Chronogram: an R package for data curation and analysis of infection and vaccination cohort studies.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-09-27 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae146
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
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引用次数: 0

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/.

Chronogram:用于感染和疫苗接种队列研究数据整理和分析的 R 软件包。
动机:跟踪疫苗和感染反应的观察性队列研究为大流行病政策提供了真实世界的数据。将生物学假设(如不同的抗原累积暴露模式是否会产生不同的抗体反应)转化为分析代码可能会很繁琐,尤其是在源数据被分解的情况下:R 软件包 chronogram 引入了 chronogram 类,其中的元数据与稀疏的感染事件、临床和实验室数据进行了无缝聚合。每种实验方式都是按顺序添加的,因此可以纳入新数据,如耗时的专业研究测定或其下游分析。源数据可以是任何矩形数据格式,包括数据库表(如结构化查询语言数据库)。这支持汇总数据类型/来源的注释,例如,以病原体诊断的方式将一个或多个传染病发作的症状、分子检测和测序结合起来。Chronogram 对观察数据进行排列,以便通过共享词汇将生物学假设转化为相应的代码:Chronogram由R语言实现,在MIT许可下可在以下网址获取:https://www.github.com/FrancisCrickInstitute/chronogram;用户手册可在以下网址获取:https://franciscrickinstitute.github.io/chronogram/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.60
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