agp_gut: American Gut Project gut microbiome dataset

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

American Gut Project gut microbiome dataset

Description

A stratified subsample of 766 gut microbiome samples from the American Gut Project (PRJEB11419, AGP-US-2015), bundled for demonstrating L-infinity dCST construction in a gut ecosystem.

Usage

agp_gut

Format

A list with four components:

counts

Integer matrix (766 x 314). Raw 16S V4 read counts. Rows are samples, columns are SILVA species-level taxa.

meta

Data frame (766 rows) with columns: Run (SRA run accession), dcst_depth1, dcst_depth2 (pre-computed dCST labels), IBS, IBD, Diabetes, Autoimmune, Seasonal_allergies, Migraine, Acid_reflux, Lung_disease, Cardiovascular_disease, Skin_condition, Obesity (binary disease indicators from self-reported AGP metadata), BMI (numeric, self-reported), and selection_reason (target-dCST inclusion or seeded background sampling).

taxa

Character vector of 314 SILVA taxonomy strings.

source

Character string documenting provenance.

Details

The subsample includes all samples assigned to four uncommon demonstration dCSTs (Prevotella_7, Pasteurellaceae, Akkermansia, and Staphylococcus). The remaining slots are a simple random sample, drawn with seed 42, from the eligible background after excluding Eukaryota and Unassigned labels. Phenotype fields are joined only after membership is fixed and do not influence selection. Because inclusion probabilities differ by dCST, this object is a computational demonstration dataset rather than a probability sample of the underlying cohort.

The American Gut Project is a large citizen-science 16S rRNA survey of the human microbiome. Health conditions are self-reported via questionnaire and should be interpreted with appropriate caution.

The phenotype fields must not be used with this dCST-stratified subset for population prevalence estimates, effect-size estimation, or association testing. The exact selection is generated by data-raw/create_agp_gut_subset.py; run-ID membership, selection reasons, and derived annotations are retained in inst/extdata/agp_gut_meta.csv.

The count matrix can be used directly with filter.asv, normalize.linf, and downstream dCST functions.

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

Source

Derived from the public American Gut Project records under ENA accession PRJEB11419 via the PRIME pipeline. See data-raw/create_agp_gut_subset.py, data-raw/build_agp_gut.R, and the installed DATA_PROVENANCE.md file.

References

McDonald, D., Hyde, E., Debelius, J. W., et al. (2018). American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems, 3(3), e00031-18. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1128/mSystems.00031-18")}

Examples

data(agp_gut)
dim(agp_gut$counts)                     # 766 x 314
table(agp_gut$meta$dcst_depth1)         # dCST distribution
sum(agp_gut$meta$IBS)                   # IBS cases

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