Compositional transformations can reasonably introduce phenotype-associated values into sparse features.

ABSTRACT: It was recently argued¹ that an analysis of tumor-associated microbiome data² is invalid because features that were originally very sparse (genera with mostly zero read counts) became associated with the phenotype following batch correction¹. Here, we examine whether such an observation should necessarily indicate issues with processing or machine learning pipelines. We show counterexamples using the centered log ratio (CLR) transformation, which is often used for analysis of compositional microbiome data³. The CLR transformation has similarities to Voom-SNM^4,5, the batch-correction method brought into question^1,2, yet is a sample-wise operation that cannot, in itself, "leak" information or invalidate downstream analyses. We

SUBMITTER: Austin GI 

PROVIDER: S-EPMC11741240 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature