To identify key microbes for a specific variable, users need to specify the taxonomy level and target variable (condition). In the Advanced Options, users can 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 will see a differential abundance analysis output table on the right-hand side. For categorical variables in DESeq2 method, rhe table will contain 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, the number of samples for each class and group size adjusted fold change will not appear. For limma method, only adjusted p-value and original p-value will appear.
Instructions:
- Select either the “DESeq2” tab or “limma” tab for the desired analysis.
- Select taxonomy level in the menu (default is genus).
- Select the target variable for differential abundance analysis.
- (Optional) Select covariates.
- (Optional) Select minimum total count cuf-off for microbes (default 500).
- (Optional) Select adjusted p-value threshold (default 0.8).
- Click the button “Run”
Running time:
- Test dataset with 30 samples and 427 microbes: 2.48s
- Test dataset with 587 samples and 203 microbes: 46.59s
