Open AccessThe Ontology of Biological and Clinical Statistics (OBCS)‐based statistical method standardization and meta‐analysis of host responses to yellow fever vaccines
Author(s) -
Zheng Jie,
Li Huan,
Liu Qingzhi,
He Yongqun
Publication year - 2017
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-017-0122-5
Subject(s) - ontology , gene ontology , missing data , vaccination , microarray analysis techniques , computer science , sample size determination , meta analysis , standardization , computational biology , sample (material) , data mining , bioinformatics , statistics , biology , medicine , immunology , machine learning , gene , mathematics , genetics , philosophy , gene expression , chemistry , epistemology , chromatography , operating system
Background The community‐based Ontology of Biological and Clinical Statistics (OBCS) represents and standardizes biological and clinical data and statistical methods. Methods Both OBCS and the Vaccine Ontology (VO) were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF‐17D. A literature meta‐analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. Results A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables (e.g., microarray type and human variations). Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. Conclusions The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology‐based meta‐analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods.