Chronogram

Chronogram integrates diverse data sources like PCR, sequencing, and symptom diaries into a tidy-like data structure to facilitate biological hypothesis testing in infection and vaccination cohort studies.

Key Features:

• Data integration: Aggregates metadata with sparse infection episode, clinical and laboratory data from any rectangular data format, including structured query language database tables.
• Sequential data incorporation: Each experimental modality is added sequentially, allowing the incorporation of new data such as specialized time-consuming research assays or their downstream analyses.
• Pathogen-agnostic annotations: Supports annotations that aggregate data types and sources, combining symptoms, molecular testing, and sequencing of one or more infectious episodes.
• Hypothesis translation: Arranges observational data to allow the translation of biological hypotheses into corresponding code via a shared vocabulary.

Scientific Applications:

• Vaccine and infection response cohort studies: Enables analysis of observational cohort studies that track vaccine and infection responses to inform pandemic policy.
• Antigenic exposure analysis: Supports testing of biological hypotheses such as whether different patterns of accumulated antigenic exposures confer differing antibody responses.

Methodology:

Chronogram implements the chronogram class in R where metadata is seamlessly aggregated with sparse infection episode, clinical and laboratory data.