microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization

Abstract

Summary Characterizing biomarkers based on microbiome profiles has great potential for translational medicine and precision medicine. Here, we present microbiomeMarker, an R/Bioconductor package implementing commonly used normalization and differential analysis methods, and three supervised learning models to identify microbiome markers. microbiomeMarker also allows comparison of different methods of differential analysis and confounder analysis. It uses standardized input and output formats, which renders it highly scalable and extensible, and allows it to seamlessly interface with other microbiome packages and tools. In addition, the package provides a set of functions to visualize and interpret the identified microbiome markers. Availability and implementation microbiomeMarker is freely available from Bioconductor (https://www.bioconductor.org/packages/microbiomeMarker). Source code is available and maintained at GitHub (https://github.com/yiluheihei/microbiomeMarker). Supplementary information Supplementary data are available at Bioinformatics online.

Microbiome Restructuring: Dominant Coral Bacterium Endozoicomonas Species Respond Differentially to Environmental Changes

“…Stacked bar plots were obtained by converting absolute abundance profiles into relative abundances. Abundance profiles were processed with the R packages phyloseq ( 81 ), vegan ( 82 ), ggplot2 ( 83 ), pheatmap ( 84 ), and microbiomeMarker ( 85 ) for downstream analyses and visualization. Alpha diversity analysis was conducted after rarifying the samples to an even depth of 5,704 reads using the estimate_richness function from phyloseq.…”

Soil composition and rootstock genotype drive the root associated microbial communities in young grapevines

“…MicrobiomeMarker (version 1.1.1) was used for Limma-Voom method, using “run_limma_voom” function (α = 0.001) corrected with the False Discovery Rate (FDR), to discriminate microbial taxa above family between vineyard soil used for greenhouse experiment ( Cao, 2020 ). To observe the contributions of vineyard and nursery microbiomes to root associated microbiome from the greenhouse experiment, the same package was used for linear discriminant analysis (LDA) effect size (LEfSe) method, using “run_lefse” function, to discriminate enriched microbial taxa above orders.…”

Similar Gut Bacterial Composition Between Patients With Ulcerative Colitis and Healthy Controls in a High Incidence Population: A Cross-sectional Study of the Faroe Islands IBD Cohort

“…In addition, we used a compositional method to assess whether groups of ASVs characterize the microbiota of UC patients and HCs (package selbal , version 0.1.0). 26 Moreover, linear discriminant analysis effect size (LefSe) was estimated on genus count data (top 50) normalized by cumulative sum scaling (package microbiomeMarker) 27 to assess differentially abundant taxa on several taxonomic levels (here, kingdom to genus).…”