Abstract
MotivationThe rapidly expanding microbiomics field is generating increasingly larger datasets, characterizing the microbiota in diverse environments. Although classical numerical ecology methods provide a robust statistical framework for their analysis, software currently available is inadequate for large datasets and some computationally intensive tasks, like rarefaction and associated analysis.ResultsHere we present a software package for rarefaction analysis of large count matrices, as well as estimation and visualization of diversity, richness and evenness. Our software is designed for ease of use, operating at least 7x faster than existing solutions, despite requiring 10x less memory.Availability and ImplementationC ++ and R source code (GPL v.2) as well as binaries are available from https://github.com/hildebra/Rarefaction and from CRAN (https://cran.r-project.org/).Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
A common task in ecology and microbiomic data analysis is to count and compare the occurrences of different organisms throughout different samples, resulting in taxa count matrices
Rarefaction is a data normalization technique designed to cope with such unequal sampling efforts, by subsampling to the same rarefaction depth for all samples, simulating equal sampling effort
For all dataset sizes rarefaction toolkit (RTK) outperformed the other programs with regards to speed and memory requirement (Fig. 1, Supplementary Table S2)
Summary
A common task in ecology and microbiomic data analysis is to count and compare the occurrences of different organisms throughout different samples, resulting in taxa count matrices. Rarefaction is a data normalization technique designed to cope with such unequal sampling efforts, by subsampling to the same rarefaction depth for all samples, simulating equal sampling effort. This allows calculation of comparable diversity estimators and enables collectors curves, to estimate total expected diversity. We present the rarefaction toolkit (RTK), which can perform fast rarefaction on very large datasets comprising millions of features even on a laptop computer, computes estimates of ecological diversity and provides appropriate visualizations of the results
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