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Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

Nature Machine Intelligence volume 2, pages 619–628 (2020)Cite this article

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Abstract

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

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Fig. 1: The immunological Elastic-Net analysis pipeline.
Fig. 2: Integration of immunological priors.
Fig. 3: Prior knowledge and sparsification.
Fig. 4: Incorporation of prior knowledge improves predictions in two clinical studies and a simulated experiment.
Fig. 5: iEN is robust to errors in the prior knowledge tensor.

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Data availability

Longitudinal term pregnancy raw data, processed data and source code for reproduction of the results are publicly available at http://flowrepository.org/id/FR-FCM-ZY3Q and http://flowrepository.org/id/FR-FCM-ZY3R for the original and validation studies, respectively. Similarly, chronic periodontitis raw data, processed data and source code for reproduction of the results are publicly available at https://flowrepository.org/id/FR-FCM-ZYT6.

Code availability

The iEN source code as well as scripts for reproduction of the results are available through: https://nalab.stanford.edu/immunological-elastic-net/ and https://github.com/Teculos/immunological-EN under an MIT licence with https://doi.org/10.5281/zenodo.3885868.

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Acknowledgements

This study was supported by the March of Dimes Prematurity Research Center at Stanford (22-FY18-808), the Bill and Melinda Gates Foundation (OPP1112382, OPP1189911 and OPP1113682), the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, the Robertson Foundation, NIH (1R01HL13984401A1, R35GM138353), the American Heart Association (18IPA34170507 and 19PABHI34580007), the Food and Drug Administration (FDA; HHSF223201610018C), the Burroughs Wellcome Fund (1019816) and the National Institutes of Health (NIH; R01AG058417, R01HL13984401, R21DE02772801 and R61NS114926). N.A., D.R.M. and G.P.N. were supported by US FDA contract no. HHSF223201610018C. N.S. was supported by a Stanford Immunology Training Grant (5 T32 AI07290-33). B.G. was supported by the NIH (K23GM111657) and the Doris Duke Charitable Foundation (2018100). D.G. and B.G. were supported by NIH R21DE02772801. L.P. and X.H. were supported by the Stanford Maternal and Child Health Research Institute. The authors are solely responsible for the content of this Article, which does not necessarily represent the official views of the US Department of Health and Human Services (HHS).

Author information

Author notes

  1. These authors contributed equally: Anthony Culos, Amy S. Tsai.

  2. These authors jointly supervised this work: Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour.

Authors and Affiliations

  1. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Anthony Culos, Amy S. Tsai, Natalie Stanley, Martin Becker, Mohammad S. Ghaemi, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Camilo Espinosa, Maria Xenochristou, Edward Ganio, Laura Peterson, Xiaoyuan Han, Ina A. Stelzer, Kazuo Ando, Dyani Gaudilliere, Thanaphong Phongpreecha, Ivana Marić, Alan L. Chang, Martin S. Angst, Brice Gaudilliere & Nima Aghaeepour

  2. Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA

    Anthony Culos, Natalie Stanley, Martin Becker, Mohammad S. Ghaemi, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Camilo Espinosa, Maria Xenochristou, Thanaphong Phongpreecha, Alan L. Chang, Trevor Hastie, Robert Tibshirani & Nima Aghaeepour

  3. Digital Technologies Research Centre, National Research Council Canada, Toronto, Ontario, Canada

    Mohammad S. Ghaemi

  4. Department of Microbiology and Immunology, Baxter Laboratory in Stem Cell Biology, Stanford University School of Medicine, Stanford, CA, USA

    David R. McIlwain & Wendy Fantl

  5. Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Laura Peterson, Ivana Marić, Gary M. Shaw, David K. Stevenson, Kara L. Davis, Brice Gaudilliere & Nima Aghaeepour

  6. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Thanaphong Phongpreecha, Sean Bendall & Garry P. Nolan

  7. Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA

    Wendy Fantl

  8. Department of Urology, Stanford University School of Medicine, Stanford, CA, USA

    Wendy Fantl

  9. Department of Statistics, Stanford University, Stanford, CA, USA

    Trevor Hastie & Robert Tibshirani

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  1. Anthony Culos
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Contributions

A.C. conducted the data analysis and software development, generated figures and contributed to writing the manuscript. A.S.T. performed experiments, processed data, wrote the manuscript and produced figures. N.S., M.B., M.S.G., R.F., H.N., T.P., I.M., A.L.C., C.E. and M.X. contributed to the analysis plan, figure design and manuscript revisions. E.G., L.P., X.H., I.A.S., K.A. and D.G. designed and performed experiments. D.R.M., A.T., G.M.S., D.K.S., S.B., K.L.D., W.F., G.P.N., T.H. and R.T. contributed to the design and evaluation of the algorithm, and edited the manuscript. M.S.A. and B.G. coordinated the effort to collect and analyse biological data and interpreted the results, and contributed to writing the manuscript. N.A. conceived, designed and coordinated the study, interpreted data, and contributed to writing the manuscript. All authors read and approved the Article.

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Correspondence to Nima Aghaeepour.

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Culos, A., Tsai, A.S., Stanley, N. et al. Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. Nat Mach Intell 2, 619–628 (2020). https://doi.org/10.1038/s42256-020-00232-8

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