On Thursday, January 14th at 7 pm UTC, as part of the Why R? Webinar series, we had the honour to host Susan Holmes from Stanford University. She did talk about microbiomes and how to use R’s workflow to perform sophisticated downstream statistical analysis on those complex bacterial communities.
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- Susan Holmes Author of “Modern Statistics for Biology”, Susan is a Professor of Statistics and member of BioX, Stanford University. She uses computational statistics to draw inferences about many complex biological phenomena, interactions between the immune system and cancer, resilience and biomarker detection in the human microbiome and drug resistance in HIV. She enjoys teaching using R and BioConductor and tries to make everything she does freely available.
Why using R for analysis of the human microbiome is a good idea
High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or composition of bacterial communities in different conditions. The sequencing reads have to be denoised and assigned to microbiomes the closest taxa from a reference database. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, whether parametric or nonparametric.
We provide examples of using the R packages
vegan to filter,
visualize and test microbiome data. We also provide examples of supervised analyses using random forests and
nonparametric testing using community networks and the
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