Table 1 Data-driven methods for normalization of shotgun metagenomic data included in this study
From: Comparison of normalization methods for the analysis of metagenomic gene abundance data
Method | Description | Availability |
|---|---|---|
Total counts | Calculates scaling factors based on the total gene abundances | - |
Median | Calculates scaling factors based on the median gene abundance | edgeR package in Bioconductor |
Upper quartile [19] | Calculates scaling factors based on the upper quartile of the gene abundances | edgeR package in Bioconductor |
Trimmed mean of M-values (TMM) [21] | Calculates scaling factors based on robust analysis of the difference in relative abundance between samples. | edgeR package in Bioconductor |
Relative Log Expression (RLE) [30] | Calculates scaling factors using the ratio between gene abundances and their geometric mean | DESeq package in Bioconductor |
Cumulative sum scaling (CSS) [20] | Calculates scaling factors as the cumulative sum of gene abundances up to a data-derived threshold | metagenomeSeq package in Bioconductor |
Reversed cumulative sum scaling (RCSS) | Calculates scaling factors as the cumulative sum of high abundant genes | - |
Quantile-quantile [19] | Transforms each sample to follow a data-derived reference distribution | - |
Rarefying [55] | Randomly removes gene fragments until the sequencing depth is equal in all samples | phyloseq package in Bioconductor |