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<p>The microbial rare biosphere, composed of low-abundance microorganisms in a community, lacks a standardized delineation method for its definition. Currently, most studies rely on arbitrary thresholds to define the microbial rare biosphere (e.g., 0.1% relative abundance per sample), hampering comparisons across studies. To address this challenge, we present <i>ulrb</i> (Unsupervised Learning based Definition of the Rare Biosphere), available as an R package. <i>ulrb</i> uses unsupervised machine learning to optimally classify taxa into abundance categories (e.g., rare, intermediate, or abundant) within microbial communities. We show that <i>ulrb</i> is more consistent than threshold-based approaches and can be applied to data derived from common microbial ecology protocols and non-microbial studies. <i>ulrb</i> can be used to identify different types of rarity and is statistically valid for the analysis of various dataset sizes. In conclusion, <i>ulrb</i> discerns rare from abundant organisms in a user-independent manner, finding applicability in selected ecological datasets.</p>

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Definition of the microbial rare biosphere through unsupervised machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月2日

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  • Francisco Pascoal,
  • Paula Branco,
  • Luís Torgo,
  • Rodrigo Costa,
  • Catarina Magalhães

摘要

The microbial rare biosphere, composed of low-abundance microorganisms in a community, lacks a standardized delineation method for its definition. Currently, most studies rely on arbitrary thresholds to define the microbial rare biosphere (e.g., 0.1% relative abundance per sample), hampering comparisons across studies. To address this challenge, we present ulrb (Unsupervised Learning based Definition of the Rare Biosphere), available as an R package. ulrb uses unsupervised machine learning to optimally classify taxa into abundance categories (e.g., rare, intermediate, or abundant) within microbial communities. We show that ulrb is more consistent than threshold-based approaches and can be applied to data derived from common microbial ecology protocols and non-microbial studies. ulrb can be used to identify different types of rarity and is statistically valid for the analysis of various dataset sizes. In conclusion, ulrb discerns rare from abundant organisms in a user-independent manner, finding applicability in selected ecological datasets.