Data import

Functions for importing external data and converting other R object as phyloseq or reverse converting

import_dada2()

Import function to read the the output of dada2 as phyloseq object

import_picrust2()

Import function to read the output of picrust2 as phyloseq object

import_qiime2()

Import function to read the the output of dada2 as phyloseq object

reexports

Objects exported from other packages

phyloseq2DESeq2()

Convert phyloseq-class object to DESeqDataSet-class object

phyloseq2edgeR()

Convert phyloseq data to edgeR DGEList object

phyloseq2metagenomeSeq() otu_table2metagenomeSeq()

Convert phyloseq data to MetagenomeSeq MRexperiment object

Microbiome marker

S4 class and methods for microbiomeMarker

microbiomeMarker()

Build microbiomeMarker-class objects

show(<microbiomeMarker>)

The main class for microbiomeMarker data

marker_table-class

The S4 class for storing microbiome marker information

marker_table()

Build or access the marker_table

`marker_table<-`()

Assign marker_table to object

`otu_table<-`(<microbiomeMarker>,<otu_table>) `otu_table<-`(<microbiomeMarker>,<phyloseq>) `otu_table<-`(<microbiomeMarker>,<microbiomeMarker>)

Assign a new OTU table

abundances()

Extract taxa abundances

nmarker()

Get the number of microbiome markers

show(<postHocTest>)

The postHocTest Class, represents the result of post-hoc test result among multiple groups

Functions reexports from phyloseq

reexports

Objects exported from other packages

Normalization

abundances()

Extract taxa abundances

transform_abundances()

Transform the taxa abundances in otu_table sample by sample

normalize(<phyloseq>) normalize(<otu_table>) normalize(<data.frame>) normalize(<matrix>) norm_rarefy() norm_tss() norm_css() norm_rle() norm_tmm() norm_clr() norm_cpm()

Normalize the microbial abundance data

Differential analysis

Functions for identifying the microbiome markers

run_aldex()

Perform differential analysis using ALDEx2

run_ancom()

Perform differential analysis using ANCOM

run_ancombc()

Differential analysis of compositions of microbiomes with bias correction (ANCOM-BC).

run_deseq2()

Perform DESeq differential analysis

run_edger()

Perform differential analysis using edgeR

run_lefse()

Liner discriminant analysis (LDA) effect size (LEFSe) analysis

run_limma_voom()

Differential analysis using limma-voom

run_marker()

Find makers (differentially expressed metagenomic features)

run_metagenomeseq()

metagenomeSeq differential analysis

run_posthoc_test()

Post hoc pairwise comparisons for multiple groups test.

run_simple_stat()

Simple statistical analysis of metagenomic profiles

run_sl()

Identify biomarkers using supervised leaning (SL) methods

run_test_multiple_groups()

Statistical test for multiple groups

run_test_two_groups()

Statistical test between two groups

Visualization

plot_ef_bar() plot_ef_dot()

bar and dot plot of effect size of microbiomeMarker data

plot_abundance()

plot the abundances of markers

plot_cladogram()

plot cladogram of micobiomeMaker results

plot_heatmap()

Heatmap of microbiome marker

plot_postHocTest()

postHocTest plot

plot_sl_roc()

ROC curve of microbiome marker from supervised learning methods

Example data

data-caporaso

16S rRNA data from "Moving pictures of the human microbiome"

data-cid_ying

16S rRNA data of 94 patients from CID 2012

data-ecam

Data from Early Childhood Antibiotics and the Microbiome (ECAM) study

data-enterotypes_arumugam

Enterotypes data of 39 samples

data-kostic_crc

Data from a study on colorectal cancer (kostic 2012)

data-oxygen

Oxygen availability 16S dataset, of which taxa table has been summarized for python lefse input

data-pediatric_ibd

IBD stool samples

data-spontaneous_colitis

This is a sample data from lefse python script, a 16S dataset for studying the characteristics of the fecal microbiota in a mouse model of spontaneous colitis.

Miscellaneous

aggregate_taxa()

Aggregate Taxa

subset_marker()

Subset microbiome markers

summarize_taxa()

Summarize taxa into a taxonomic level within each sample