Metagenomic data possess some unique characteristics such as vast differences in sequencing depth, sparsity (containing many zeros) and large variance in distributions (overdispersion). These unique attributes have made it inappropriate to directly apply methods developed in other omics fields to perform differential analysis on metegenomic data. To account for these issues, MicrobiomeAnalyst includes various normalization methods such as:
- Rarefaction and scaling methods: these methods deal with uneven sequencing depths by bringing samples to the same scale for comparison.
- Transformation methods: it includes methods to deal with sparsity, compositionality, and large variations within the data.
More details on microbiome data normalization can be found here.