Chen Yin-Cheng, Su Yin-Yuan, Chu Tzu-Yu, Wu Ming-Fong, Huang Chieh-Chun, Lin Chen-Ching
Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
NPJ Biofilms Microbiomes. 2025 Jan 3;11(1):3. doi: 10.1038/s41522-024-00598-2.
The intricate nature of microbiota sequencing data-high dimensionality and sparsity-presents a challenge in identifying informative and reproducible microbial features for both research and clinical applications. Addressing this, we introduce PreLect, an innovative feature selection framework that harnesses microbes' prevalence to facilitate consistent selection in sparse microbiota data. Upon rigorous benchmarking against established feature selection methodologies across 42 microbiome datasets, PreLect demonstrated superior classification capabilities compared to statistical methods and outperformed machine learning-based methods by selecting features with greater prevalence and abundance. A significant strength of PreLect lies in its ability to reliably identify reproducible microbial features across varied cohorts. Applied to colorectal cancer, PreLect identifies key microbes and highlights crucial pathways, such as lipopolysaccharide and glycerophospholipid biosynthesis, in cancer progression. This case study exemplifies PreLect's utility in discerning clinically relevant microbial signatures. In summary, PreLect's accuracy and robustness make it a significant advancement in the analysis of complex microbiota data.
微生物群测序数据的复杂性——高维度和稀疏性——给研究和临床应用中识别信息丰富且可重复的微生物特征带来了挑战。为解决这一问题，我们引入了PreLect，这是一个创新的特征选择框架，它利用微生物的流行率来促进在稀疏微生物群数据中进行一致的选择。在对42个微生物组数据集的既定特征选择方法进行严格基准测试后，PreLect与统计方法相比表现出卓越的分类能力，并且通过选择具有更高流行率和丰度的特征，其性能优于基于机器学习的方法。PreLect的一个显著优势在于它能够可靠地识别不同队列中可重复的微生物特征。应用于结直肠癌，PreLect识别出关键微生物，并突出了癌症进展中的关键途径，如脂多糖和甘油磷脂生物合成。这个案例研究例证了PreLect在辨别临床相关微生物特征方面的效用。总之，PreLect的准确性和稳健性使其成为复杂微生物群数据分析中的一项重大进展。
