Data Integration for Immunology

Posted: 31 Jul 2020

See all articles by Silvia Pineda

Silvia Pineda

University of California, San Francisco (UCSF)

Daniel Bunis

University of California, San Francisco (UCSF) - The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research

Idit Kosti

University of California, San Francisco (UCSF)

Marina Sirota

University of California, San Francisco (UCSF) - Institute for Computational Health Sciences

Date Written: July 2020

Abstract

Over the last several years, next-generation sequencing and its recent push toward single-cell resolution have transformed the landscape of immunology research by revealing novel complexities about all components of the immune system. With the vast amounts of diverse data currently being generated, and with the methods of analyzing and combining diverse data improving as well, integrative systems approaches are becoming more powerful. Previous integrative approaches have combined multiple data types and revealed ways that the immune system, both as a whole and as individual parts, is affected by genetics, the microbiome, and other factors. In this review, we explore the data types that are available for studying immunology with an integrative systems approach, as well as the current strategies and challenges for conducting such analyses.

Suggested Citation

Pineda, Silvia and Bunis, Daniel and Kosti, Idit and Sirota, Marina, Data Integration for Immunology (July 2020). Annual Review of Biomedical Data Science, Vol. 3, pp. 113-136, 2020, Available at SSRN: https://ssrn.com/abstract=3658952 or http://dx.doi.org/10.1146/annurev-biodatasci-012420-122454

Silvia Pineda (Contact Author)

University of California, San Francisco (UCSF) ( email )

Third Avenue and Parnassus
San Francisco, CA CA 94143
United States

Daniel Bunis

University of California, San Francisco (UCSF) - The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research ( email )

San Francisco, CA
United States

Idit Kosti

University of California, San Francisco (UCSF)

Third Avenue and Parnassus
San Francisco, CA CA 94143
United States

Marina Sirota

University of California, San Francisco (UCSF) - Institute for Computational Health Sciences ( email )

Third Avenue and Parnassus
San Francisco, CA 94143
United States

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