An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data

Area of Science:

• Biochemistry
• Bioinformatics
• Data Science

Background:

• Nuclear Magnetic Resonance (NMR) spectroscopy is vital for quantitative metabolic profiling of biological tissues.
• Data analysis is challenging due to sample variations, necessitating preprocessing steps like noise reduction, normalization, and spectrum alignment.
• Standard NMR analysis pipelines require multiple indispensable preprocessing steps for accurate interpretation.

Purpose of the Study:

• To develop a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data.
• To introduce a new peak alignment algorithm, hierarchical Cluster-based Peak Alignment (CluPA), for improved spectral alignment.
• To establish a robust statistical methodology for analyzing variability in aligned NMR spectra.

Main Methods:

• A novel peak alignment algorithm, Cluster-based Peak Alignment (CluPA), utilizes hierarchical clustering and Fast Fourier Transformation (FFT) cross-correlation for efficient spectral alignment.
• A differential analysis method calculates the ratio of between-group and within-group sum of squares (BW-ratio) to quantify spectral variability.
• Statistical inference is performed by bootstrapping the null distribution from experimental data, avoiding distributional assumptions.

Main Results:

• The CluPA algorithm provides high-quality spectral alignment, enabling a simplified methodology for studying NMR spectral variability.
• The BW-ratio effectively quantifies differences in variability between predefined groups of NMR spectra.
• Evaluations using a published dataset demonstrated clear improvements in correlation maps, spectral, and grey scale plots compared to existing methods.

Conclusions:

• The developed workflow offers a modular and statistically sound framework for NMR metabolomic data analysis.
• The 'speaq' R package, implementing the entire workflow, is freely available for broader scientific use.
• The quantitative analysis is effective for CluPA-aligned spectra, enhancing the utility of NMR metabolomics.