pime: Prevalence Interval for Microbiome Evaluation - PIME
In microEcology/pime: Prevalence Interval for Microbiome Evaluation

This package removes the within group variation found in metataxonomic surveys (16S rRNA datasets) by capturing only biological differences at high sample prevalence levels. Prevalence is defined here as the percent of samples within a treatment that all contain the same taxa irrespective of relative abundance. It takes a phyloseq object as input, builds hundreds of decision trees using a supervised machine learning algorithm and combines them into a single model to predict the likelihood of detecting any factor as source of sample variation. Higher OOB error indicates the dataset has a high relative abundance of taxa with low prevalence, which is defined as noise in PIME analysis. To remove the noise, PIME applies the following steps: i) per treatment slices of the full dataset are obtained and filtered using prevalence values for taxa at 5 and determine the decline in noise in each prevalence interval. The number of taxa and the number of remaining sequences for each prevalence interval are also computed.

Prevalence Interval for Microbiome Evaluation

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microEcology/pime documentation built on Nov. 13, 2019, 11:16 p.m.