Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities

BACKGROUND: Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is the current standard method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. Here we introduce a normalization method for species count data called scaling with ranked subsampling (SRS) and demonstrate its suitability for the analysis of microbial communities. METHODS: SRS consists of two steps. In the scaling step, the counts for all species or operational taxonomic units (OTUs) are divided by a scaling factor chosen in such a way that the sum of scaled counts equals the selected total number of counts C(min). The relative frequencies of all OTUs remain unchanged. In the subsequent ranked subsampling step, non-integer count values are converted into integers by an algorithm that minimizes subsampling error with regard to the population structure (relative frequencies of species or OTUs) while keeping the total number of counts equal C(min). SRS and rarefying were compared by normalizing a test library representing a soil bacterial community. Common parameters of biodiversity and population structure (Shannon index H’, species richness, species composition, and relative abundances of OTUs) were determined for libraries normalized to different size by rarefying as well as SRS with 10,000 replications each. An implementation of SRS in R is available for download (https://doi.org/10.20387/BONARES-2657-1NP3). RESULTS: SRS showed greater reproducibility and preserved OTU frequencies and alpha diversity better than rarefying. The variance in Shannon diversity increased with the reduction of the library size after rarefying but remained zero for SRS. Relative abundances of OTUs strongly varied among libraries generated by rarefying, whereas libraries normalized by SRS showed only negligible variation. Bray–Curtis index of dissimilarity among replicates of the same library normalized by rarefying revealed a large variation in species composition, which reached complete dissimilarity (not a single OTU shared) among some libraries rarefied to a small size. The dissimilarity among replicated libraries normalized by SRS remained negligibly low at each library size. The variance in dissimilarity increased with the decreasing library size after rarefying, whereas it remained either zero or negligibly low after SRS. CONCLUSIONS: Normalization of OTU or species counts by scaling with ranked subsampling preserves the original community structure by minimizing subsampling errors. We therefore propose SRS for the normalization of biological count data.