2-Way k-Means as a Model for Microbiome Samples.

Area of Science:

• Microbiology
• Computational Biology
• Bioinformatics

Background:

• Microbiome sequencing facilitates sample clustering based on shared microbial composition.
• Current clustering methods struggle with samples exhibiting mixed compositions, which fall between defined clusters.
• This limitation hinders a comprehensive understanding of complex microbial ecosystems.

Purpose of the Study:

• To develop an unsupervised learning approach for identifying two-way clusters in microbiome data.
• To address the challenge of classifying samples with intermediate or mixed microbial compositions.
• To improve the accuracy and granularity of microbiome sample classification.

Main Methods:

• A novel mixture model is defined to enable two-way cluster assignment for samples.
• A modified generalized k-means algorithm is developed for learning the proposed mixture model.
• The methodology is applied to analyze microbial 16S rDNA sequencing data.

Main Results:

• The proposed method effectively identifies samples with mixed microbial compositions.
• Demonstrated applicability to real-world data from the Human Vaginal Microbiome Project.
• The two-way clustering approach provides a more nuanced classification of microbial communities.

Conclusions:

• The developed mixture model and generalized k-means variant offer a significant advancement in microbiome data analysis.
• This method enhances the ability to characterize complex microbial communities, including those with intermediate compositions.
• The findings have implications for understanding microbial ecology and host-microbe interactions.