Differential abundance analysis (DAA)
Sunday, August 24, 2025
Differential abundance analysis (DAA)
- Goal: identify taxa with different abundance between groups
- Approaches:
- Classical statistical tests
- Microbiome-specific methods
- Challenges: compositionality, sparsity, multiple testing
Elementary methods provide more replicable results in microbial differential abundance analysis
- Relative abundances with a Wilcoxon test
- Log-transformed relative abundances with a t-test
- Presence/absence of taxa with logistic regression
Pelto et al. 2025 show that simple methods often give more replicable results.
Classical tests are linear models
- Relative abundances with a Wilcoxon test
- Non-parametric, but can be expressed as a rank-based linear model
\[
\mathrm{Rank}(\mathrm{Relative\ abundance}) \sim \mathrm{Group}
\]
- Log-transformed relative abundances with a t-test
\[
\log(\mathrm{Relative\ abundance}) \sim \mathrm{Group}
\]
- Presence/absence of taxa with logistic regression
- Generalized linear model with logit link
\[
\mathrm{Presence} \sim \mathrm{Group}, \quad \mathrm{Presence} \in \{0,1\}
\]
Wilcoxon vs t-test
- Wilcoxon: compares medians, robust to outliers, non-parametric
- t-test: compares means, assumes normality, sensitive to outliers
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library(maaslin3)
maaslin3_out <- maaslin3(
input_data = tse,
formula = "~ patient_status",
normalization = "TSS",
transform = "LOG",
)
