01. microbiome_pipeline

Pipeline

In all cases, the first thing that needs to be done is to check the quality of your data. For this, the microbiomeutilities package provides a single function called microbiome_pipeline that puts together base, phyloseq and microbiome R package functions to provide the user with data qc visualisation. The microbiome_pipeline creates and HTML report that is saved in the specified output directory. In addition, this function saves intermediate and final outputs in following folder:

1. QC
2. AlphaDiversity
3. Ordinations
4. Others
5. PhyloseqObjects

The best way to run this function is to:

First in RStudio.

load the required libraries

library(RColorBrewer)
library(ape)
library("microbiomeutilities")
library(data.table)
library(microbiome)
library(phyloseq)

Once you have these then run the following command.

microbiome_pipeline(otufile = "mybiom.biom",  
                    mapping = "mapping.csv",              
                    taxonomy = NULL,                    
                    treefilename = "myTree.tre",            
                    type = "biom",
                    work_dir = "F:/path/to/my/Rproject",
                    out_dir = "F:/path/to/my/Rproject/myoutput", 
                    VariableA = "MainVariable",               
                    VariableB = "SecondVariable",           
                    UnConstOrd = TRUE,                           
                    heatmap = TRUE,                          
                    filterCount = 4,                           
                    filterPrev = 0.01,                          
                    col.palette = "Paired",                    
                    filterpseq = TRUE,                          
                    samsize = NA,
                    projectname = "MY Project",
                    author = "MY Name")

The use of Rmarkdown is that once you knit it to a desired file type, html, pdf, doc etc. you can see all the QC messages.

For more information check on Rmarkdown RStudio

After successfully running this you can check the folders for HTML report and other output. This will serve as a reference to make appropriate choice for filtering, normalisation, transformation, and statistical tests.

Credits to developers of:
Phyloseq
microbiome
data.table
ape
vegan
ggplot2
ggpubr

For issues please comment on the microbiomeutilities issues page