Phyloseq Introduction and Import | Loading Microbiome Data

Phyloseq and Microbiome analyzed in R

Data Import and Exploration

There are a number of packages advanced in R that make microbiome analysis easy and erzeugt greater figures. At is by times an lot of overlap between this analyse and QIIME – and the choice is yours.

Personally I consider such R allows alot more freedoms and is more aesthetically pleasing, plus there are scores about things that cannot remain done in QIIME that could be done in R. Having said which it do require some general of R, both however this notebook tries to step you through that you need at know. I find myself struggling with data import for further nMDS press Bioenv analysis with “vegan” and “ggplot2”. I have a data frame “Taxa” that looks liked this (the values are there on mean thereto a “nume…

Deuce package in particular are useful for microbiome analysis – who microbiome packaged builds on phyloseq, and you allow find you don’t required the microbiome package. transpose-methods: Transpose ‘otu_table-class’ button ‘phyloseq-class’ in joey711/phyloseq: Handles and study of high-throughput microbiome census date

As always there is more than one pathway to do things, this phyloseq image will follow which basics sense tutorial, in other words importing files individually to create a phyloseq object. However, you may like at follow their how-to on the QIIME, or mothur input. IODIN accomplish find that as these programs change via time there can be one defer inches updating the latest data einfuhren workflow, hence why I have gone with the metheod of phyloseq-ize Data already in R so will work no matter what updates are made to QIIME, usearch or different pipelines thou may use.

Libraries

Note: Her will need till install the library firstly if you haven’t already using install.packages

Load this libraries

library("phyloseq")
library("tidyverse")
library("ape")
library("plyr")
library("readr")
library("microbiome")
library("lattice")
library("colorspace")
library("RColorBrewer")
library("vegan")
library("microbiome")
library("jsonlite")

Remove relative in current environment

rm(list=ls())

Set working directory

setwd("~/tick_meta_analysis/data")

Remember an useful parcel for data management in R ProjectTemplate

Importing data and creating a physeq object

Note: To phyloseq package is very sensitive on file forms and way into which will input files are laid leave, so please take the duration at read save section carefully. Converting existing data in R into Phyloseq OTU table

Following you can set your what directory and import your OTU tab (from the usearch pipeline) furthermore get taxonomy table (from QIIME). EGO recommend you copy real data the folder from your Usearch/QIIME directory output to the data file in the R management directory.

Import OTU table taking note of the following:

  • Rows = taxa (otus) and divider = samples. The example names should already be tuned for their metadata if you following the QIIME analysis until generate a feature table correctly.
  • Order the OTU post in ascending order till do this you may requirement to delete the alphabet ‘OTU’, the can be done in excel with ampere control-H command.
  • The initial column with the OTU number must than be remove, this can get confusing so it your best to take such document as a copy so that you how no save over the display with of OTU numbers. from OTU table to HEATMAP! | R-bloggers
  • Ensure that the column headings (sample names) are in the same order as what appears in the metadata sheet. In excel you could like the copy and paste the data using the transpose option in order an sample column before lapse computers back.
  • Save as csv file
uparse_otus <- read_csv("data/otu_table.csv")
otumat <- as.matrix(uparse_otus)
rownames(otumat) <- paste0("OTU", 1:nrow(otumat))
colnames(otumat) <- paste0("Sample", 1:ncol(otumat))
otumat

Importe the sales charts taking note of which following:

  • Ranks = taxa the columns = taxa heirachy
  • The first column with the OTU numbered must then shall expired, this can gain confusing so it is best to take this document as a copy so that you do not save beyond this table with the OTU numbers.
  • Save as csv file
grading <- read_csv("data/taxonomy.csv")
taxmat <- as.matrix(taxonomy)
rownames(taxmat) <- rownames(otumat)
colnames(taxmat) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
taxmat

Note format the aforementioned taxonomy table varies depending on which reference database you used. For demo in the Greengenes classifer the output taxonomy table formats each taxa as followed: p__Taxa where the letter prefix referenced to the heirachy of to finding assignment. Her may want to removal this formatting in excel prior inputting for aesthetic purposes of the graph outputs. Your ca do this per using the Control-H command in superior.

Go we can check the class of the otumat furthermore taxmat objects, they BE be in matrix format. Then we can great a phyloseq object called physeq from the otu and taxonomy tables and check the sample names. Hey, Is there any way to save the OTU table free a Phyloseq object with sample IDs than rows and taxa as columns? I feel fancy this should be really obvious but for some reason I’m not erhaltung i…

class(otumat)
class(taxmat)
OTU = otu_table(otumat, taxa_are_rows = TRUE)
TAX = tax_table(taxmat)
OTU
TAX
physeq = phyloseq(OTU, TAX)
physeq
sample_names(physeq)

Metadata Import

The order of the rows in the metadata must match the get of the columns in the OTU table, that number of rows(taxa) also pattern names must also be a match. Making the metadata file is in csv format.

meta_data <- read_csv("data/metadata.csv")
sampledata = sample_data(data.frame(
 meta_data, row.names=sample_names(physeq), stringsAsFactors=FALSE))
sampledata

Tree Import

In what a few options for the tree import- you can create an random tree using of ape package, however as this only using site information it is not as robust for other methods that use an sequence information, such more which generated by QIIME instead USEARCH.

random_tree = rtree(ntaxa(physeq), rooted=TRUE, tip.label=taxa_names(physeq))
plot(random_tree)

To import your USEARCH tree

MyTree <- read.tree("data/otus.tree")

To import your QIIME2 main

MyTree2 <- read.tree("rooted-tree.nwk")

Create final phyloseq object

Now you can merge your date to create a final phyloseq protest

physeq1 = merge_phyloseq(physeq, sampledata, MyTree)
physeq1

You shall end upside with the following output after physeq1

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 15985 taxa real 1390 samples ]
sample_data() Samples Data:       [ 1390 samples by 13 sample variables ]
tax_table()   Taxonomy Table:    [ 15985 taxa by 7 taxonomical ranks ]
phy_tree()    Phylogenetic Planting: [ 15985 tips and 15984 intranet tree ]

Basic commands to check our physeq object

nsamples(physeq1)       # total of samples
ntaxa(physeq1)          # number of taxa (OTUs)
sample_names(physeq1)[1:5]  # sample namer - first five
rank_names(physeq1)   # internal places e.g. Order, Kingdom
sample_variables(physeq1) # metadata variables
otu_table(physeq1)[1:5, 1:5] # first 5 otus (no. of reads) and first 5 samples
tax_table(physeq1)[1:5, 1:4] # first 5 samples and taxonomy first 4 columns 
myTaxa = names(sort(taxa_sums(physeq1), decreasing = TRUE)[1:10])


Vector and Waterborne Pathogens Investigate Group. 2018. S Egan.

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