They provide seven popular normalization methods, including:
rarefy: random subsampling counts to the smallest library size in the data set.
TSS: total sum scaling, also referred to as “relative abundance”, the abundances were normalized by dividing the corresponding sample library size.
TMM: trimmed mean of m-values. First, a sample is chosen as reference. The scaling factor is then derived using a weighted trimmed mean over the differences of the log-transformed gene-count fold-change between the sample and the reference.
RLE: relative log expression, RLE uses a pseudo-reference calculated using the geometric mean of the gene-specific abundances over all samples. The scaling factors are then calculated as the median of the gene counts ratios between the samples and the reference.
CSS: cumulative sum scaling, calculates scaling factors as the cumulative sum of gene abundances up to a data-derived threshold.
CLR: centered log-ratio normalization.
CPM: pre-sample normalization of the sum of the values to 1e+06.