Tensor-based insights into systems immunity and infectious disease

Chin, Jackson L.; Chan, Liana C.; Yeaman, Michael R.; Meyer, Aaron S.

doi:10.1016/j.it.2023.03.003

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Pf2 localizes variation to specific samples, genes, and populations of cells while enabling interpretation beyond what is possible with nonlinear approaches such as autoencoders 88 . As the scale, capabilities, and questions asked using single cell techniques expands, fundamentally new approaches such as Pf2 that are scalable, easily interpretable, and unsupervised will be needed to understand how single cells coordinate their function within and across tissues 31 .…”

Section: Discussionmentioning

confidence: 99%

“…Most importantly, associating trends in the dataset to specific dimensions can help interpret the results and therefore derive insights from the underlying experiments 20,26,27 . Dimensionspecific associations also make these methods especially effective in data integration for combining datasets with shared experimental parameters 20,[28][29][30][31][32] .…”

Section: Introductionmentioning

confidence: 99%

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Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Ramirez,

Orcutt-Jahns,

Pascoe

et al. 2024

Preprint

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Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Ramirez,

Orcutt-Jahns,

Pascoe

et al. 2024

Preprint

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“…Examining individual cytokine responses or responsive pathways can only provide a limited picture of how responses vary, which is a common challenge with profiling data collected across several experimental variables/dimensions 22 . Therefore, we sought to identify patterns of response variation using an approach that explicitly accounts for data gathered across multiple experimental conditions.…”

Section: Cpd Reveals Coordinated and Substantial Bc-associated Patter...mentioning

confidence: 99%

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients’ blood

Orcutt-Jahns,

Lima Junior,

Rockne

et al. 2023

Preprint

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Cytokines mediate cell-to-cell communication across the immune system and therefore are critical to immunosurveillance in cancer and other diseases. Several cytokines show dysregulated abundance or signaling responses in breast cancer, associated with the disease and differences in survival and progression. Cytokines operate in a coordinated manner to affect immune surveillance and regulate one another, necessitating a systems approach for a complete picture of this dysregulation. Here, we profiled cytokine signaling responses of peripheral immune cells from breast cancer patients as compared to healthy controls in a multidimensional manner across ligands, cell populations, and responsive pathways. We find alterations in cytokine responsiveness across pathways and cell types that are best defined by integrated signatures across dimensions. Alterations in the abundance of a cytokine’s cognate receptor do not explain differences in responsiveness. Rather, alterations in baseline signaling and receptor abundance suggesting immune cell reprogramming are associated with altered responses. These integrated features suggest a global reprogramming of immune cell communication in breast cancer.Significance StatementWhile individual cytokine responses have previously been observed to be altered in breast cancer, cytokine signaling responses are tightly interconnected in a way that has not been previously characterized. Here, we profile cytokine signaling responses and find alterations that are shared across both pathways and cell types. The signatures across these measurements better define the alterations and point to a broad immunosuppression response.HighlightsBaseline and post-stimulation cytokine signaling profiles differ between healthy donors and breast cancer patients.Changes in cytokine response are not explained by differences in abundance of the cognate receptorFeatures of signaling response and receptor abundance dysregulation are coordinated across patientsIntegrated patterns of dysregulation in breast cancer patients share features of Th17 like-response as well as regulatory-like B and CD8+T cells

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“…Tensor factorization, including CPD, can permit more extensive data reduction over matrix methods while also better elucidating patterns in codependent processes 5 . Previous work has illustrated the application of CPD in gene expression 6 , serum antibody analysis 7 , immune cytokine responses 8 , and various other fields 9 .…”

Section: Introductionmentioning

confidence: 99%

Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization

Hung,

Hodzic,

Tan

et al. 2024

Preprint

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Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor. However, while tensor methods may impute these missing values, the choice of fitting algorithm may influence the fidelity of these imputations. Previous approaches, based on alternating least squares with prefilled values or direct optimization, suffer from introduced bias or slow computational performance. In this study, we propose that censored least squares can better handle missing values with data structured in tensor form. We ran censored least squares on four different biological datasets and compared its performance against alternating least squares with prefilled values and direct optimization. We used the error of imputation and the ability to infer masked values to benchmark their missing data performance. Censored least squares appeared best suited for the analysis of high-dimensional biological data by accuracy and convergence metrics across several studies.

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Tensor-based insights into systems immunity and infectious disease

Cited by 4 publications

References 13 publications

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients’ blood

Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization

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