Tests which individual taxa differ between groups on an amplicon ASV/feature table (phyloseq) using compositionally-aware methods - ALDEx2 (Dirichlet-MC CLR, conservative), ANCOM-BC2/ANCOMBC (sampling-fraction bias correction, structural zeros, passed_ss, default p_adj_method=holm), MaAsLin2/MaAsLin3 (multivariable GLM, random effects, prevalence/abundance split), LinDA (CLR mixed-model regression), ZicoSeq (permutation FDR), LEfSe, and q2-composition ancombc. Covers why the hit list depends more on the DA tool than the biology (Nearing benchmark) so the deliverable is a CONSENSUS of >=2 tools, why a relative change is not absolute without a load anchor, the prevalence-filter knob, BH/FDR plus an effect-size floor, and why DESeq2/edgeR misfire here. Use when finding differentially abundant taxa, handling covariates or longitudinal designs, or choosing a method. Whole-community diversity -> diversity-analysis; shotgun DA -> metagenomics/metagenome-visualization; CoDA theory -> metagenomics/abundance-estimation
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Tests which individual taxa differ between groups on an amplicon ASV/feature table (phyloseq) using compositionally-aware methods - ALDEx2 (Dirichlet-MC CLR, conservative), ANCOM-BC2/ANCOMBC (sampling-fraction bias correction, structural zeros, passed_ss, default p_adj_method=holm), MaAsLin2/MaAsLin3 (multivariable GLM, random effects, prevalence/abundance split), LinDA (CLR mixed-model regression), ZicoSeq (permutation FDR), LEfSe, and q2-composition ancombc. Covers why the hit list depends more on the DA tool than the biology (Nearing benchmark) so the deliverable is a CONSENSUS of >=2 tools, why a relative change is not absolute without a load anchor, the prevalence-filter knob, BH/FDR plus an effect-size floor, and why DESeq2/edgeR misfire here. Use when finding differentially abundant taxa, handling covariates or longitudinal designs, or choosing a method. Whole-community diversity -> diversity-analysis; shotgun DA -> metagenomics/metagenome-visualization; CoDA theory -> metagenomics/abundance-estimation
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('<pkg>') then ?function_name to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
ANCOM-BC2 changed argument names between ancombc() and ancombc2(), and its default p_adj_method is holm, not BH - confirm both against the installed version. The MaAsLin3 maaslin3() API differs from MaAsLin2's Maaslin2().
Differential Abundance Testing
"Find which taxa differ between my groups" -> Run two or more compositionally-aware DA tools and report their consensus - because the significant-taxa list is a property of the tool as much as of the sample, and a relative-abundance change is not an absolute change.
R: ALDEx2::aldex(counts, conds, test='t', effect=TRUE, denom='all') then a second tool (ANCOMBC::ancombc2() or MicrobiomeStat::linda())
Scope: per-taxon DA on an amplicon feature table. Whole-community alpha/beta/PERMANOVA -> diversity-analysis. Shotgun profiler-table DA -> metagenomics/metagenome-visualization. Shared compositional/closure/CLR/zero theory -> metagenomics/abundance-estimation. Collapse ASVs to genus/species first -> taxonomy-assignment. QIIME2 CLI route -> qiime2-workflow.
The Single Most Important Modern Insight -- Which Taxa Are "Significant" Depends More on the Tool Than on the Biology
Run ALDEx2, ANCOM-BC2, MaAsLin2, and LinDA on the same ASV table and the four significant-taxa lists overlap but disagree (Nearing 2022 Nat Commun 13:342, across 38 datasets). So the deliverable is NOT "the differential taxa" - it is the CONSENSUS of >=2 compositionally-aware tools, every tool NAMED: the intersection is high-confidence, the union is exploratory, and a single-tool hit is tentative. Picking the tool with the prettiest volcano is p-hacking by software (uncorrected multiplicity hidden in the method menu). Three corollaries:
A relative-abundance increase is not an absolute increase. Microbiome counts are compositional - the sequencer fixes the total, so one taxon blooming forces every other taxon's proportion down (the blooming-taxon illusion). "Taxon X increased" is a statement about its SHARE unless an external load anchor (spike-in / flow cytometry / qPCR, see metagenomics/abundance-estimation) or MaAsLin3's absolute-abundance mode licenses an absolute claim.
Uncorrected Wilcoxon/t-test on raw relative abundances is wrong twice in one line - closure (reference-frame) AND multiple testing. But a BH-corrected simple test, honestly labelled as relative, can replicate BETTER than a sophisticated model (Pelto 2025): the forbidden thing is the uncorrected, closure-blind form, not simple tests per se.
There is no settled best tool. The benchmarks optimize different criteria, so they rank tools differently. Consensus-of-tools is the only stance that survives all of them.
The Benchmark Landscape (no settled winner)
Benchmark
Optimized for
Verdict
Nearing 2022 Nat Commun 13:342
cross-method consistency
ALDEx2 + ANCOM-II most consistent and most conservative; LEfSe/edgeR flag far more, agree less
Yang & Chen 2022 Microbiome 10:130
FDR-power balance
ZicoSeq / LinDA / ANCOM-BC-family best
Yang & Chen 2023 Brief Bioinform 24:bbac607
correlated (repeated-measures) designs
use a mixed-model-capable tool (LinDA, MaAsLin2, ANCOM-BC2)
Pelto 2025 Brief Bioinform 26(2):bbaf130
cross-study replicability
elementary BH-corrected methods most replicable; ANCOM-BC2 worst
Report the disagreement AS the result; verify current best practice against the latest tool docs rather than hard-coding one method.
Tool Taxonomy
Tool
Citation
Mechanism / role
When
ALDEx2
Fernandes 2014 Microbiome 2:15
Dirichlet Monte-Carlo posterior + CLR; tests each draw; reports expected effect + BH-adjusted p
conservative two-group anchor; small-to-moderate n
covariates; non-parametric permutation p; strong FP control
LEfSe
Segata 2011 Genome Biol 12:R60
Kruskal-Wallis + LDA effect size
exploratory biomarker discovery; NOT a formal FDR-controlled test
DESeq2
Love 2014 Genome Biol 15:550
RNA-seq median-of-ratios size factor
caveat only; geometric-mean reference dies on sparse zero-heavy tables
Decision Tree by Scenario
Scenario
Recommended
Why
Two groups, want a trustworthy conservative anchor
ALDEx2
Dirichlet-MC + CLR; most reproducible/conservative (Nearing); gate on effect size
Need interpretable LFC + CI, structural zeros, multi-group
ANCOM-BC2
models and corrects per-sample sampling fraction; global/pairwise/Dunnett/trend; passed_ss
Large cohort, covariates, speed, mixed model
LinDA
CLR regression + bias mode; asymptotic FDR; fast; random effects in the formula
Covariates + permutation-grounded non-parametric p
ZicoSeq
reference-taxa frame + permutation FDR
Longitudinal / many covariates / flexible GLM
MaAsLin2
fixed_effects + random_effects; normalization/transform menu
Prevalence-vs-abundance separation or absolute abundance
MaAsLin3
logistic prevalence model + abundance model; load-data hook
Inside a QIIME2 CLI pipeline
qiime composition ancombc + tabulate/da-barplot
native artifact flow (v1 ANCOM-BC; go to R for v2 passed_ss/multi-group) -> qiime2-workflow
Repeated / paired samples
any tool above WITH a random effect
ignoring subject structure is pseudo-replication
ALWAYS
run >=2 of the above, report the consensus
tool choice drives the hit list more than biology (Nearing 2022)
Shotgun species table, not amplicon
-> metagenomics/metagenome-visualization
same CoDA theory; different upstream pipeline
Uncorrected t-test/Wilcoxon on TSS proportions
DO NOT
closure biases the test and there is no FDR control
Filter Before Testing (a modeling knob, not housekeeping)
Goal: Drop rare features before testing so the BH denominator is not crushed and log/CLR transforms are well-behaved.
Approach: Keep features present in at least 10-25% of samples (and optionally a mean-abundance floor); declare the threshold and confirm the headline result is not knife-edge-sensitive to it. Every tool exposes this (prv_cut, min_prevalence, prev.filter).
library(phyloseq)
ps <- readRDS('phyloseq_object.rds')# prv_cut 0.10: a feature must appear in >= 10% of samples; raising to 0.25 removes more tests# (smaller BH correction, more power on survivors) but discards rare-but-real taxa - a declared choice
keep <- filter_taxa(ps,function(x)sum(x >0)>=0.10* nsamples(ps),TRUE)
ALDEx2: The Conservative Floor of the Consensus
Goal: Identify taxa that differ between two groups while propagating the sampling uncertainty of low-count features.
Approach: Draw mc.samples Monte-Carlo instances from a Dirichlet posterior of the counts (this IS the zero handling - no explicit pseudocount), CLR-transform each instance against the geometric mean of all features (denom='all'), run the test on every draw, and report the EXPECTED effect size and BH-adjusted p over the draws.
library(ALDEx2)
counts <- as.matrix(otu_table(ps))# integer counts, taxa in ROWSif(!taxa_are_rows(ps)) counts <- t(counts)
groups <-as.character(sample_data(ps)$Group)# mc.samples 128: standard Monte-Carlo draws; 256+ for publication (more stable expected p)
res <- aldex(counts, groups, mc.samples =128, test ='t', effect =TRUE, denom ='all')# we.eBH = Welch expected BH-adjusted p (report this, NOT we.ep); wi.eBH = Wilcoxon equivalent# effect = median standardized effect = median(diff.btw / max(diff.win)); the primary decision variable
hits <- res[res$we.eBH <0.05&abs(res$effect)>1,]# q AND effect floor (Gloor: gate on effect, not p alone)
Gate on effect size AND q, not p alone: with large n trivially small CLR differences become "significant," and Gloor's own guidance is that |effect| > 1 is a strong ~2-SD signal. For >2 groups use aldex.kw(); for covariates the aldex.glm() + model.matrix route works but ALDEx2 is weakest here - prefer ANCOM-BC2/LinDA/MaAsLin2 for serious covariate or random-effect modeling.
ANCOM-BC2: Bias-Corrected LFC With a Sensitivity Safeguard
Goal: Estimate an interpretable bias-corrected log-fold-change per taxon, with covariate adjustment, structural-zero handling, and a flag for hits that are hostage to the pseudo-count.
Approach: Model log(observed count) as a function of covariates, estimate each sample's log sampling fraction as an offset and subtract it, then refit across a range of pseudo-counts and record how often each q-value flips (passed_ss).
library(ANCOMBC)
out <- ancombc2(data = ps, fix_formula ='Group + Age + Sex',
rand_formula =NULL,# '(1 | SubjectID)' for repeated measures - see Failure Modes
p_adj_method ='BH',# DEFAULT is 'holm'; set 'BH' deliberately for FDR
prv_cut =0.10, lib_cut =1000,
group ='Group', struc_zero =TRUE, pseudo_sens =TRUE,
global =FALSE, pairwise =FALSE, n_cl =2)
res <- out$res
# a confident hit is BOTH significant AND robust to the pseudo-count. ANCOM-BC2 suffixes the# diff_/passed_ss_ columns with the literal model-matrix coefficient (variable + factor level,# verbatim case, e.g. 'Grouptreated') - match it by pattern rather than hard-coding the case.
dcol <- grep('^diff_Group',names(res), value =TRUE)[1]
robust <- res[res[[dcol]]& res[[sub('^diff_','passed_ss_', dcol)]],]
passed_ss is the most valuable ANCOM-BC2-specific feature: a CLR/log model on sparse data is hostage to the zero-replacement constant, and passed_ss quantifies that per taxon. A hit with passed_ss == FALSE depends on the arbitrary pseudo-count - do not report it as confident. For >2 groups set global=TRUE (omnibus), pairwise=TRUE (mdFDR-controlled pairs), dunnet=TRUE, or trend=TRUE; results land in out$res_global/res_pair/res_dunn/res_trend.
LinDA: Fast CLR Regression With Native Mixed Models
Goal: Get FDR-controlled log2-fold-changes on a large cohort, including repeated-measures designs, without Monte-Carlo or EM cost.
Approach: Fit ordinary linear regression on the CLR-transformed table covariate by covariate, estimate the compositional bias as the mode of the per-feature coefficients and subtract it; a random effect in the formula makes it a linear mixed model.
library(MicrobiomeStat)
otu <- as.data.frame(otu_table(ps)); if(!taxa_are_rows(ps)) otu <- t(otu)
meta <- as.data.frame(sample_data(ps))
fit <- linda(feature.dat = otu, meta.dat = meta,
formula ='~ Group + Age + (1 | SubjectID)',# random effect -> mixed model
feature.dat.type ='count', prev.filter =0.10, alpha =0.05)
fit$output[[1]]# names(fit$output) are the model-matrix coefficient columns (e.g. 'Grouptreated' - the factor level keeps its case); per-feature: log2FoldChange, lfcSE, stat, pvalue, padj, reject
LinDA is the natural fast modern entry in a consensus panel and the cleanest route to mixed models. Yang & Chen rate it among the best FDR-power trade-offs.
MaAsLin2 / MaAsLin3 and ZicoSeq (the rest of the panel)
Goal: Fit covariate-rich or longitudinal differential-abundance models, or add a permutation-based panel member, when ALDEx2/ANCOM-BC2/LinDA do not cover the design.
Approach: Use MaAsLin2/3 for multivariable GLMs with random effects, or ZicoSeq for a non-parametric permutation-FDR test against empirically selected reference taxa.
MaAsLin2 fits a flexible per-feature GLM; its package DEFAULT is TSS + LOG + LM (not CLR), and random_effects is the canonical route for longitudinal designs. NOTE the orientation gotcha: it expects features in COLUMNS, samples in rows.
MaAsLin3 (maaslin3()) splits each feature into an abundance model (level when present) and a logistic prevalence model (present/absent) tested jointly, and can ingest total-load measurements for absolute-abundance inference. ZicoSeq (GUniFrac::ZicoSeq()) winsorizes, posterior-samples, normalizes against empirically selected reference taxa, and returns permutation FDR (zc$p.adj.fdr) - a non-parametric panel member that accepts covariates via adj.name.
Consensus: Intersect the Tools
Goal: Convert two or more per-tool hit sets into a confidence-graded result instead of one tool's answer.
Approach: Collect the significant feature SETS (BH within each tool), then report the intersection as high-confidence, the union as exploratory, and tabulate, per taxon, how many of N tools agree and which ones. Never pool p-values across tools.
sig_aldex <- rownames(res)[res$we.eBH <0.05&abs(res$effect)>1]
sig_linda <- rownames(fit$output[[1]])[fit$output[[1]]$reject]# [[1]] = the group coefficient (named 'Grouptreated')
confident <- intersect(sig_aldex, sig_linda)# high-confidence
exploratory <- union(sig_aldex, sig_linda)# report with the tool that found each
Per-Method Failure Modes
Cherry-picking the tool with the prettiest result
Trigger: running several tools and reporting only the one(s) that flag the favored taxon. Mechanism: that is uncorrected multiplicity hidden in the method menu (p-hacking by software). Symptom: "the recommended method found X" with no mention of the tools that disagreed. Fix: decide the panel a priori, report ALL tools, intersect for confident hits, disclose disagreement.
Uncorrected Wilcoxon/t-test on relative abundances
Trigger: a per-taxon Wilcoxon/t-test on TSS proportions with no FDR correction. Mechanism: closure makes a naive test call every taxon "decreased" when one blooms, and hundreds of uncorrected tests inflate false positives. Symptom: dozens of "significant" taxa, all in the same direction, no q-values. Fix: use a CoDA/reference-frame tool; if a simple test is used, BH-correct it and label the comparison as relative (Pelto 2025).
Pseudo-replication of repeated measures
Trigger: longitudinal/paired samples treated as independent rows. Mechanism: fewer independent units than rows inflates significance. Symptom: implausibly small p-values on a small subject count. Fix: a random effect - ANCOM-BC2 rand_formula='(1|SubjectID)', MaAsLin2 random_effects='SubjectID', LinDA (1|SubjectID) in the formula. Cross-check ANCOM-BC2 mixed-model output against LinDA/MaAsLin2 (GitHub issue #111 reported rand_formula correctness problems in some versions).
Prevalence filter set blindly
Trigger: an undeclared prevalence cut, or none at all. Mechanism: the cut decides which taxa are even tested and thus the BH landscape - it is a modeling choice. Symptom: the hit list changes materially between prv_cut=0.1 and 0.25. Fix: declare and justify the threshold; confirm the headline result survives moving it.
Relative change reported as absolute
Trigger: "taxon X doubled" from a closed table with no load data. Mechanism: one taxon blooming compresses every other proportion. Symptom: whole-community "depletion" that is really one taxon rising. Fix: anchor to load (spike-in/flow/qPCR) or MaAsLin3 absolute mode; otherwise state the claim is relative.
DESeq2/edgeR on a sparse 16S table
Trigger: RNA-seq median-of-ratios / TMM on a zero-heavy ASV table. Mechanism: the geometric-mean size-factor reference collapses on zeros and the "most features unchanged" assumption is violated. Symptom: degenerate size factors, errors, or inflated hit counts that disagree with CoDA tools (Nearing). Fix: use a compositional tool; if DESeq2 is unavoidable, the poscounts estimator is the minimum mitigation - present as a caveat, not a recipe.
ANCOM-BC2 hit held hostage by the pseudo-count
Trigger: reporting diff_* == TRUE without checking passed_ss_*. Mechanism: significance depends on the arbitrary zero-replacement constant. Symptom: a hit that vanishes when the pseudo-count changes. Fix: require diff_* & passed_ss_* for a confident call.
rare features carry little information and crush the BH denominator; declare the value and test sensitivity
BH q <= 0.05 across taxa, within each tool
Benjamini-Hochberg 1995 JRSS B 57:289
hundreds-thousands of features make uncorrected p meaningless; do not pool p across tools
ALDEx2 `
effect
> 1` (with q <= 0.05)
ALDEx2 mc.samples = 128 (256+ for publication)
Fernandes 2014 Microbiome 2:15
Monte-Carlo draws; more draws stabilize the expected p
ANCOM-BC2 passed_ss == TRUE required
Lin & Peddada 2024 Nat Methods 21:83
flags hits whose significance is hostage to the pseudo-count
Consensus of >=2 compositionally-aware tools
Nearing 2022 Nat Commun 13:342
tool choice drives the hit list more than biology; intersection = confident
ZicoSeq permutations perm.no >= 99
Yang & Chen 2022 Microbiome 10:130
permutation FDR resolution; raise for finer tail p
Common Errors
Error / symptom
Cause
Solution
ALDEx2 returns NA effects / errors
proportions or non-integer matrix passed
feed integer COUNTS with taxa in rows
passed_ss column missing
pseudo_sens = FALSE
set pseudo_sens = TRUE (the default)
Far fewer hits than expected
ANCOM-BC2 p_adj_method left at holm
set p_adj_method = 'BH' deliberately if FDR is wanted
MaAsLin2 finds nothing / orientation error
features in rows, not columns
transpose so samples are rows, features columns
Mixed-model hits disagree across tools
rand_formula correctness varies by version
cross-check ANCOM-BC2 against LinDA/MaAsLin2
Tools disagree on the hit list
normal - tool choice drives results
report the consensus and the disagreement, do not cherry-pick
Many "depleted" taxa in a host/plant sample
host mitochondria/chloroplast 16S inflates the table
filter Mitochondria/Chloroplast features (see taxonomy-assignment) before DA
Contaminant ASVs among the hits (low-biomass)
reagent kitome not removed before DA
run decontam upstream with negative controls (amplicon-processing; metagenomics/contamination-controls)
References
Fernandes AD, Reid JNS, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. 2014. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2:15.
Gloor GB, Macklaim JM, Fernandes AD. 2016. Displaying variation in large datasets: plotting a visual summary of effect sizes. J Comput Graph Stat 25:971-979.
Lin H, Peddada SD. 2020. Analysis of compositions of microbiomes with bias correction (ANCOM-BC). Nat Commun 11:3514.
Lin H, Peddada SD. 2024. Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures (ANCOM-BC2). Nat Methods 21:83-91.
Mallick H, Rahnavard A, McIver LJ, et al. 2021. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 17:e1009442.
Nickols WA, Kuntz T, Shen J, et al. 2026. MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery. Nat Methods 23:554-564.
Zhou H, He K, Chen J, Zhang X. 2022. LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol 23:95.
Yang L, Chen J. 2022. A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions. Microbiome 10:130.
Yang L, Chen J. 2023. Benchmarking differential abundance analysis methods for correlated microbiome sequencing data. Brief Bioinform 24:bbac607.
Pelto J, Auranen K, Kujala JV, Lahti L. 2025. Elementary methods provide more replicable results in microbial differential abundance analysis. Brief Bioinform 26(2):bbaf130.
Nearing JT, Douglas GM, Hayes MG, et al. 2022. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun 13:342.
Segata N, Izard J, Waldron L, et al. 2011. Metagenomic biomarker discovery and explanation. Genome Biol 12:R60.
Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550.
Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B 57:289-300.
Related Skills
diversity-analysis - Whole-community alpha/beta/PERMANOVA; answer "do the communities differ" before "which taxa differ"
taxonomy-assignment - Collapse ASVs to genus/species before per-taxon testing
amplicon-processing - Produces the ASV feature table tested here
qiime2-workflow - The qiime composition ancombc CLI route
metagenomics/abundance-estimation - Shared compositional/closure/CLR/zero/load-anchor theory
metagenomics/metagenome-visualization - The same DA mechanics on shotgun profiler tables
experimental-design/multiple-testing - FDR control and multiplicity across taxa
