---
title: "Introduction to the microbiome R package"
author: "Leo Lahti, Sudarshan Shetty, et al."
bibliography: 
- bibliography.bib
date: "`r Sys.Date()`"
output:
  BiocStyle::html_document:
    toc: true
    fig_caption: yes    
  rmarkdown::md_document:
    toc: true
  rmarkdown::pdf_document:
    toc: true    
vignette: >
  %\VignetteIndexEntry{microbiome R package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r, echo=FALSE, message=FALSE}
library(knitr)
require(knitcitations)
# cleanbib()
# options("citation_format" = "pandoc")
bib <- read.bibtex("bibliography.bib")

# Can also write math expressions, e.g. 
# $Y = X\beta + \epsilon$, footnotes^[A footnote here.]
# > "He who gives up [code] safety for [code] speed deserves neither."
# ([via](https://twitter.com/hadleywickham/status/504368538874703872))
knitr::opts_chunk$set(fig.width=10, fig.height=10, message=FALSE, warning=FALSE)
```

# Introduction

The [microbiome R package](http://microbiome.github.io/microbiome)
facilitates exploration and analysis of microbiome profiling data, in
particular 16S taxonomic profiling.

This vignette provides a brief overview
with example data sets from published microbiome profiling studies
[@lahti14natcomm, @Lahti13provasI, @OKeefe15].
A more comprehensive tutorial is available
[on-line](http://microbiome.github.io/microbiome).

Tools are provided for the manipulation, statistical analysis, and
visualization of taxonomic profiling data. In addition to targeted
case-control studies, the package facilitates scalable exploration of
large population cohorts [@lahti14natcomm]. Whereas sample collections
are rapidly accumulating for the human body and other environments,
few general-purpose tools for targeted microbiome analysis are
available in R.  This package supports the independent
[phyloseq](http://joey711.github.io/phyloseq) data format and expands
the available toolkit in order to facilitate the standardization of
the analyses and the development of best practices. See also the
related [PathoStat
pipeline](http://bioconductor.org/packages/release/bioc/html/PathoStat.html)
[mare pipeline](https://github.com/katrikorpela/mare),
[phylofactor](), and [structSSI]() for additional 16S rRNA amplicon
analysis tools in R. The aim is to complement the other available
packages, but in some cases alternative solutions have been necessary
in order to streamline the tools and to improve complementarity.

We welcome feedback, bug reports, and suggestions for new features
from the user community via the [issue
tracker](https://github.com/microbiome/microbiome/issues) and [pull
requests](http://microbiome.github.io/microbiome/Contributing.html). See
the [Github site](https://github.com/microbiome/microbiome) for source
code and other details. These R tools have been utilized in recent
publications and in introductory courses [@salonen14, @Faust15,
@Shetty2017], and they are released under the [Two-clause FreeBSD
license](http://en.wikipedia.org/wiki/BSD\_licenses).

Kindly cite the work as follows: "Leo Lahti [et
al.](https://github.com/microbiome/microbiome/graphs/contributors)
(Bioconductor, 2017). Tools for microbiome analysis in R. Microbiome
package version `r sessionInfo()$otherPkgs$microbiome$Version`. URL:
(http://microbiome.github.io/microbiome)


# Installation

To install microbiome package in R (Bioconductor development version),
use

```{r install, eval=FALSE}
library(BiocManager)
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install(version = "devel")
BiocManager::install("microbiome")
```

Then load the package in R

```{r loading}
library(microbiome)  
```



# Data

The microbiome package relies on the independent
[phyloseq](http://joey711.github.io/phyloseq) data format. This
contains an OTU table (taxa abundances), sample metadata (age, BMI,
sex, ...), taxonomy table (mapping between OTUs and higher-level
taxonomic classifications), and a phylogenetic tree (relations between
the taxa).


## Example data sets

Example data sets are provided to facilitate reproducible examples and
further methods development.

The HITChip Atlas data set [Lahti et al. Nat. Comm. 5:4344,
2014](http://www.nature.com/ncomms/2014/140708/ncomms5344/full/ncomms5344.html)
contains 130 genus-level taxonomic groups across 1006 western
adults. Load the example data in R with

```{r atlasdata}
# Data from 
# http://www.nature.com/ncomms/2014/140708/ncomms5344/full/ncomms5344.html
data(atlas1006) 
atlas1006
```


# Further reading

The [on-line tutorial](http://microbiome.github.io/microbiome)
provides many additional tools and examples, with more thorough
descriptions. This package and tutorials are work in progress. We
welcome feedback, for instance via [issue
Tracker](https://github.com/microbiome/microbiome/issues), [pull
requests](https://github.com/microbiome/microbiome/), or via
[Gitter](https://gitter.im/microbiome).



# Acknowledgements

Thanks to all
[contributors](https://github.com/microbiome/microbiome/graphs/contributors).
Financial support has been provided by Academy of Finland (grants
256950 and 295741), [University of
Turku](http://www.utu.fi/en/Pages/home.aspx), Department of
Mathematics and Statistics. In addition, the work has been supported
by Laboratory of Microbiology, Wageningen University, The
Netherlands. This work relies on the independent
[phyloseq](https://github.com/joey711/phyloseq) package and data
structures for R-based microbiome analysis developed by Paul McMurdie
and Susan Holmes. This work also utilizes a number of independent R
extensions, including dplyr [@dplyr], ggplot2 [@Wickham_2009],
phyloseq [@McMurdie2013], and vegan [@Oksanen_2015].


# References

```{r, echo=FALSE,results="asis"}
bibliography()
```


