What this entity is
Canonical name: Partial correlation. 1
Partial correlation measures the association between two variables while accounting for another variable. 1
Known aliases: None reported in the source documents. 1,2
Stable external identifier: None reported in the source documents. 1,2
Key applications and findings
The Ecotype Correlation Analysis analysis used partial correlations to compare environmental and phylogenetic similarity as predictors of bacterial gene-content similarity across 172 species. 1
The median partial correlation for environment was 0.0025, compared with 0.0143 for phylogeny. 1 Phylogeny dominated the gene-content signal in 60.5% of species, whereas environment dominated in 39.5% of species. 1 A significant positive environment effect was detected in 12 species (7.0%), a significant negative environment effect in 4 species (2.3%), and no significant effect in 156 species (90.7%). 1
The analysis used environmental embeddings from alph-aearth, genome metadata and taxonomy, NCBI environmental and isolation-source metadata, pangenome composition, and gene-cluster presence/absence profiles from kbase-ke-pangenome. 1 These results support the interpretation that vertical inheritance generally dominates genome-wide gene-content similarity, while environmental adaptation may act on specific gene subsets rather than the whole genome. 1
The Prophage Gene Modules and Terminase-Defined Lineages Across Bacterial Phylogeny and Environmental Gradients analysis refines this whole-genome result by applying a partial Spearman correlation to a more specific prophage-module phenotype: environmental niche breadth and prophage module count remained correlated at rho=0.468 after controlling for genome size, with p=8.41e-110, across 2,008 species with at least 5 embedded genomes. 3 This supports the use of partial correlation for detecting environmental structure in targeted gene-module traits even when genome size is a major confounder, but it does not contradict the weak environment–whole-genome gene-content association because the response variables differ. 3
The Soil Metal Concentrations Drive Functional Gene Shifts report extends these applications to observational soil metagenomic data: partial-correlation models are planned to test whether COG–metal associations remain after controlling for co-varying chromium, copper, lead, and zinc. 4 This planned analysis is intended to distinguish metal-specific associations from a generic multi-metal-contamination response, rather than establish such specificity in the current results. 4
Reanalysis and methodological refinement
The Ecotype Reanalysis — Environmental vs Human-Associated Species refines the original null result by applying genome-level harmonized environmental classifications within a consistent reanalysis: environmental species had median partial correlation 0.051 versus 0.084 for human-associated species, and the one-sided Mann–Whitney U test for Environmental > Human-associated gave U=1536 and p=0.83. 2
The reanalysis therefore supports the original conclusion that environmental species do not show a stronger environment–gene-content relationship, while showing that clinical sampling bias does not explain the weak signal. 2
Its absolute correlations should not be compared directly with the original analysis: the reanalysis median across 183 species was 0.081 versus 0.003 originally, described as a 27x difference, because it used all genomes with embeddings rather than diversity-maximizing downsampling. 2
The soil-metal application refines the method’s role from interpreting existing associations to a planned confounding-control test; the required COG–metal partial-correlation analysis cannot currently be completed because no local CSV contains the Spearman rho values or model residuals, and Spark access to the kescience_mgnify and kbase_ke_pangenome tables is required. 4
Limitations in this use
The partial-correlation analysis assumes linear relationships between distance matrices and may not capture nonlinear ecological effects. 1
AlphaEarth embeddings covered only 28.4% of genomes, limiting the environmental signal available for analysis. 1 The prophage study likewise found that only 28% of genomes had AlphaEarth environmental embeddings, producing a biased subsample toward clinically and environmentally well-sampled lineages. 3
Geographic coordinates in NCBI metadata were often missing or imprecise, reducing the quality of environmental associations. 1 The analysis suggests the hypothesis that AlphaEarth embeddings do not fully capture ecologically relevant environmental variation. 1
The reanalysis further found that environmental species had a 21% NaN partial-correlation rate versus 7% for human-associated species; this filtering did not reveal the hypothesized stronger environmental signal. 2
The soil-metal application adds co-contamination and spatial dependence as limitations: positive correlations among metals may make ordinary FDR correction anti-conservative, and planned Moran’s I testing will assess spatial autocorrelation before any spatial-error validation. 4 These concerns qualify interpretation of the planned partial correlations because residual metal associations could reflect shared contamination or geography rather than the controlled metal alone. 4
A downsampled-versus-full-genome extraction comparison, functional gene-subset tests, structured ENVO classification, and genome-count covariate analysis are needed to resolve the methodological discrepancy and test whether whole-genome distances mask environment-specific effects. 2
Related pages
- pangenome-integration — The method was used to compare environmental and phylogenetic distances with pangenome gene-content similarity. 1
- ecotype-environment-gene-content — The reanalysis strengthens the cross-project null result for environment–gene-content association. 2
- environmental-resistome — Planned partial-correlation tests will assess whether soil-metal-associated functional shifts are metal-specific. 4
- metal-cross-resistance — The method is proposed to separate individual-metal effects from correlated multi-metal contamination. 4
- environment-embedding-geography — The prophage analysis used an AlphaEarth-controlled partial correlation to relate environmental niche breadth to prophage module count. 3
- average-nucleotide-identity — ANI distances were extracted as part of the supporting analysis. 1
- alph-aearth — Environmental embeddings supplied one predictor distance. 1
- kbase-ke-pangenome — Gene-cluster presence/absence profiles supplied the gene-content data. 1