David Greenwood, Marianne Shawe-Taylor, Hermaleigh Townsley, Joshua Gahir, Nikita Sahadeo, Yakubu Alhassan, Charlotte Chaloner, Oliver Galgut, Gavin Kelly, David L V Bauer, Emma C Wall, Mary Y Wu, Edward J Carr
Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae146, https://doi.org/10.1093/bioadv/vbae146
Abstract
Motivation: Observational cohort studies that track vaccine and infection responses offer real-world data to inform pandemic policy. Translating biological hypotheses, such as whether different patterns of accumulated antigenic exposures confer differing antibody responses, into analysis code can be onerous, particularly when source data is dis-aggregated.
Results: The R package chronogram introduces the class chronogram, where metadata is seamlessly aggregated with sparse infection episode, clinical and laboratory data. Each experimental modality is added sequentially, allowing the incorporation of new data, such as specialized time-consuming research assays, or their downstream analyses. Source data can be any rectangular data format, including database tables (such as structured query language databases). This supports annotations that aggregate data types/sources, for example, combining symptoms, molecular testing, and sequencing of one or more infectious episodes in a pathogen-agnostic manner. Chronogram arranges observational data to allow the translation of biological hypotheses into their corresponding code via a shared vocabulary.
Availability and implementation: Chronogram is implemented R and available under an MIT licence at: https://www.github.com/FrancisCrickInstitute/chronogram; a user manual is available at: https://franciscrickinstitute.github.io/chronogram/
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