In a nutshell
- Select a rank (e.g. Genus), set "Regress by" to "Taxa vs Envp" and press "Set Mode",
followed by "Run Analysis". The displayed table shows the effect of the environmental
variables on the abundance of taxa. For each taxon a regression model is fit
of the form:
abundance of taxon = env1 + env2 + env3 ...,
where env1, env2, ... are
the environmental variables. For each taxon and each
environmental variable a p-value is shown, indicating if the variable impacts
the abundance of the respective taxon. Select an environmental variable in the header of the displayed
table to sort the p-values. If you have paired data, select the "Paired" checkbox
and press "Run Analysis". In the case of paired data, a linear mixed effects
model is fit of the form:
abundance of taxon = env1 + env2 + env3 ... + pair1 + pair2 + ...,
where the environmental variables are included as fixed effects and the pairs as
random effect.
- Use the Taxa drop-down menu to select a specific taxon or OTU. Press "Run Analysis"
to analyze the correlation between the abundance of the selected taxon/OTU and the
environmental variables.
- Next, do a regression analysis to identify complex associations between
environmental variables and the community diversity. First, set Taxa to "All" and
"Regress by" to "Diversity vs Envp" and press "Set Mode". Next set the Index
to either "Shannon" or "Richness" and press "Run Analysis". In the
generated figure, first the p-values of the
regression model are displayed. Additionally,
for each environmental variable a two scatter-plots are shown. The p-values dispalyed
in the scatter plot indicate if the Pearson correlation between the environmental
variable and the diversity index is significant. Select the "Paired" checkbox for
paired data.
- You can further do a regression analysis to identify environmental variables
impacting the global community composition. Set "Regress by" to "Distance vs Envp"
and press "Set Mode". Next, set Taxa to "All", "Distance Method"
to "Jaccard" and press "Run Analysis".
- Calculate multiple linear regression model to explain the grouping of samples
as specified in the meta data file.
Set "Regress by" to Group and press "Set Mode". Select different groups for
condition A and condition B. Set Taxa to "All". Press "Run Analysis".