Dominant Community State Types: From Normalization to a Gut Demonstration

In linf: L-Infinity Normalization and Dominant Community State Types

```{css, echo=FALSE} table td, table th { white-space: nowrap; }

```r
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width = 7,
  fig.height = 5
)
library(linf)

The linf package implements L-infinity normalization and Dominant Community State Types (dCSTs) for compositional data. This vignette demonstrates the complete workflow in two parts: first a quick-start with toy data to introduce the core functions, then a real-data analysis of gut microbiome samples from the American Gut Project showing how to inspect dominant and subdominant community structure.

Part 1: Quick Start with Toy Data

L-infinity normalization

L-infinity normalization scales each row of a count matrix by its maximum value, projecting samples onto the L-infinity unit ball. Every normalized row has a maximum of exactly 1.

set.seed(1)
S.counts <- matrix(
  rpois(30, lambda = 5),
  nrow = 10, ncol = 3,
  dimnames = list(paste0("s", 1:10), c("Taxon_A", "Taxon_B", "Taxon_C"))
)

Z <- normalize.linf(S.counts)
apply(Z, 1, max)   # all 1

Dominance sample sets

Each sample is assigned to its dominant feature: the column achieving the within-sample maximum. Samples with the same dominant feature form a depth-1 dominance sample set.

cells <- linf.cells(Z)
table(cells$label)

Truncated dCSTs

linf.csts() groups samples by dominant feature and collapses small groups (below threshold n0) into a RARE_DOMINANT bucket. This produces Dominant Community State Types (dCSTs).

A <- matrix(c(5, 1, 0), nrow = 6, ncol = 3, byrow = TRUE)
B <- matrix(c(1, 5, 0), nrow = 2, ncol = 3, byrow = TRUE)
C <- matrix(c(1, 0, 5), nrow = 2, ncol = 3, byrow = TRUE)
S <- rbind(A, B, C)
S <- sweep(S, 1, rowSums(S), "/")
colnames(S) <- c("Dom1", "Dom2", "Dom3")

res <- linf.csts(S, n0 = 5)
table(res$cell.label, useNA = "ifany")

Only Dom1 (6 samples) meets the n0 = 5 threshold. Dom2 and Dom3 are collapsed into RARE_DOMINANT.

Part 2: Gut Microbiome dCST Demonstration

Background

Single-species dominance is common in some microbial ecosystems and less common in others. dCSTs provide a deterministic way to summarize the dominant feature and then refine large groups by subdominant features. The gut example below demonstrates those mechanics without treating the selected package subset as an epidemiologic sample.

The data

The agp_gut dataset bundled with this package contains r nrow(agp_gut$counts) gut microbiome samples from the American Gut Project (PRJEB11419), a citizen-science 16S rRNA V4 survey. The dataset is a dCST-stratified demonstration subset that includes all samples in four selected uncommon dCSTs and fills the remaining slots with a seed-42 simple random sample from the eligible background. Phenotypes are joined only after selection and do not influence membership. Because inclusion probabilities differ by dCST, it is not a probability sample of the underlying cohort; phenotype frequencies, effect sizes, and hypothesis tests must not be interpreted as population results.

data(agp_gut)
str(agp_gut, max.level = 1)

dim(agp_gut$counts)   # samples x taxa

Metadata includes self-reported health conditions parsed from the AGP questionnaire:

disease_cols <- c("IBS", "IBD", "Obesity", "Cardiovascular_disease",
                  "Autoimmune", "Acid_reflux")
sapply(disease_cols, function(col) sum(agp_gut$meta[[col]], na.rm = TRUE))

Step 1: Quality filtering

filter.asv() removes low-depth samples and rare taxa in one call. We require at least 1,000 reads per sample and taxa present in at least 5% of samples.

filt <- filter.asv(agp_gut$counts, min.lib = 1000, prev.prop = 0.05,
                   min.count = 2)
dim(filt$counts)
dim(filt$rel)

Step 2: L-infinity normalization

M <- normalize.linf(filt$counts)

# Verify: every row max is 1
stopifnot(all(abs(apply(M, 1, max) - 1) < 1e-10))

Step 3: Depth-1 dCSTs

csts <- linf.csts(M, n0 = 30)

# dCST size distribution
sort(table(csts$cell.label), decreasing = TRUE)

The landscape is dominated by Bacteroides and Escherichia-Shigella, with Prevotella, Faecalibacterium, and several smaller dCSTs forming the tail. The RARE_DOMINANT bucket collects samples dominated by taxa too infrequent to form their own dCST at this threshold.

Note on Escherichia-Shigella: this genus is known to be inflated in 16S V4 data due to primer cross-reactivity. Its high prevalence should be interpreted with caution.

Step 4: Depth-2 refinement

refine.linf.csts() subdivides large dCSTs by their subdominant species. For instance, a Bacteroides-dominated sample might be further classified by whether its second-most-abundant taxon is Faecalibacterium or Lachnospiraceae. This captures co-dominance patterns that are especially important in the diverse gut environment.

csts2 <- refine.linf.csts(M, csts, n0 = 30)

# Show depth-2 dCSTs with >= 20 samples
tab2 <- sort(table(csts2$cell.label), decreasing = TRUE)
tab2[tab2 >= 20]

Dominance strength

A useful diagnostic: the relative abundance of the dominant species in each sample. In the gut, most samples have modest dominance (< 50%), consistent with the diverse nature of the ecosystem.

rel <- filt$rel
dom_strength <- apply(rel, 1, max)
hist(dom_strength, breaks = 50, col = "#2ecc71", border = "white",
     main = "Dominance Strength in Gut Microbiome",
     xlab = "Relative abundance of dominant species",
     ylab = "Number of samples")
abline(v = 0.5, col = "red", lty = 2, lwd = 2)
legend("topright", "50% dominance", col = "red", lty = 2, lwd = 2, bty = "n")

dCST size distribution

tab1 <- sort(table(csts$cell.label), decreasing = TRUE)
par(mar = c(10, 4, 3, 1))
bp <- barplot(tab1,
        col = ifelse(names(tab1) == "RARE_DOMINANT", "#e74c3c", "#3498db"),
        las = 2, cex.names = 0.7,
        ylab = "Number of samples",
        main = "Gut dCST Size Distribution (depth 1)")
text(bp, tab1 + 5, labels = tab1, cex = 0.7, pos = 3)

Phenotype metadata

The bundle retains selected self-reported phenotype fields so that users can inspect the object structure and practice data alignment. The following table is purely descriptive; dCST-stratified selection precludes population prevalence estimation or treating association calculations as cohort results.

meta <- agp_gut$meta[match(rownames(M), agp_gut$meta$Run), ]
stopifnot(all(meta$Run == rownames(M)))

conditions <- c("IBS", "IBD", "Obesity", "Cardiovascular_disease",
                "Autoimmune", "Acid_reflux", "Lung_disease")
phenotype_summary <- data.frame(
  Condition = conditions,
  Recorded_cases = vapply(
    conditions,
    function(x) sum(meta[[x]] == 1, na.rm = TRUE),
    integer(1)
  ),
  Non_missing = vapply(
    conditions,
    function(x) sum(!is.na(meta[[x]])),
    integer(1)
  )
)
knitr::kable(
  phenotype_summary,
  caption = "Recorded phenotypes in the selected demonstration subset"
)

Limitations

This analysis has several important caveats:

• Self-reported metadata. AGP health conditions are self-reported by citizen-science participants, with unknown accuracy.
• 16S resolution. The Escherichia-Shigella inflated abundance is a known V4 artifact. Shotgun metagenomics would resolve this.
• dCST-stratified demonstration subset. Four uncommon dCSTs are included exhaustively and the eligible background is sampled with seed 42. Phenotypes do not affect membership, but differing inclusion probabilities mean the object must not be used for population prevalence estimates, effect-size estimation, or association testing.

The repository also contains the source for a separate 5,000-sample companion analysis. It is not installed as a package vignette.

Summary

The dCST framework provides a clustering-free approach to community typing that is deterministic, hierarchical, and biologically interpretable. The combination of depth-1 dCSTs (dominant species) and depth-2 refinement (co-dominance patterns) captures structure at multiple resolutions without requiring any parameter tuning beyond the minimum support threshold n0.

linf documentation built on Aug. 5, 2026, 9:08 a.m.