The step_filter_taxa function is a general function that allows for flexible filtering of OTUs based on across-sample abundance criteria. The other functions, step_filter_by_prevalence, step_filter_by_variance, step_filter_by_abundance, and step_filter_by_rarity, are convenience wrappers around step_filter_taxa, each designed to filter OTUs based on a specific criterion: prevalence, variance, abundance, and rarity, respectively.
The step_subset_taxa function is used to subset taxa based on their taxonomic level.
The phyloseq or TSE used as input can be pre-filtered using methods that are most convenient to the user. However, the dar package provides several functions to perform this filtering directly on the recipe object.
The step_filter_taxa function applies an arbitrary set of functions to OTUs as across-sample criteria. It takes a phyloseq object as input and returns a logical vector indicating whether each OTU passed the criteria. If the “prune” option is set to FALSE, it returns the already-trimmed version of the phyloseq object.
This function filters OTUs based on their abundance. The taxa retained in the dataset are those where the sum of their abundance is greater than the product of the total abundance and the provided threshold.
This function filters OTUs based on their prevalence. The taxa retained in the dataset are those where the prevalence is greater than the provided threshold.
This function filters OTUs based on their rarity. The taxa retained in the dataset are those where the sum of their rarity is less than the provided threshold.
This function filters OTUs based on their variance. The taxa retained in the dataset are those where the variance of their abundance is greater than the provided threshold.
The subset_taxa function subsets taxa based on their taxonomic level. The taxa retained in the dataset are those where the taxonomic level matches the provided taxa.
These functions provide a powerful and flexible way to filter and subset OTUs in phyloseq objects contained within a recipe object, making it easier to work with complex experimental data. By understanding how to use these functions effectively, you can streamline your data analysis workflow and focus on the aspects of your data that are most relevant to your research questions. The dar package offers the added convenience of performing these operations directly on the recipe object.
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