If you are starting the workshop at this section, or had problems running code in a previous section, use the following to load the data used in this section. Diversity in the ecological sense is intuitively understood as the complexity of a community of organisms.
There are many ways to quantify this complexity so that we can compare communities objectively. The two main categories of methods are known as alpha diversity and beta diversity Strade della zona 3 di milano Alpha diversity measures the diversity within a single sample and is generally based on the number and relative abundance of taxa at some rank e.
Beta diversity also uses the number of relative abundance of taxa at some rank, but measures variation between samples. In other words, an alpha diversity statistic describes a single sample and a beta diversity statistic describes how two samples compare. The vegan package is the main tool set used for calculating biological diversity statistics in R.
The diversity function from the vegan package can be used to calculate the alpha diversity of a set of samples. We also need to exclude the taxon ID column by subsetting the columns to only samples i. Since alpha diversity is a per-sample attribute, we can just add this as a column to the sample data table:.
Adding this as a column to the sample data table makes it easy to graph using ggplot2. We can use analysis of variance ANOVA to tell if at least one of the diversity means is different from the rest.
That tells us that there is a difference, but does not tell us which means are different. We will use the HSD. So that takes care of comparing the alpha diversity of sites, but there are other interesting groupings we can compare, such as the genotype and the type of the sample roots vs leaves.
We could do the above all over with minor modifications, but one of the benefits of using a programming language is that you can create your own functions to automate repeated tasks. We can generalize what we did above and put it in a function like so:. Looks like there is no difference in the alpha diversity between genotypes, but a large difference between the diversity of roots and leaves.
First we need to convert the taxmap object to a phyloseq object, since all of the phyloseq functions expect phyloseq objects. If you want to try it, you can install it by typing:. Alpha diversity statistics capture the diversity of whole samples in a single number, but to see the abundance of each taxon in a group of samples e. Stacked barcharts are typically used for this purpose, but we will be using heat trees.
First, we need to calculate the abundance of each taxon for a set of samples from our OTU abundance information. Now we can use these taxon counts to make heat trees of the primary taxa present in leafs and roots:. Beta diversity is a way to quantify the difference between two communities. There are many metrics that are used for this, but we will only mention a few of the more popular ones.
A few also incorporate phylogenetic relatedness and require a phylogenetic tree of the organisms in either community to be calculated. The vegan function vegdist is used to calculate the pairwise beta diversity indexes for a set of samples. Since this is a pairwise comparison, the output is a triangular matrix. In R, a matrix is like a data.The second part of the workshop demonstrates how to use dada2 on raw reads, and analysis of these data using the phyloseqtreeDAadaptiveGPCA packages for denoising, estimating differential abundance, ordinations.
The treelapse and metavizr packages allow browsing and interactive visualization of microbiome profiles. Together, these packages provide easily linked components for data acquisition and flexible analysis of 16S rRNA and whole metagenome shotgun microbiome profiles.
At the end of this workshop, users will be able to access publicly available metagenomic data and to perform common statistical analyses of these and other data in Bioconductor. The microbiome is formed of the ecological communities of microorganisms that dominate the living world.
Bacteria can now be identified through the use of next generation sequencing applied at several levels. Shotgun sequencing of all bacteria in a sample delivers knowledge of all the genes present. This gene presents several variable regions which can be used to identify the different taxa.
These approaches do not incorporate all the data, in particular sequence quality information and statistical information available on the reads were not incorporated into the assignments.
In contrast, the de novo read counts used here will be constructed through the incorporation of both the quality scores and sequence frequencies in a probabilistic noise model for nucleotide transitions. For more details on the algorithmic implementation of this step see Benjamin J Callahan et al. In this workflow, we have used the labeled sequences to build a de novo phylogenetic with the. A published but essentially similar version of this workflow, including reviewer reports and comments is available Ben J Callahan et al.
This is a workflow for denoising, filtering, performing data transformations, visualization, supervised learning analyses, community network tests, hierarchical testing and linear models. We provide all the code and give several examples of different types of analyses and use-cases. There are often many different objectives in experiments involving microbiome data and we will only give a flavor for what could be possible once the data has been imported into R.
In addition, the code can be easily adapted to accommodate batch effects, covariates and multiple experimental factors. This workflow is based on software packages from the open-source Bioconductor project Huber et al. We provide all steps necessary from the denoising and identification of the reads input as raw sequences as fastq files to the comparative testing and multivariate analyses of the samples and analyses of the abundances according to multiple available covariates.
We will select portions of Complete Bioconductor Worflow output to cover in this tutorial. The phyloseq package uses a specialized system of S4 data classes to store all related phylogenetic sequencing data as a single, self-consistent, self-describing experiment-level object, making it easier to share data and reproduce analyses.
In general, phyloseq seeks to facilitate the use of R for efficient interactive and reproducible analysis of amplicon count data jointly with important sample covariates. This tutorial shows a useful example workflow, but many more analyses are available to you in phyloseq, and R in general, than can fit in a single workflow.
The phyloseq home page is a good place to begin browsing additional phyloseq documentation, as are the three vignettes included within the package, and linked directly at the phyloseq release page on Bioconductor. More complete details can be found on the phyloseq FAQ page.
In the previous section the results of dada2 sequence processing were organized into a phyloseq object.The phyloseq package is fast becoming a good way a managing micobial community data, filtering and visualizing that data and performing analysis such as ordination.
Along with the standard R environment and packages vegan and vegetarian you can perform virually any analysis. Today we will. We first need to make sure we have the necessary packages: phyloseq, ggplot2, gridExtra, gridR, ape, and edgeR. First read in the dataset, see what the objects look like. Look at the head of each. Get the sample names and tax ranks, finally view the phyloseq object. Lets draw a first bar plot. Agglomerate taxa at the Genus level combine all with the same name and remove all taxa without genus level assignment.
You could also use the MDS method of ordination here, edit the code to do so. Can also edit the distance method used to jaccard, jsd, euclidean. Play with changing those parameters.
Investigate transformations. We transform microbiome count data to account for differences in library size, variance, scale, etc. Now try doing oridination with other transformations, such as relative abundance, log. R" biocLite "phyloseq" biocLite "ggplot2" biocLite "gridExtra" biocLite "edgeR" biocLite "vegan" library phyloseq library ggplot2 library gridExtra library vegan.
Loading required package: permute Loading required package: lattice This is vegan 2. Can view the distance method options with? Subset dataset by phylum s 16 sV1V3. Warning: Transformation introduced infinite values in discrete y-axis. Run 0 stress 0. Procrustes: rmse 1. Similar to previous best Run 4 stress 0. New best solution Similar to previous best Run 6 stress 0.
Similar to previous best Run 8 stress 0. Similar to previous best Run 14 stress 0. Similar to previous best Run 16 stress 0.
Procrustes: rmse 2. Similar to previous best Run 18 stress 0. Procrustes: rmse 4. Similar to previous best Run 19 stress 0. Similar to previous best Run 20 stress 0. Loading required package: limma. Warning in contrasts.This is a demo of how to import amplicon microbiome data into R using Phyloseq and run some basic analyses to understand microbial community diversity and composition accross your samples.
More demos of this package are available from the authors here. This script was created with Rmarkdown. In this tutorial, we are working with illumina 16s data that has already been processed into an OTU and taxonomy table from the mothur pipeline. Phyloseq has a variety of import options if you processed your raw sequence data with a different pipeline. The samples were collected from the Western basin of Lake Erie between May and November at three different locations. The goal of this dataset was to understand how the bacterial community in Lake Erie shifts during toxic algal blooms caused predominantly by a genus of cyanobacteria called Microcystis.
In this tutorial, we will learn how to import an OTU table and sample metadata into R with the Phyloseq package. We will perform some basic exploratory analyses, examining the taxonomic composition of our samples, and visualizing the dissimilarity between our samples in a low-dimensional space using ordinations. Lastly, we will estimate the alpha diversity richness and evenness of our samples. First, we will import the mothur shared file, consensus taxonomy file, and our sample metadata and store them in one phyloseq object.
By storing all of our data structures together in one object we can easily interface between each of the structures. For example, as we will see later, we can use criteria in the sample metadata to select certain samples from the OTU table. The sample metadata is just a basic csv with columns for sample attributes. Here is a preview of what the sample metadata looks like. As you can see, there is one column called SampleID with the names of each of the samples.
The remaining columns contain information on the environmental or sampling conditions related to each sample.
We convert this dataframe into phyloseq format with a simple constructor. The only formatting required to merge the sample data into a phyloseq object is that the rownames must match the sample names in your shared and taxonomy files. Now we have a phyloseq object called moth.
If we wanted to, we could also add a phylogenetic tree or a fasta with OTU representative sequences into this object. At anytime, we can print out the data structures stored in a phyloseq object to quickly view its contents. Now we will filter out Eukaryotes, Archaea, chloroplasts and mitochondria, because we only intended to amplify bacterial sequences.
The minimum sample read count is 1. Since this is not a quantitative analysis, and since we have more Phyla in this dataset than we can reasonably distinguish colors 43! Depending on your dataset and the taxonomic level you are depicting, you can adjust this prune parameter.
In later analyses, we will of course included these taxa, but for now they will just clutter our plot. This plot was created using facets to seperate samples along the y axis by sampling station. This is a great feature of ggplot. This reflects the rare phyla that were removed.Constrained Analysis of Principal Coordinates, capscale.
List of distance method keys supported in distance. Export a distance object as. A sample-wise filter function builder analogous to filterfun.
Retrieve phylogenetic tree phylo -class from object. Retrieve reference sequences XStringSet -class from object.
Microbiome data processing
Convert phyloseq-class into a named list of its non-empty components. Although this distance is Euclidean, for numerical reasons it will sometimes look non-Euclidean, and a correction will be performed. See correction argument. A dpcoa -class object see dpcoa. Pavoine, S. Journal of Theoretical Biology, This is a phyloseq-specific implementation of the Jensen-Shannon Divergence for comparing pairs of microbial communities samples in an experiment.
The expectation is that you have many samples say. The phyloseq data on which to compute the pairwise sample distance matrix.Make a Heatmap on R Studio
Value An object of class dist '' suitable for certain ordination methods and other distance-based analyses. See distance. Jensen-Shannon Divergence and Hilbert space embedding. This function calculates the Fast UniFrac distance for all sample-pairs in a phyloseq-class object. If the tree and contingency table are separate objects, suggested solution is to combine them into an experiment-level class using the phyloseq function.
For example, the following code. Parallelization is possible for UniFrac calculated with the phyloseq-packageand is encouraged in the instances of large trees, many samples, or both. Parallelization has been implemented via the foreach-package.
This means that parallel calls need to be preceded by 2 or more commands that register the parallel backend''. This is acheived via your choice of helper packages. One of the simplest seems to be the doParallel package.The analysis of microbiological communities brings many challenges: the integration of many different types of data with methods from ecology, genetics, phylogenetics, network analysis, visualization and testing.
The data itself may originate from widely different sources, such as the microbiomes of humans, soils, surface and ocean waters, wastewater treatment plants, industrial facilities, and so on; and as a result, these varied sample types may have very different forms and scales of related data that is extremely dependent upon the experiment and its question s.
In general, phyloseq seeks to facilitate the use of R for efficient interactive and reproducible analysis of OTU-clustered high-throughput phylogenetic sequencing data. An overview of phyloseq's intended functionality, goals, and design can be found with free and open access the phyloseq article in PLoS ONE. The most updated examples are posted in our online tutorials from the phyloseq home page. A separate vignette describes analysis tools included in phyloseq along with various examples using included example data.
A quick way to load it is:.
By contrast, this vignette is intended to provide functional examples of the basic data import and manipulation infrastructure included in phyloseq. This includes example code for importing OTU-clustered data from different clustering pipelines, as well as performing clear and reproducible filtering tasks that can be altered later and checked for robustness. The motivation for including tools like this in phyloseq is to save time, and also to build-in a structure that requires consistency across related data tables from the same experiment.
This not only reduces code repetition, but also decreases the likelihood of mistakes during data filtering and analysis. For example, it is intentionally difficult in phyloseq to create an experiment-level object in which a component tree and OTU table have different OTU names.
The import functions, trimming tools, as well as the main tool for creating an experiment-level object, phyloseqall automatically trim the OTUs and samples indices to their intersection, such that these component data types are exactly coherent. The class structure in the phyloseq package follows the inheritance diagram shown in the figure below.
The phyloseq package contains multiple inherited classes with incremental complexity so that methods can be extended to handle exactly the data types that are present in a particular object. Currently, phyloseq uses 4 core data classes. The orientation of a data. These methods can operate on instances of the phyloseq-class, and will stop with an error if the required component data is missing. To use phyloseq in a new R session, it will have to be loaded.
This can be done in your package manager, or at the command line using the library command:. An important feature of phyloseq are methods for importing phylogenetic sequencing data from common taxonomic clustering pipelines. These methods take file pathnames as input, read and parse those files, and return a single object that contains all of the data.
Some additional background details are provided below. The best reproducible examples on importing data with phyloseq can be found on the official data import tutorial page:.
It is distributed in a number of different forms including a pre-installed virtual machine. The most comprehensive class is chosen automatically, based on the input files listed as arguments. At least one argument needs to be provided. The open-source, platform-independent, locally-installed software package, mothurcan also process barcoded amplicon sequences and perform OTU-clustering.
It is extensively documented on the mothur wiki. Currently, there are three different files produced by the mothur package Ver 1. The group file is produced by mothur 's make. Details can be found at its wiki page. The tree file is a phylogenetic tree calculated by mothur. If all three file types are provided, an instance of the phyloseq-class is returned that contains both an OTU abundance table and its associated phylogenetic tree.Instructions to manipulate microbiome data sets using tools from the phyloseq package and some extensions from the microbiome packageincluding subsetting, aggregating and filtering.
A phyloseq object contains OTU table taxa abundancessample metadata, taxonomy table mapping between OTUs and higher-level taxonomic classificationsand phylogenetic tree relations between the taxa. Some of these are optional. Relative abundances note that the input data needs to be in absolute scale, not logarithmic! An alternative method is to impute the zero-inflated unobserved values. Sometimes a multiplicative Kaplan-Meier smoothing spline KMSS replacement, multiplicative lognormal replacement, or multiplicative simple replacement are used.
Use at least n. Aggregate taxa to higher taxonomic levels. This is particularly useful if the phylogenetic tree is missing. Here, we merge all Bacteroides groups into a single group named Bacteroides. Add metadata to a phyloseq object. For reproducibility, we just use the existing metadata in this example, but this can be replaced by another data.
Microbiome data processing Leo Lahti, Sudarshan Shetty et al. Retrieving data elements from phyloseq object A phyloseq object contains OTU table taxa abundancessample metadata, taxonomy table mapping between OTUs and higher-level taxonomic classificationsand phylogenetic tree relations between the taxa.
Pick metadata as data. Sample 54 female CentralEurope o 18 5. Sample 45 female CentralEurope o 13 5. Sample 34 female CentralEurope r 7 6. Sample 52 male US NA 19 5. Sample 52 female CentralEurope o 15 5.
Sample 45 female CentralEurope o 16 5. Merging operations Aggregate taxa to higher taxonomic levels. Rarefaction pseq.