Data-driven mathematical and visualization approaches for removing rare features for Compositional Data Analysis (CoDA).

ABSTRACT: Sparse feature tables, in which many features are present in very few samples, are common in big biological data (e.g. metagenomics). Ignoring issues of zero-laden datasets can result in biased statistical estimates and decreased power in downstream analyses. Zeros are also a particular issue for compositional data analysis using log-ratios since the log of zero is undefined. Researchers typically deal with this issue by removing low frequency features, but the thresholds for removal differ markedly between studies with little or no justification. Here, we present CurvCut, an unsupervised data-driven approach with human confirmation for rare-feature removal. CurvCut implements two distinct approaches for determining natural breaks in the feature distributions: a method based on curvature a

SUBMITTER: Ortiz-Velez A 

PROVIDER: S-EPMC10768885 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature