Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective

Area of Science:

• Immunology
• Computational Biology
• Data Science

Background:

• Longitudinal immune profiling data from vaccination or infection studies exhibit complex multi-index array structures.
• Tensor decomposition methods, like Non-negative CANDECOMP/PARAFAC (NCPD), are increasingly used for analyzing such high-dimensional datasets.
• Selecting appropriate parameters, such as rank and trial, for tensor decomposition remains a significant challenge.

Purpose of the Study:

• To develop novel metrics for assessing the quality of Non-negative CANDECOMP/PARAFAC (NCPD) decompositions.
• To provide a data-driven approach for selecting the optimal rank and trial for NCPD of immune profiling data.
• To enhance the interpretability of tensor decomposition components using a dictionary learning framework.

Main Methods:

• Utilized experimental data models to derive new quality assessment metrics for NCPD.
• Applied these metrics to guide the selection of rank and trial in tensor decomposition.
• Integrated a dictionary learning framework to facilitate the interpretation of decomposition results.

Main Results:

• Demonstrated that incorporating experimental data models leads to improved metrics for NCPD quality assessment.
• Showcased a robust method for choosing the rank and trial, optimizing the decomposition process.
• Established that framing NCPD results within a dictionary learning context significantly aids in component interpretation.

Conclusions:

• The proposed metrics, inspired by experimental data models, offer a principled way to evaluate and select parameters for NCPD in systems-level immune profiling.
• This approach enhances the reliability and interpretability of tensor decomposition for complex immunological datasets.
• The integration with dictionary learning provides a powerful tool for uncovering meaningful biological insights from high-dimensional immune data.