首页|Compositional transformations can reasonably introduce phenotypeassociated values into sparse features
Compositional transformations can reasonably introduce phenotypeassociated values into sparse features
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NETL
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from biorxiv.org: "It was recently argued that an analysis of tumor-associated microbiome data is invalid because features that were originally very sparse (genera with mostly zero read counts) became associated with the phenotype following batch correction. Here, we examine whether such an observation should necessarily indicate issues with processing or machine learning pipelines. "We focus on the centered log ratio (CLR) transformation, which is often recommended for analysis of compositional microbiome data. The CLR transformation has similarities to Voom-SNM, the batchcorrection method brought into question, yet is a sample-wise operation that cannot, in itself, \"leak\" information or invalidate downstream analyses. "We show that because the CLR transformation divides each value by the geometric mean of its sample, common imputation strategies for missing or zero values result in transformed features that are associated with the geometric mean. Through analyses of both synthetic and vaginal microbiome datasets we demonstrate that when the geometric mean is associated with a phenotype, sparse and CLR-transformed features will also become associated with it.
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