Journal article
Anna Konstorum, Jian Xing, Shuchin Aeron, Misha Kilmer, Steven H. Kleinstein
bioRxiv, Cold Spring Harbor Laboratory, 2026
bioRxiv, Cold Spring Harbor Laboratory, 2026
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Cite
APA
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Konstorum, A., Xing, J., Aeron, S., Kilmer, M., & Kleinstein, S. H. (2026). Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective. BioRxiv. https://doi.org/10.64898/2026.07.03.736447
Chicago/Turabian
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Konstorum, Anna, Jian Xing, Shuchin Aeron, Misha Kilmer, and Steven H. Kleinstein. “Assessing Tensor Decomposition Quality of Immune Profiling Data from a Dictionary Learning Perspective.” bioRxiv (2026).
MLA
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Konstorum, Anna, et al. “Assessing Tensor Decomposition Quality of Immune Profiling Data from a Dictionary Learning Perspective.” BioRxiv, Cold Spring Harbor Laboratory, 2026, doi:10.64898/2026.07.03.736447.
BibTeX Click to copy
@article{konstorum2026a,
title = {Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective},
year = {2026},
journal = {bioRxiv},
publisher = {Cold Spring Harbor Laboratory},
doi = {10.64898/2026.07.03.736447},
author = {Konstorum, Anna and Xing, Jian and Aeron, Shuchin and Kilmer, Misha and Kleinstein, Steven H.}
}
Abstract
Systems-level immune profiling data arising from longitudinal studies of vaccination or infection has an inherent multi-index array structure. While tensor decomposition of such datasets has gained popularity, choosing a rank and trial for a decomposition is not straightforward. We show that taking into account the experimental data model can inspire the development of new metrics to assess the quality of a Non-negative CANDECOMP/PARAFAC (NCPD) decomposition, and can thus be used to choose a rank and trial for the decomposition. Moreover, we show how framing the results via a dictionary learning framework can better enable interpretation of the components of the decomposition.
Systems-level immune profiling data arising from longitudinal studies of vaccination or infection has an inherent multi-index array structure. While tensor decomposition of such datasets has gained popularity, choosing a rank and trial for a decomposition is not straightforward. We show that taking into account the experimental data model can inspire the development of new metrics to assess the quality of a Non-negative CANDECOMP/PARAFAC (NCPD) decomposition, and can thus be used to choose a rank and trial for the decomposition. Moreover, we show how framing the results via a dictionary learning framework can better enable interpretation of the components of the decomposition.