omics.co.in logo omics.co.in

Chapter 5: Differential Abundance Testing: ANCOM-BC & MaAsLin2

1 min read • Verified October 2026 • Peer-Reviewed Learning Resource
Tested Stacks: GATK 4.6.1 Samtools 1.21 Nextflow 24.10 Seurat v5.1 Python 3.12
Prerequisites: Basic Linux CLI & Sequencing Formats
📚 Chapter 5 of 13 0% Course Completed

Chapter 5: Differential Abundance Testing: ANCOM-BC & MaAsLin2

Sequencing instruments have fixed throughput capacities. As a result, microbiome data is strictly compositional: total read counts sum to an arbitrary library size, constraining data to a mathematical simplex. If one bacterial species blooms, the relative abundance of all other species artificially drops, even if their absolute biological biomass remains constant.

The Fallacy of Standard T-Tests & DESeq2

Applying standard parametric t-tests or naive RNA-seq tools to compositional microbiome tables produces massive false-positive rates (>30–50%). Compositionally aware methods like ANCOM-BC2 or ALDEx2 explicitly correct for sampling fraction bias.

1. Differential Abundance with ANCOM-BC2 in R

library(ANCOMBC)

# Run ANCOM-BC2 with bias correction
out <- ancombc2(
    data = physeq_obj,
    tax_level = "Genus",
    fix_formula = "Treatment + Age + Sex",
    p_adj_method = "holm",
    pseudo_sens = TRUE,
    prv_cut = 0.10,          # Filter genera present in <10% of samples
    group = "Treatment"
)

# Extract significantly perturbed genera
res <- out$res
sig_taxa <- res[res$diff_TreatmentTreated == TRUE, c("taxon", "lfc_TreatmentTreated", "q_TreatmentTreated")]
print(head(sig_taxa))

2. Multivariable Association Modeling with MaAsLin2

MaAsLin2 (Microbiome Multivariable Association with Linear Models) tests associations between microbial features and complex clinical phenotypes while controlling for confounding clinical covariates (e.g. antibiotic usage, BMI, dietary factors).

⚠️

Common Pitfalls & Troubleshooting Checklist

  • Java Heap Exhaustion (GATK / Picard): If processes terminate with java.lang.OutOfMemoryError, set your environment variable explicitly: export _JAVA_OPTIONS="-Xmx16g -Djava.io.tmpdir=/tmp".
  • Index Timestamp Mismatch: If tools complain that a BAM index is missing or out of date, regenerate with samtools index -@ 4 sample.bam.
  • Conda Channel Conflicts: Avoid ABI incompatibilities between bioconda and conda-forge by setting strict priority: conda config --set channel_priority strict.
  • Chromosome Naming Contamination: Always verify whether reference FASTA headers use chr1 or 1; divergent naming causes silent alignment drops.
Was this guide helpful to your research?
Help us maintain 100% accurate, peer-reviewed bioinformatics standards.
📝 Propose Edit on GitHub ↗