Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm

Area of Science:

• Proteomics
• Mass Spectrometry
• Biomarker Discovery

Background:

• Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) is crucial for identifying disease biomarkers.
• Raw proteomic profiles contain biases requiring pre-processing, particularly baseline subtraction, which is complicated by varying peak widths and manual optimization.
• Existing methods for baseline subtraction in MALDI-TOF MS are time-consuming and prone to errors.

Purpose of the Study:

• To develop an automated pipeline for efficient and accurate baseline subtraction in proteomic MS data.
• To address challenges in baseline subtraction, including the relationship between peptide mass-to-charge ratio (m/z) and peak width.
• To eliminate the need for manual optimization of baseline subtraction parameters.

Main Methods:

• Implementation of a novel 'continuous' line segment algorithm operating on a transformed m/z-axis to normalize peak widths.
• Development of an input-free algorithm for estimating peak widths on the transformed m/z scale.
• Deployment of the automated pipeline on six public proteomic MS datasets with various m/z-axis transformations.

Main Results:

• The automated pipeline successfully reduced or eliminated the peak width and peak location relationship across multiple datasets and transformations.
• Quantitative assessment using Mean Absolute Scaled Error (MASE) demonstrated near-optimal baseline subtraction.
• The 'continuous' line segment algorithm significantly outperformed naive sliding window algorithms, achieving at least a four-fold improvement in computational time on real data.

Conclusions:

• The proposed automated pipeline provides data-specific, informed arguments for baseline subtraction, avoiding subjective and time-intensive manual methods.
• Complete automation of baseline subtraction is achievable, enhancing reproducibility and efficiency in proteomic data analysis.
• Individual components of the pipeline can be utilized as standalone routines for specific pre-processing needs.