Statistical normalization methods in microbiome data with application to microbiome cancer research

ABSTRACT

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

KEYWORDS:

• Microbiome
• normalization
• 16S rRNA sequencing data
• shotgun metagenomic sequencing data
• microbiome cancer research

Acknowledgement

Thanks to Yuxuan Xia for proofreading this article.

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No potential conflict of interest was reported by the author.

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The author(s) reported there is no funding associated with the work featured in this article.