Chronogram: an R package for data curation and analysis of infection and vaccination cohort studies
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David Greenwood Marianne Shawe-Taylor Hermaleigh Townsley Joshua Gahir Nikita Sahadeo Yakubu Alhassan Charlotte Chaloner Oliver Galgut Gavin Kelly David LV Bauer Emma Wall Mary Wu Edward CarrAbstract
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/.
Journal details
Journal
Bioinformatics Advances
Volume
4
Issue number
1
Pages
vbae146
Available online
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10.1093/bioadv/vbae146
Europe PubMed Central
39399374
Pubmed
39399374
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