Package index • mia

Association
`getCrossAssociation()`	Calculate correlations between features of two experiments.
`addMediation()` `getMediation()`	Perform mediation analysis
Clustering
`calculateDMN()` `runDMN()` `getDMN()` `bestDMNFit()` `getBestDMNFit()` `calculateDMNgroup()` `performDMNgroupCV()`	Dirichlet-Multinomial Mixture Model: Machine Learning for Microbiome Data
`addCluster()`	Clustering wrapper
Data handling
Agglomeration
`agglomerateByRank()` `agglomerateByVariable()` `agglomerateByRanks()` `splitByRanks()` `unsplitByRanks()`	Agglomerate data using taxonomic information or other grouping
`agglomerateByPrevalence()`	Agglomerate data based on population prevalence
`splitOn()` `unsplitOn()`	Split `TreeSummarizedExperiment` column-wise or row-wise based on grouping variable
Transformation
`transformAssay()`	Transform assay
Other functions
`mergeSEs()`	Merge SE objects into single SE object.
`meltSE()`	Converting a `SummarizedExperiment` object into a long data.frame
`rarefyAssay()`	Subsample Counts
Datasets
`mia-datasets`	mia datasets
`dmn_se`	Twins' microbiome data from 278 individuals
`enterotype`	Human gut microbiome dataset from 22 subjects based on shotgun DNA sequencing
`esophagus`	Human esophageal community from 3 individuals
`GlobalPatterns`	Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.
`HintikkaXOData`	Multiomics dataset from 40 rat samples
`peerj13075`	Skin microbial profiles 58 genetically unrelated individuals
`Tengeler2020`	Gut microbiota profiles of 27 individuals with ADHD and healthy controls
`Tito2024QMP`	Fecal microbiota samples from 589 patients across different colorectal cancer stages
Dataset description
`getPrevalence()` `getPrevalent()` `getRare()` `subsetByPrevalent()` `subsetByRare()` `getPrevalentAbundance()`	Calculation prevalence information for features across samples
`getTop()` `getUnique()` `summarizeDominance()` `summary(<SummarizedExperiment>)`	Summarizing microbiome data
`getDominant()` `addDominant()`	Get dominant taxa
Data loading
`importBIOM()` `convertFromBIOM()` `convertToBIOM()`	Convert a `TreeSummarizedExperiment` object to/from ‘BIOM’ results
`importQIIME2()` `importQZA()`	Import QIIME2 results to `TreeSummarizedExperiment`
`importMothur()`	Import Mothur results as a `TreeSummarizedExperiment`
`importMetaPhlAn`	Import Metaphlan results to `TreeSummarizedExperiment`
`importHUMAnN`	Import HUMAnN results to `TreeSummarizedExperiment`
`importTaxpasta()`	Import taxpasta-specific BIOM results to `TreeSummarizedExperiment`
`convertFromDADA2()`	Create a `TreeSummarizedExperiment` object from ‘DADA2’ results
`convertFromPhyloseq()` `convertToPhyloseq()`	Create a `TreeSummarizedExperiment` object from a phyloseq object
Diversity
Alpha Diversity
`addAlpha()`	Estimate alpha diversity indices
Dissimilarity
`addDissimilarity()` `getDissimilarity()`	Calculate dissimilarities
`addDivergence()` `getDivergence()`	Estimate divergence
Ordination
`getNMDS()` `calculateNMDS()` `addNMDS()` `runNMDS()`	Perform non-metric MDS on sample-level data
`getCCA()` `addCCA()` `getRDA()` `addRDA()` `calculateCCA()` `runCCA()` `calculateRDA()` `runRDA()`	Canonical Correspondence Analysis and Redundancy Analysis
`getDPCoA()` `calculateDPCoA()` `addDPCoA()` `runDPCoA()`	Calculation of Double Principal Correspondance analysis
`getLDA()` `addLDA()`	Latent Dirichlet Allocation
`getNMF()` `addNMF()`	Non-negative Matrix Factorization
Package
`mia` `mia-package`	`mia` Package.
Taxonomy information
`taxonomyRanks()` `taxonomyRankEmpty()` `checkTaxonomy()` `setTaxonomyRanks()` `getTaxonomyRanks()` `getTaxonomyLabels()` `mapTaxonomy()` `IdTaxaToDataFrame()`	Functions for accessing taxonomic data stored in `rowData`.
`getHierarchyTree()` `addHierarchyTree()`	Calculate hierarchy tree
decontam
`isContaminant(<SummarizedExperiment>)` `isNotContaminant(<SummarizedExperiment>)` `addContaminantQC()` `addNotContaminantQC()`	decontam functions
Deprecated functions
`cluster()` `addTaxonomyTree()` `taxonomyTree()` `mergeRows()` `mergeCols()` `mergeFeatures()` `mergeSamples()` `mergeFeaturesByRank()` `mergeFeaturesByPrevalence()` `getExperimentCrossAssociation()` `testExperimentCrossAssociation()` `testExperimentCrossCorrelation()` `getExperimentCrossCorrelation()` `loadFromBiom()` `loadFromQIIME2()` `readQZA()` `loadFromMothur()` `loadFromMetaphlan()` `loadFromHumann()` `countDominantFeatures()` `subsetByRareTaxa()` `subsetByRareFeatures()` `subsetByPrevalentTaxa()` `subsetByPrevalentFeatures()` `countDominantTaxa()` `full_join()` `inner_join()` `left_join()` `right_join()` `plotNMDS()` `estimateDivergence()` `meltAssay()` `transformSamples()` `ZTransform()` `relAbundanceCounts()` `transformCounts()` `transformFeatures()` `getUniqueFeatures()` `getUniqueTaxa()` `getTopFeatures()` `getTopTaxa()` `getRareFeatures()` `getRareTaxa()` `getPrevalentFeatures()` `getPrevalentTaxa()` `subsampleCounts()` `addPerSampleDominantFeatures()` `addPerSampleDominantTaxa()` `perSampleDominantFeatures()` `perSampleDominantTaxa()` `makePhyloseqFromTreeSE()` `makePhyloseqFromTreeSummarizedExperiment()` `makeTreeSummarizedExperimentFromPhyloseq()` `makeTreeSEFromBiom()` `makeTreeSummarizedExperimentFromBiom()` `makeTreeSEFromDADA2()` `makeTreeSummarizedExperimentFromDADA2()` `makeTreeSEFromPhyloseq()` `estimateEvenness()` `estimateRichness()` `estimateDiversity()` `estimateFaith()` `estimateDominance()` `subsetSamples()` `subsetFeatures()` `subsetTaxa()` `relabundance()` `relabundance<-`() `runOverlap()` `calculateOverlap()` `calculateJSD()` `runJSD()` `calculateUnifrac()` `runUnifrac()`	These functions will be deprecated. Please use other functions instead.

Reference

Association

Clustering

Data handling

Agglomeration

Transformation

Other functions

Datasets

Dataset description

Data loading

Diversity

Alpha Diversity

Dissimilarity

Ordination

Package

Taxonomy information

decontam

Deprecated functions