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Proteomic matrix-assisted laser desorption/ionisation (MALDI) lineartime-of-flight (TOF) mass spectrometry (MS) may be used to produce proteinprofiles from biological samples with the aim of discovering biomarkers fordisease. However, the raw protein profiles suffer from several sources of biasor systematic variation which need to be removed via pre-processing beforemeaningful downstream analysis of the data can be undertaken. Baselinesubtraction, an early pre-processing step that removes the non-peptide signalfrom the spectra, is complicated by the following: (i) each spectrum has, onaverage, wider peaks for peptides with higher mass-to-charge ratios (m/z), and(ii) the time-consuming and error-prone trial-and-error process for optimisingthe baseline subtraction input arguments. With reference to the aforementionedcomplications, we present an automated pipeline that includes (i) a novel`continuous' line segment algorithm that efficiently operates over data with atransformed m/z-axis to remove the relationship between peptide mass and peakwidth, and (ii) an input-free algorithm to estimate peak widths on thetransformed m/z scale. The automated baseline subtraction method was deployedon six publicly available proteomic MS datasets using six different m/z-axistransformations. Optimality of the automated baseline subtraction pipeline wasassessed quantitatively using the mean absolute scaled error (MASE) whencompared to a gold-standard baseline subtracted signal. Near-optimal baselinesubtraction was achieved using the automated pipeline. The advantages of theproposed pipeline include informed and data specific input arguments forbaseline subtraction methods, the avoidance of time-intensive and subjectivepiecewise baseline subtraction, and the ability to automate baselinesubtraction completely. Moreover, individual steps can be adopted asstand-alone routines.

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