Function reference • microbiomeMarker

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` `ntaxa` `nsamples` `otu_table` `sample_data` `tax_table` `taxa_names` `sample_names` `%>%` `import_qiime` `import_mothur` `import_biom`	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
`[`(<marker_table>,<ANY>,<ANY>,<ANY>)	Extract `marker_table` object
`show(<postHocTest>)`	The postHocTest Class, represents the result of post-hoc test result among multiple groups
`postHocTest()`	Build postHocTest object
`extract_posthoc_res()`	Extract results from a posthoc test
Functions reexports from phyloseq
`reexports` `ntaxa` `nsamples` `otu_table` `sample_data` `tax_table` `taxa_names` `sample_names` `%>%` `import_qiime` `import_mothur` `import_biom`	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
`confounder()`	Confounder analysis
`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
Comparison of different methods
`compare_DA()`	Comparing the results of differential analysis methods by Empirical power and False Discovery Rate
`summary(<compareDA>)`	Summary differential analysis methods comparison results
Visualization
`plot_ef_bar()` `plot_ef_dot()`	bar and dot plot of effect size of microbiomeMarker data
`plot(<compareDA>)`	Plotting DA comparing result
`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` `caporaso`	16S rRNA data from "Moving pictures of the human microbiome"
`data-cid_ying` `cid_ying`	16S rRNA data of 94 patients from CID 2012
`data-ecam` `ecam`	Data from Early Childhood Antibiotics and the Microbiome (ECAM) study
`data-enterotypes_arumugam` `enterotypes_arumugam`	Enterotypes data of 39 samples
`data-kostic_crc` `kostic_crc`	Data from a study on colorectal cancer (kostic 2012)
`data-oxygen` `oxygen`	Oxygen availability 16S dataset, of which taxa table has been summarized for python lefse input
`data-pediatric_ibd` `pediatric_ibd`	IBD stool samples
`data-spontaneous_colitis` `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

Reference

Data import

Microbiome marker

Functions reexports from phyloseq

Normalization

Differential analysis

Comparison of different methods

Visualization

Example data

Miscellaneous