Data pre-processing for analyzing microbiome data – A mini review

• Published in: Computational and structural biotechnology journal 2023-01, Vol.21, p.4804-4815
• Main Authors: Zhou, Ruwen, Ng, Siu Kin, Sung, Joseph Jao Yiu, Goh, Wilson Wen Bin, Wong, Sunny Hei
• Format: Article
• Language: English
• Subjects: 16S rRNA Sequencing; Batch Effect; Data Preprocessing; Microbiome Data; Mini-Review; Normalization
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.10.001

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies.