MaAsLin3 for Differential Abundance
Longitudinal and Complex Association Testing with MaAsLin3
MaAsLin3 (Multivariate Association with Linear Models) enables complex modeling of associations between microbial features and metadata using R’s
This means: “Model the abundance of each feature as influenced by diet, age, and sex.”This will perform the same function as adding each variable as a fixed effect in the standard GUI.Each variable is assumed to have an independent effect on the microbial feature’s abundance and/or prevalence.Expands to:This tests if the effect of diet is different between males and females.This tells MaAsLin3: “Model treatment effects, while accounting for multiple samples from the same subject.”Random effects help control for non-independence of observations (e.g., same person measured over time).Multiple random effects are supported:Note: Group/ordered predictors should not appear in random effects.Only one strata variable can be used in a model:
This means:
lme4 formula syntax. The Cosmos-Hub automatically translates user-selected variables into valid MaAsLin3 formulas, supporting a wide range of study designs. This extends beyond the typical “Fixed” and “Random” effects by implementing other variables such as:
Interaction Terms
Interaction Terms
Interaction terms model how the effect of one variable depends on another.
- Syntax:
salmonella_enterica*days_of_life - Example: Testing if the impact of
salmonella_entericadiffers across days of life.
Group Effects
Group Effects
- For ANOVA-style group testing, checks if any level differs from the others overall. This will group variables into a common cluster for analysis.
-
Syntax:
group(variable) - Example: When multiple levels of a categorical variable should be treated jointly. Note: Group effects can be combined with fixed and random effects.
Ordered Effects
Ordered Effects
For testing stepwise differences between levels, like “mild < moderate < severe”. Used for ordinal metadata where the order matters but the scale is not linear.
- Syntax:
ordered(variable) - Example: Disease stages (Stage I, II, III) or ordered diet interventions.
Note: Ordered effects can be combined similarly with fixed/random effects.
Strata Effects
Strata Effects
- For matched case-control designs, where samples are paired/matched within strata. Strata effects separate the analysis across strata of a variable and cannot be combined with other formula components.
-
Syntax:
strata(variable) - Example: Stratifying analyses by distinct clinical groups.
Basic Formula Components
1
Fixed Effects (Simple relationships)
2
Interaction Terms
3
Random Effects (Repeated Measures or Hierarchies)
4
Group, Ordered, and Strata Variables
Example
Let’s say you’re analyzing a microbiome dataset of gut samples from different diets, ages, and sexes, with repeated sampling across time:- Main effects of diet, sex, age
- Diet × sex interaction
- Random effect for individual subjects
Formula Scenarios
| Scenario | Formula Syntax | Description | |
|---|---|---|---|
| Fixed Effects | ~ diet + age | Basic effects of variables like diet or age | |
| Random Effects | `~ diet + (1 | subject_id)` | Accounts for repeated measurements within individuals |
| Interaction Terms | ~ diet * sex | Tests whether the effect of one variable depends on another | |
| Fixed + Random + Interaction | `~ diet * sex + (1 | subject_id)` | Full model with main, interaction, and repeated effects |
| Group Effects | ~ group(diet) | Treats all levels of a categorical variable jointly | |
| Ordered Effects | ~ ordered(stage) | Tests stepwise effects in ordinal variables (e.g., Stage I < II < III) | |
| Strata Effects (Exclusive) | ~ strata(pair_id) | For matched case-control studies (cannot be combined with other effects) |
Combining Effect Types
Effect types can often be mixed—except strata, which must stand alone:| Combination | Example | |
|---|---|---|
| Fixed + Random | `~ age + (1 | subject_id)` |
| Fixed + Interaction | ~ age + treatment + age:treatment | |
| Fixed + Group | ~ group(treatment) + age | |
| Fixed + Ordered | ~ ordered(dose_level) + age | |
| Fixed + Random + Group | `~ group(treatment) + age + (1 | subject_id)` |
| Fixed + Random + Interaction | `~ treatment * timepoint + (1 | subject_id)` |
| Strata Only | ~ strata(pair_id) |
Syntax Notes
- Interaction Terms:
var1:var2models whether the effect ofvar1changes acrossvar2. - Group Effects:
group(var)aggregates all levels for joint testing. - Ordered Effects:
ordered(var)evaluates progression across ordered categories. - Strata Effects:
strata(var)conditions the analysis on matched groups (e.g., case-control pairs) and cannot be combined with other effects.
Cosmos-Hub Smart Formula Handling
- Automatic parsing: Your selections in the Cosmos-Hub interface are converted into the correct formula string.
- Validation: Invalid or unsupported combinations (e.g., using
stratawithrandom) are flagged before submission. - Support for prevalence and abundance.
Key Considerations
- Strata must be the only term in the formula—no other variables allowed.
- Group and ordered variables behave like fixed effects but are tested differently.
- Interaction models are essential for longitudinal or treatment-timepoint designs.