Table 1.

Data-driven methods for normalization of shotgun metagenomic data included in this study

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