Bio flow cytometry differential analysis skills differential analysis

Version Compatibility

Reference examples tested with: diffcyt 1.22+, CATALYST 1.26+, edgeR 4.0+, limma 3.58+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

testDA_edgeR/testDS_limma are diffcyt functions operating on count/median objects from calcCounts/calcMedians; the CATALYST-integrated path is the diffcyt() wrapper on the SCE. Confirm the signature with ?diffcyt before relying on it.

Differential Analysis

"Compare cell populations between my conditions" -> Test cluster frequencies (DA) and within-cluster marker expression (DS) between groups, with the sample (not the cell) as the unit.

• R: diffcyt::diffcyt(sce, analysis_type='DA', method_DA='diffcyt-DA-edgeR', design, contrast)
• R: diffcyt(sce, analysis_type='DS', method_DS='diffcyt-DS-limma', ...)

The Single Most Important Modern Insight -- The Sample Is the Experimental Unit, Not the Cell

Tens of thousands of cells from one donor are technical PSEUDOREPLICATES, not independent observations. A per-cell test (Wilcoxon across all cells) treats them as n = cells and produces astronomically significant p-values from two mice - it is the single most common statistical sin in modern cytometry (Hurlbert 1984 Ecol Monogr 54:187; the cytometry mirror of the scRNA-seq pseudobulk lesson). The correct unit is the SAMPLE/subject: diffcyt aggregates cells to PER-SAMPLE-PER-CLUSTER counts (DA) and PER-SAMPLE-PER-CLUSTER arcsinh-MEDIANS (DS), then tests across samples with edgeR/limma/GLMM (Weber 2019 Commun Biol 2:183). Biological replication is mandatory (>= 2-3 per group); DA from a single sample per condition has no valid test. Paired with this: cluster proportions are COMPOSITIONAL (they sum to 1), so a real increase in one population mechanically forces apparent depletion in others - a source of false DA in "unchanged" clusters.

DA vs DS, and the type/state marker link

• DA (differential abundance): does a cluster's FREQUENCY differ? Clusters are defined by TYPE markers.
• DS (differential state): within a fixed-identity cluster, does a STATE marker's expression differ? State markers were withheld from clustering for exactly this test.

Method Taxonomy

Method	Citation	Mechanism	When to use
diffcyt-DA-edgeR / voom	Weber 2019 Commun Biol 2:183	edgeR/voom empirical-Bayes on per-sample counts; optional TMM	standard 2+ group with replicates (DEFAULT)
diffcyt-DA-GLMM / DS-LMM	Weber 2019	random effects in the formula	paired/repeated-measures/nested (subject random effect)
cydar	Lun 2017 Nat Methods 14:707	overlapping hyperspheres + edgeR + spatial FDR	continuum, avoid hard clusters
CITRUS	Bruggner 2014 PNAS 111:E2770	hierarchical clustering + LASSO	predictive signature, LARGE n; correlated-not-causal; largely superseded
sccomp / scCODA / DCATS	Mangiola 2023 PNAS 120:e2203828120 / Buttner 2021 Nat Commun 12:6876 / Lin 2023 Genome Biol 24:151	simplex-aware compositional models	strong compositional shift (one pop dominates); DCATS for assignment uncertainty

Run diffcyt DA and DS

Goal: Test abundance and state on a CATALYST-clustered SCE.

Approach: Build design + contrast from ei(sce); the diffcyt() wrapper uses the stored clustering. State markers are tested in DS, type markers define DA clusters.

library(CATALYST); library(diffcyt)

sce <- readRDS('sce_clustered.rds')
design   <- createDesignMatrix(ei(sce), cols_design = 'condition')
contrast <- createContrast(c(0, 1))                    # Treatment vs Control

res_DA <- diffcyt(sce, clustering_to_use = 'meta20',
                  analysis_type = 'DA', method_DA = 'diffcyt-DA-edgeR',
                  design = design, contrast = contrast)
res_DS <- diffcyt(sce, clustering_to_use = 'meta20',
                  analysis_type = 'DS', method_DS = 'diffcyt-DS-limma',
                  design = design, contrast = contrast)

library(SummarizedExperiment)
rowData(res_DA$res)        # cluster_id, logFC, p_val, p_adj (BH across clusters)

Paired / Repeated-Measures (mixed models)

Goal: Account for within-subject correlation (e.g. pre/post on the same donor).

Approach: Use a GLMM/LMM method with a random effect for subject via a formula.

formula <- createFormula(ei(sce), cols_fixed = 'condition', cols_random = 'patient_id')
res_DA  <- diffcyt(sce, clustering_to_use = 'meta20',
                   analysis_type = 'DA', method_DA = 'diffcyt-DA-GLMM',
                   formula = formula, contrast = createContrast(c(0, 1)))

Compositional Re-Check

Goal: Confirm a headline single-population shift is not inducing artifactual reciprocal depletion.

Approach: Re-test with a simplex-aware model when one cluster changes a lot or total yield differs by group.

# If a dominant population expands, the apparent depletion of others may be a simplex artifact.
# Re-test with sccomp / scCODA (reference cell type) / DCATS (assignment uncertainty)
# before reporting reciprocal depletion as independent biology.

Per-Method Failure Modes

Per-cell pseudoreplication

Trigger: Wilcoxon/t-test across all cells. Mechanism: cells aren't independent. Symptom: p ~ 1e-40 from few subjects. Fix: aggregate to per-sample summaries (diffcyt).

Compositional false DA

Trigger: one population expands strongly. Mechanism: proportions sum to 1. Symptom: significant "depletion" of unrelated clusters. Fix: TMM only when total cell abundance is NOT itself the biological signal (else it removes real signal), or a compositional method (sccomp/scCODA/DCATS); report total-yield differences.

Batch cleaned instead of modeled

Trigger: normalizing batch out then testing naively. Mechanism: over-correction removes real signal. Symptom: attenuated effects. Fix: include batch in the design; if batch == condition, no rescue - design it out.

No replicates

Trigger: 1 sample per condition. Mechanism: no error term. Symptom: uninterpretable p. Fix: require >= 2-3 biological replicates per group.

Quantitative Thresholds

Threshold	Source	Rationale
>= 2-3 biological replicates per group	Weber 2019	minimum for a valid DA/DS error term
BH FDR across clusters (and clusters x markers for DS)	diffcyt	high-resolution grids have many tests
arcsinh median as DS statistic	Nowicka 2017	robust per-cluster per-sample summary

Common Errors

Error / symptom	Cause	Solution
testDA_edgeR(sce, ...) fails	wrong signature	use the diffcyt() wrapper on the SCE, or calcCounts first
results empty	wrong clustering_to_use name	match the stored clustering id (e.g. meta20)
no DS results	state markers not flagged	set marker_class='state' in the panel
paired design ignored	used fixed-effect method	use diffcyt-DA-GLMM with a random effect

References

• Weber 2019 Commun Biol 2:183 — diffcyt (DA + DS).
• Bruggner 2014 PNAS 111(26):E2770-E2777 — CITRUS.
• Lun 2017 Nat Methods 14(7):707-709 — cydar hypersphere DA.
• Mangiola 2023 PNAS 120(33):e2203828120 — sccomp compositional analysis.
• Buttner 2021 Nat Commun 12:6876 — scCODA.
• Lin 2023 Genome Biol 24:151 — DCATS (assignment-uncertainty-aware).
• Nowicka 2017 F1000Research 6:748 — CyTOF workflow; arcsinh-median DS statistic.
• Hurlbert 1984 Ecol Monogr 54(2):187-211 — pseudoreplication.

Related Skills

• clustering-phenotyping - Cluster (type markers) before testing
• gating-analysis - Compare manually gated population frequencies
• differential-expression/de-results - Shared edgeR/limma output semantics (padj)
• differential-expression/edger-basics - The count-model engine diffcyt reuses
• experimental-design/multiple-testing - FDR across clusters and clusters x markers
• experimental-design/batch-design - Model batch in the design, don't clean it out