
• Pre-trained machine learning model (fitted on 15-metric feature space)
• Instrument type identifier for each input file

Outputs

• Extracted precursor ion chromatogram (PIC) files in standardized format
• 15-metric feature matrix with file identifiers and instrument labels
• Quality class predictions for each file (with confidence scores if applicable)
• Longitudinal summary visualizations aggregating quality across instruments

How to apply

First, convert each raw DIA file to extracted precursor ion chromatogram (PIC) format using msConvert with instrument-specific parameters, validating output for format integrity. Second, extract the standardized 15-metric feature set from each PIC file, ensuring all metrics are computed identically regardless of source instrument. Third, normalize or scale the 15-metric matrix to account for instrument-specific signal ranges or mass calibration offsets. Fourth, apply the unified pre-trained machine learning model (trained on the pooled 15-metric feature space) to predict quality labels for each file. This approach relies on the principle that the 15 metrics capture instrument-independent characteristics of DIA quality (e.g., precursor intensity distribution, MS/MS spectral richness) rather than instrument-specific artifacts.

Related tools

• msConvert (Converts raw DIA files from timsTOF, TripleTOF, and Orbitrap instruments into extracted precursor ion chromatogram (PIC) format, standardizing the intermediate file representation before metric extraction.)
• DIA-NN (Provides the protein qualitative and quantitative algorithms incorporated into iDIA-QC for downstream proteomics analysis following quality assessment.)
• Python (Environment for implementing the metric extraction, data normalization, machine learning model application, and output formatting steps within the iDIA-QC GUI framework.) — https://github.com/guomics-lab/iDIA-QC

Evaluation signals

• All input raw files successfully converted to PIC format without errors; PIC files contain expected chromatogram peaks and retention time ranges.
• The 15-metric feature matrix has identical columns and units across all input files, with no missing values or NaN entries (after imputation if applied).
• Quality predictions are generated for 100% of input files; predicted class labels match the expected range (e.g., 'pass'/'fail' or 0–100 confidence score) with no null outputs.
• Longitudinal visualizations show consistent instrument representation (no instrument type missing from the aggregated summary) and temporal trends align with known instrument maintenance or calibration events.
• Cross-validation or held-out test accuracy of the unified model on files from all three instrument types is within expected bounds (reported in the article or compared to instrument-specific baseline models).

Limitations

• The 15-metric feature set was derived from timsTOF, TripleTOF, and Orbitrap instruments; applicability to other DIA instruments or older instrument firmware versions is not validated.
• msConvert PIC extraction parameters may require tuning per instrument type (precursor m/z window, retention time binning); default parameters may not optimize quality metrics for all platforms.
• Machine learning model performance assumes the 15 metrics capture quality variation uniformly across instruments; systematic instrument biases in metric distributions could lead to instrument-specific prediction drift.
• The analysis time per file (~5 minutes) may scale non-linearly with large sample batches; computational resources (CPU, memory) are not specified.

Evidence

• [readme] 15-metric extraction and multi-instrument support: "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"
• [readme] msConvert conversion to PIC: "uses msConvert for file conversion to extracted precursor ion chromatogram (PIC)"
• [readme] Unified machine learning prediction across instruments: "we utilize machine learning models to predict the quality of the DIA files"
• [readme] Supported raw file formats by instrument: "This software supports the .raw, .d, and .wiff raw data formats"
• [readme] Longitudinal aggregation across instruments: "The outputs of iDIA-QC in a longitudinal summary, visually display the quality of the DIA files"