From RNA-seq to Biological Inference: Using Compositional Data Analysis in Meta-Transcriptomics.

The proper analysis of high-throughput sequencing datasets of mixed microbial communities (meta-transcriptomics) is substantially more complex than for datasets composed of single organisms. Adapting commonly used RNA-seq methods to the analysis of meta-transcriptome datasets can be misleading and not use all the available information in a consistent manner. However, meta-transcriptomic experiments can be investigated in a principled manner using Bayesian probabilistic modeling of the data at a functional level coupled with analysis under a compositional data analysis paradigm. We present a worked example for the differential functional evaluation of mixed-species microbial communities obtained from human clinical samples that were sequenced on an Illumina platform. We demonstrate methods to functionally map reads directly, conduct a compositionally appropriate exploratory data analysis, evaluate differential relative abundance, and finally identify compositionally associated (constant ratio) functions. Using these approaches we have found that meta-transcriptomic functional analyses are highly reproducible and convey significant information regarding the ecosystem.