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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
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