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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Parasitic Diseases Laboratory » Research » Publications at this Location » Publication #390379
Research Project: Molecular Approaches to Control Intestinal Parasites that Affect the Microbiome in Swine and Small Ruminants

Location: Animal Parasitic Diseases Laboratory

Title: Large-scale meta-longitudinal microbiome data with a known batch factor

Author
item Oh, Sunghee
item Li, Robert

Submitted to: Genes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/18/2022
Publication Date: 2/22/2022
Citation: Oh, S., Li, R.W. 2022. Large-scale meta-longitudinal microbiome data with a known batch factor. Nature Methods. https://doi.org/10.3390/genes13030392.
DOI: https://doi.org/10.3390/genes13030392

Interpretive Summary: The gut microbiome plays an important role in the host physiology, nutrition, and development by modulating immunity and protects the host against invading pathogens. Gut microbial compositions fluctuate widely in response to numerous biotic and environmental factors. Time series or longitudinal studies are ideal methods to understand the dynamic nature of the microbiome. Currently, there are few generic frameworks that enable unified statistical inferences of time series data obtained across multiple research centres via different platforms and from different phenotype measurements. In this study, we developed a better batch detection tool that outperformed conventional methods under various simulation and real-world scenarios in data analysis.

Technical Abstract: Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.