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

Area of Science:

• Microbiome Research
• Bioinformatics
• Machine Learning

Background:

• Recent arguments suggest tumor-associated microbiome data analysis is invalid if sparse features become phenotype-associated after batch correction.
• This raises concerns about potential information leakage in processing or machine learning pipelines.

Purpose of the Study:

• To investigate whether sparse features becoming phenotype-associated necessarily indicates issues with microbiome data processing or machine learning pipelines.
• To demonstrate that sample-wise transformations can create such associations without information leakage.

Main Methods:

• Utilized the centered log ratio (CLR) transformation, a common method for compositional microbiome data.
• Analyzed synthetic and vaginal microbiome datasets.
• Re-analyzed features previously highlighted as problematic by Gihawi et al.

Main Results:

• Demonstrated that the CLR transformation, a sample-wise operation, can cause initially sparse features to become associated with a phenotype.
• This association occurs when the geometric mean, used in CLR, is linked to the phenotype, particularly with common imputation strategies for zero values.
• Showed that this phenomenon, observed with CLR, serves as a counterexample to the claim of necessary information leakage.

Conclusions:

• The appearance of phenotype-associated sparse features after sample-wise transformations like CLR does not independently prove information leakage in machine learning pipelines.
• Such observations can arise from the nature of the transformation and data characteristics, not necessarily from pipeline artifacts.
• Emphasized the need for cautious interpretation of individual features in multivariate microbiome data.