R-CMD-check

NOTE While we continue to maintain this R package, the development has been discontinued as we have shifted to supporting methods development based on the new TreeSummarizedExperiment data container, which provides added capabilities for multi-omics data analysis. Check the miaverse project for details.

About

The microbiomeutilities R package is part of the microbiome-verse tools that provides additional data handling and visualization support for the microbiome R/BioC package

Philosophy: “Seemingly simple tasks for experienced R users can always be further simplified for novice users”

Package website and online documentation

Install microbiomeutilities 

install.packages("devtools")
devtools::install_github("microsud/microbiomeutilities")

Citation:

o Leo Lahti, Sudarshan Shetty et al. (2017-2020). Tools for microbiome analysis in R. Version 2.1.28. URL: http://microbiome.github.com/microbiome
o Sudarshan A. Shetty, & Leo Lahti. (2020, October). microbiomeutilities: Utilities for Microbiome Analytics.

The microbiome R package relies on the independently developed
o phyloseq package and data structures for R-based microbiome analysis developed by Paul McMurdie and Susan Holmes.
o ggplot2 H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.
o tidyverse packages.

Microbiome package website with step-wise tutorials:
URL: http://microbiome.github.com/microbiome.

Tutorials
* Microbiome R package tutorials
* Open and reproducible spring school 2018
* Tools Microbiome Anlaysis

About the Author

MicrobiomeHD
The package provides access to a subset of studies included in the MicrobiomeHD database from Duvallet et al 2017: Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nature communications. These datasets are converted to phyloseq objects and can be directly used in R environment.

Datasets from:

  • Duvallet, Claire, et al. “Meta-analysis of gut microbiome studies identifies disease-specific and shared responses.” Nature communications 8.1 (2017): 1784.
  • Son, J. et al. Comparison of fecal microbiota in children with autism spectrum disorders and neurotypical siblings in the simons simplex collection. PLoS ONE 10, e0137725 (2015).
  • Kang, D. W. et al. Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children. PLoS ONE8, e68322 (2013).
  • Schubert, A. M. et al. Microbiome data distinguish patients with clostridium difficile infection and non-c. difficile-associated diarrhea from healthy controls. mBio 5, e01021–14–e01021–14 (2014).
  • Youngster, I. et al. Fecal microbiota transplant for relapsing clostridium difficile infection using a frozen inoculum from unrelated donors: a randomized, open-label, controlled pilot study. Clin. Infect. Dis. 58, 1515–1522 (2014).
  • Baxter, N. T., Ruffin, M. T., Rogers, M. A. & Schloss, P. D. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med. 8, 37 (2016).
  • Zackular, Joseph P., et al. “The gut microbiome modulates colon tumorigenesis.” MBio 4.6 (2013): e00692-13.
  • Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766–766 (2014).
  • Singh, P. et al. Intestinal microbial communities associated with acute enteric infections and disease recovery. Microbiome 3, 45 (2015).
  • Noguera-Julian, M. et al. Gut microbiota linked to sexual preference and hiv infection. EBioMedicine 5, 135–146 (2016). Dinh, D. M. et al. Intestinal microbiota, microbial translocation, and systemic inflammation in chronic HIV infection. J. Infect. Dis. 211, 19–27 (2014).
  • Lozupone, C. A. et al. Alterations in the gut microbiota associated with hiv-1 infection. Cell Host Microbe 14, 329–339 (2013).
  • Gevers, D. et al. The treatment-naive microbiome in new-onset crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
  • Zhang, Z. et al Large-scale survey of gut microbiota associated with MHE via 16s rRNA-based pyrosequencing. Am. J. Gastroenterol. 108, 1601–1611 (2013).
  • Wong, J. M. W., Souza, R. De, Kendall, C. W. C., Emam, A. & Jenkins, D. J. A. Colonic health: fermentation and short chain fatty acids. J. Clin. Gastroenterol. 40, 235–243 (2006).
  • Ross, M. C. et al. 16s gut community of the cameron county hispanic cohort. Microbiome 3, 7 (2015).
  • Zupancic, M. L. et al. Analysis of the gut microbiota in the old order Amish and its relation to the metabolic syndrome. PLoS ONE 7, e43052 (2012).
  • Scher, J. U. et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013).
  • Alkanani, A. K. et al. Alterations in intestinal microbiota correlate with susceptibility to type 1 diabetes. Diabetes 64, 3510–3520 (2015).
  • Scheperjans, F. et al Gut microbiota are related to parkinson’s disease and clinical phenotype. Mov. Disord. 30, 350–358 (2014).

NOTE:
The aim of this package is not to replace any of the other tools mentioned on this site.
## Change log

CHANGES IN VERSION 1.00.16 (2021-04-14)
o Fix format_to_besthit to return all slots present in input.

CHANGES IN VERSION 1.00.15 (2021-01-21)
o Fix plot_taxa_cv to deal with taxa_are_rows==FALSE.

CHANGES IN VERSION 1.00.14 (2021-01-21)
o Added utilities within peak-methods.

CHANGES IN VERSION 1.00.12 (2021-01-18)
o Removed prevalence option plot_taxa_heatmap.

CHANGES IN VERSION 1.00.12 (2021-01-18)
o Bug fix format_to_besthit.

CHANGES IN VERSION 1.00.11 (2020-11-09)
o Small improvement in error handling. o Phyloseq slots to tibble o add_refseq for storing ASV sequences

CHANGES IN VERSION 1.00.10 (2020-11-02)
o Improve get_group_abundances documentation and code

CHANGES IN VERSION 1.00.09 (2020-10-21)
o Added longitudinal page to website
o Added new function plot_area
o Added new function plot_paired_abundances
o Added new function plot_spaghetti

CHANGES IN VERSION 1.00.08 (2020-10-17)
o Cosmetic updates

CHANGES IN VERSION 1.00.07 (2020-10-15)
o Update to plot_taxa_heatmap
o Fixed plot_abund_prev options
o Added plot_alpha_rcurve
CHANGES IN VERSION 1.00.06 (2020-10-13)
o Added new function dominant_taxa
o Removed plot_ternary due to clash between ggplot2 and ggtern
o Added new function find_samples_taxa

CHANGES IN VERSION 1.00.05 (2020-10-11)
o Added new function dominant_taxa
o Added new function get_group_abundances
o removed microbiome_pipeline

CHANGES IN VERSION 1.00.04 (2020-10-11)
o Added new function plasticity
o modified theme_biome_utils

CHANGES IN VERSION 1.00.03 (2020-10-04)
o Added new function plot_ternary
o Deprecated plot_select_taxa

CHANGES IN VERSION 1.00.02 (2020-10-04)
o Version tested with R version 4.0.2 (2020-10-04)
o Added new function plot_listed_taxa
o Deprecated plot_select_taxa
o Added option for half violin in boxplots

CHANGES IN VERSION 1.00.01 (2020-10-03) o Added new function plot_abund_prev
o Added new function simple_heatmap
o Added new function taxa_distribution
o Added a custom theme theme_biome_utils
o Added gghalves to imports
o Fixed microbiome_pipeline report

CHANGES IN VERSION 1.00.00 (2020-10-01)
o Version tested with R version 4.0.2 (2020-06-22)
o Fix typos in documentation
o Add prefix option to format_to_besthit
o Edited phy_to_ldf to speed up conversion
o Removed format_phyloseq function as it is redundant
o Speedup taxa_summary function
o Free up pheatmap option in plot_taxa_heatmap
o Updated for more info from print_ps output
o plot_taxa_boxplot now returns a faceted plot
o Added new function plot_diversity_stats
o R code styling styler::tidyverse_style()

References:

  1. Callahan, B. J., McMurdie, P. J. & Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker gene data analysis. bioRxiv, 113597.
  2. Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A. & Holmes, S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nature methods 13, 581-583.
  3. Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K. & Gordon, J. I. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature methods 7, 335-336.
  4. Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H. & Robinson, C. J. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and environmental microbiology 75, 7537-7541.
    Team, R. C. (2000). R language definition. Vienna, Austria: R foundation for statistical computing.

More useful resources:

  1. Ben J. Callahan and Colleagues: Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses.
  2. Comeau AM and Colleagues: Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research
  3. MicrobiomeHD A standardized database of human gut microbiome studies in health and disease Case-Control
  4. Rhea A pipeline with modular R scripts
  5. Phyloseq Import, share, and analyze microbiome census data using R