speaq

speaq automates analysis of raw one-dimensional (1D) Nuclear Magnetic Resonance (NMR) spectra to generate feature matrices for high-throughput metabolomics.

Key Features:

• Wavelet-based summarization: Uses wavelet-based techniques to condense raw 1D NMR spectral data while preserving essential information.
• Automated peak-picking and grouping: Performs peak-picking followed by feature grouping and bypasses traditional binning methods.
• Feature matrix generation: Constructs a matrix of features, samples, and peak values for downstream analysis.
• Multivariate statistical functions: Provides built-in multivariate statistical functions for comprehensive data analysis.
• Compatibility with NMR and LC-MS workflows: Compatible with other NMR frameworks and complementary liquid chromatography-mass spectrometry (LC-MS) workflows.

Scientific Applications:

• Metabolomics: Enables robust, high-throughput analysis of complex 1D NMR metabolomics datasets.
• Biochemistry and systems biology: Supports biochemical and systems-level studies requiring quantitative spectral feature extraction.
• Clinical diagnostics: Facilitates analysis of NMR spectra for clinical diagnostic research using spectral feature matrices.

Methodology:

Wavelet-based summarization of raw 1D NMR spectra; peak-picking followed by feature grouping without binning; construction of a feature-by-sample matrix of peak values.