Regressing Microbiome Taxa Count Data
Updated: Jun 16, 2020
Regressing microbiome data on covariates is challenging, but can provide a huge amount of information on biomarkers, mechanisms of action, and developing a testable hypothesis.
Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data.
At this talk from at the Microbiome Data Congress 2020, Bill Shannon PhD, BioRankings co-founder, talks about the use of DM-RPart applied to cytokine data and microbiome taxa count data. This model is applicable to any microbiome taxa count/metadata, automatically fit, and intuitively interpretable.
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