pseudoQC

pseudoQC simulates regression-based quality control (QC) samples to correct and normalize unwanted signal variations in mass spectrometry-based metabolomics and proteomics datasets.

Key Features:

• Simulation of Quality Control (QC) Samples: Generates synthetic QC sample data for datasets that lack empirical QC samples to enable QC-based correction and normalization.
• Regression-Based Simulation: Uses regression-based simulation approaches to produce synthetic data that mimic actual experimental conditions in MS workflows.
• Machine Learning Regression Methods: Implements four distinct machine learning-based regression methods, including linear and nonlinear approaches, with nonlinear methods reported to perform better for correction and normalization.
• Correction and Normalization: Applies simulated QC datasets to correct and normalize metabolomics and proteomics measurements obtained by mass spectrometry.

Scientific Applications:

• Metabolomics and Proteomics Data Processing: Improves accuracy of metabolite and protein profiling by addressing unwanted signal variation in MS-based studies.
• Datasets Lacking QC Samples: Enables retrospective QC-based correction and normalization for existing datasets that did not include empirical QC samples.
• High-Throughput and Long-Term Studies: Facilitates handling of signal variability in large-scale, high-throughput, or long-term mass spectrometry experiments.
• Inter-Experimenter Variability: Addresses variability between experimenters by providing simulated QC references for normalization.

Methodology:

Employs regression-based simulations to generate synthetic QC data, applies four machine learning-based regression methods (linear and nonlinear) to model signal variation, and uses the simulated datasets to correct and normalize the original MS metabolomics and proteomics data.