What this entity is
Canonical name: KMeans clustering. 1
Known aliases: K-means clustering; KMeans. 1
Stable external identifier: None was reported in the source documents. 1,2
KMeans clustering is a partitioning method used after principal component analysis (PCA) to identify within-species gene-content ecotypes from bacterial pangenome data. 1
Use in ecotype and metabolic differentiation
The gene-content analysis used PCA with up to 50 components followed by KMeans, searched cluster counts from k = 2–6, and selected the k with the best silhouette score. 1
KMeans identified valid gene-content ecotypes in 12 of 15 sampled species, assigning 1,820 genomes across 12 species spanning 6 phyla. 1
The species averaged 3.7 ecotypes, with a range of 2–6 ecotypes per species, and the mean silhouette score was 0.215 with a median of 0.174. 1
The clearest clustering occurred in Erwinia amylovora, with a silhouette score of 0.468 and 2 ecotypes, and in Bacteroides xylanisolvens, with a silhouette score of 0.366 and 6 ecotypes. 1
The weakest clustering signals occurred in Staphylococcus simulans, with a silhouette score of 0.118, and Streptococcus pseudopneumoniae, with a silhouette score of 0.131. 1
Valid clusters required at least 2 ecotypes, at least 10 genomes per ecotype, and at least 20 assigned genomes in total. 1
A separate metabolic-capability analysis supports the use of clustering for within-species metabolic ecotypes: all 10 target species formed clusters with silhouette scores greater than 0.2, ranging from 0.35 for PALSA-747 sp. to 0.89 for Salmonella enterica. 2
This result refines the earlier gene-content analysis rather than replacing it: the two studies used different species sets and feature definitions, with the metabolic analysis finding significant cluster–isolation-environment associations in Salmonella enterica (χ²=1570.2, df=25, p<0.0001) and Phenylobacterium sp. (χ²=12.2, df=1, p=0.0005), but not in four sampled marine organisms. 2
The resulting gene-content ecotype assignments were compared using 257 chi-square or Fisher’s exact tests across 12 species and 23 COG categories, with 170 tests, or 66.1%, significant after Benjamini–Hochberg false-discovery-rate correction at q < 0.05. 1
Interpretation and limitations
KMeans-based ecotype assignments provide multi-species evidence that within-species gene-content variation is functionally differentiated, while the overlapping cluster boundaries and small effect sizes limit the strength of ecological interpretation. 1
The largest reported mean COG-category effect sizes were 0.0392 for category S, representing unknown function, and 0.0337 for category L, representing replication, recombination, and repair. 1
KMeans was used because hdbscan was unavailable on the cluster, and the report notes that KMeans assumes spherical clusters and requires a selected k, whereas HDBSCAN could better handle variable-density subpopulations. 1
Without within-species phylogenetic controls such as core-genome trees, the KMeans-derived ecotypes cannot be distinguished from phylogenetic or demographic substructure. 1
The metabolic-capability analysis reinforces this limitation: its environment–cluster associations were observational, lacked explicit phylogenetic correction, and could also reflect coarse isolation-source metadata or pathway annotations that miss ecologically relevant gene-content and expression differences. 2
Approximately 38% of gene clusters had COG annotations, leaving 62% unannotated, so KMeans-associated functional differences may be biased toward better-characterized functions. 1
The report proposes comparing KMeans results with HDBSCAN, overlaying core-genome phylogenetic trees on ecotype assignments, scaling the analysis to all 456 eligible species, and integrating habitat metadata. 1
Related pages
This method is documented in Ecotype Functional Differentiation and Metabolic Capability vs Metabolic Dependency. 1,2
It contributes to ecotype-environment-gene-content, which addresses relationships among ecotypes, environments, and gene content, and to pangenome-integration through clustering of pangenome gene-content profiles. 1
The analysis also contributes to multi-omics-integration through proposed integration of functional annotation and protein-structure evidence. 1
KMeans was preceded by principal-component-analysis, and its functional comparisons used cog-functional-categories. 1