RESUMO
BACKGROUND: Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, 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 optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel 'continuous' line segment algorithm that efficiently operates over data with a transformed m/z-axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m/z scale. RESULTS: The automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Several of the transformations investigated were able to reduce, if not entirely remove, the peak width and peak location relationship resulting in near-optimal baseline subtraction using the automated pipeline. The proposed novel 'continuous' line segment algorithm is shown to far outperform naive sliding window algorithms with regard to the computational time required. The improvement in computational time was at least four-fold on real MALDI TOF-MS data and at least an order of magnitude on many simulated datasets. CONCLUSIONS: The advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.
RESUMO
Mechanisms controlling the balance between proliferation and self-renewal versus growth suppression and differentiation during normal and leukemic myelopoiesis are not understood. We have used the bi-potent FDB1 myeloid cell line model, which is responsive to myelopoietic cytokines and activated mutants of the granulocyte macrophage-colony stimulating factor (GM-CSF) receptor, having differential signaling and leukemogenic activity. This model is suited to large-scale gene-profiling, and we have used a factorial time-course design to generate a substantial and powerful data set. Linear modeling was used to identify gene-expression changes associated with continued proliferation, differentiation, or leukemic receptor signaling. We focused on the changing transcription factor profile, defined a set of novel genes with potential to regulate myeloid growth and differentiation, and demonstrated that the FDB1 cell line model is responsive to forced expression of oncogenes identified in this study. We also identified gene-expression changes associated specifically with the leukemic GM-CSF receptor mutant, V449E. Signaling from this receptor mutant down-regulates CCAAT/enhancer-binding protein alpha (C/EBPalpha) target genes and generates changes characteristic of a specific acute myeloid leukemia signature, defined previously by gene-expression profiling and associated with C/EBPalpha mutations.