Function reference • MicrobiomeAnalysis

Data import Functions for importing external data and converting other R object as phyloseq or reverse converting
`import_SE()`	Convert inputs into SummarizedExperiment object
`import_dada2()`	Convert the output of dada2 into phyloseq object
`import_qiime2()`	Convert the qiime2 output of dada2 into phyloseq object
`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
Miscellaneous Functions to manipulate object
`aggregate_taxa()`	Aggregate Taxa
`summarize_taxa()`	Summarize taxa into a taxonomic level within each sample
`subset_marker()`	Subset microbiome markers
Data Processing Functions to precess data before test
`abundances()`	Extract taxa abundances
`transform_abundances()`	Transform the abundances in `profile` sample by sample
`trim_prevalence()`	Trimming samples or features whose prevalence is less than threshold
`filter_abundance()`	Filtering feature who is low relative abundance or unclassified
`impute_abundance()`	Imputation methods on missing value
`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
`scale_variables()`	Data scaling adjusts each variable/feature
Example data
`data-Zeybel_2022_gut` `Zeybel_2022_gut`	gut microbiota from Zeybel et al. - 2022 paper
`data-Zeybel_2022_gut_paired` `Zeybel_2022_gut_paired`	Paired gut microbiota from Zeybel et al. - 2022 paper
`data-Zeybel_2022_metabolite` `Zeybel_2022_metabolite`	fecal metabolites from Zeybel et al. - 2022 paper
`data-Zeybel_2022_metabolite_paired` `Zeybel_2022_metabolite_paired`	Paired fecal metabolites from Zeybel et al. - 2022 paper
`data-Zeybel_2022_oral` `Zeybel_2022_oral`	oral microbiota from Zeybel et al. - 2022 paper
`data-Zeybel_2022_oral_paired` `Zeybel_2022_oral_paired`	Paired oral microbiota from Zeybel et al. - 2022 paper
`data-Zeybel_2022_protein` `Zeybel_2022_protein`	serum protein from Zeybel et al. - 2022 paper
`data-Zeybel_2022_protein_paired` `Zeybel_2022_protein_paired`	Paired serum protein from Zeybel et al. - 2022 paper
`data-caporaso` `caporaso`	16S rRNA data from "Moving pictures of the human microbiome"
`data-ecam` `ecam`	Data from Early Childhood Antibiotics and the Microbiome (ECAM) study
`data-enterotypes_arumugam` `enterotypes_arumugam`	Enterotypes data of 39 samples
Community Analysis Functions to investigate association between variables and microbiota community
`run_betadisper()`	Multivariate homogeneity of groups dispersions (variances)
`run_ANOSIM()`	Analysis of Similarity (ANOSIM)
`run_distance()`	Calculate the distance among samples
`run_MANTEL()`	Mantel Test (MANTEL)
`run_MRPP()`	Multi-response Permutation Procedures (MRPP)
`run_PERMANOVA()`	Permutational multivariate analysis of variance (PERMANOVA)
`run_ord()`	Ordination for microbiota data
`get_alphaindex()`	Calculating index of Alpha diversity on microbiota 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_metagenomeseq()`	metagenomeSeq differential analysis
`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
`run_marker()`	Find makers (differentially expressed metagenomic features)
`postHocTest()`	Build postHocTest object
`run_metabolomeDA()`	Perform differential analysis on metabolomic data
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
`[`(<marker_table>,<ANY>,<ANY>,<ANY>)	Extract `marker_table` object
Statistical tookits Functions to do statistical test
`calculate_median_abundance()`	Calculate the Median abundance of features per group (two groups)
`calculate_mean_abundance()`	Calculate the Mean abundance of features per group (two groups)
`calculate_MeanRank_abundance()`	Calculate the Rank Mean abundance of features per group(two groups)
`calculate_geometricmean_abundance()`	Calculate the Mean abundance of features per group (two groups)
`calculate_occurrence_taxa()`	Calculate the Occurrence of features per group (two groups)
`run_OddRatio()`	95% confidential interval Odds Ratio
`run_CI()`	Confidence Interval
`run_group.CI()`	Group Confidence Interval
`run_STDERR()`	Standard Error
`run_group.STDERR()`	Group Standard Error Interval
`run_summarySE()`	Summarizes data for mean median sd etc
`insertRow()`	Insert Row into a Matrix
`insertCol()`	Insert Row into a Matrix
Additional tookits Additional Functions
`default_color()`	generate multiple colors for visualization
`check_sample_names()`	check whether samples' names start with numeric and then paste "S_"
`get_GUniFrac()`	Generalized UniFrac distances for comparing microbial communities
`get_eigValue()`	Extract and visualize the eigenvalues/variances of dimensions
`confounder()`	Confounder analysis
Functions to visualization visualization Functions
`plot_ord()`	Visualization of Ordination results with scatterplot
`plot_volcano()`	plot the differential analysis results by volcano plot

Reference

Data import

Miscellaneous

Data Processing

Example data

Community Analysis

Differential analysis

Microbiome marker

Statistical tookits

Additional tookits

Functions to visualization