To identify key microbes for a specific variable, users need to specify the taxonomy level and target variable (condition). In the Advanced Options, users could also add covariates to the linear model, add a minimum count cut-off (all features with average read number less than this cut-off will be filtered), and a adjusted p-value cut-off.
After click the “Run” button, users would see a differential abundance analysis output table on the right-hand side. For categorical variables in DESeq2 method, we show the feature name, adjusted p-value, original p-value, log2 fold change, number of samples for each class, feature prevalance, and group size adjusted fold change. For numeric variables in DESeq2 method, number of samples for each class and group size adjusted fold change won’t show up. For limma method, only adjusted p-value and original p-value will show up.