Feasibility of Interaction Specification Using generate_taxa_test Functions

[DarioS]

Let's say I have a clinical data set with Age (Young or Old) and Gender (Male or Female) factors. Can the Age*Gender interaction be specified to generate_taxa_test_single? Or is it impossible and a model formula should be written for linda function? Our idea is that Young Female patients have a special cancer microbiome that neither the Young (overall) nor Female (overall) group has.

[cafferychen777]

Hi Dario,

Thank you for your question regarding specifying interactions using the generate_taxa_test functions in the MicrobiomeStat package.

Your idea of creating a new column in the meta.dat data frame to represent the Age*Gender interaction is a viable approach. Here's how you can implement it:

1. Create a new column in meta.dat called Age_Gender_Interaction by combining the Age and Gender columns.

MicrobiomeData$meta.dat$Age_Gender_Interaction <- interaction(MicrobiomeData$meta.dat$Age, MicrobiomeData$meta.dat$Gender)

2. When running the generate_taxa_test_single function, set group.var = "Age" and include both "Gender" and "Age_Gender_Interaction" in the adj.vars parameter.

result <- generate_taxa_test_single(
  data.obj = MicrobiomeData,
  group.var = "Age",
  adj.vars = c("Gender", "Age_Gender_Interaction"),
  # other parameters...
)

By doing this, the function will perform the differential abundance analysis while considering the main effects of Age and Gender, as well as their interaction effect represented by the Age_Gender_Interaction variable.

This approach should allow you to capture the special cancer microbiome present in the Young Female group that might not be evident when considering the Young or Female groups individually.

I think this is a good solution to incorporate the interaction effect in your analysis using the MicrobiomeStat package.

Let me know if you have any further questions or if there's anything else I can assist you with.

Best regards, Caffery