Append linkmaps to SummarizedExperiment side information

getModules and addModules allow to retrieve or append a linkmap to the side information of a SummarizedExperiment (SE) object. This makes ariadne interoperable with SE-based data analysis.

getModules(x, ...)

addModules(x, ...)

# S4 method for class 'SummarizedExperiment'
addModules(x, modules, by = "rows", key = "row.names", as = "ids")

# S4 method for class 'SummarizedExperiment'
getModules(x, modules, by = "rows", key = "row.names", as = "ids")

Arguments

x

The rowData or colData of a SummarizedExperiment object.

...

Unused.

modules

data.frame. A linkmap as returned by weavePath or weaveComplex. Its first and second columns must contain elements to match to key and the target modules, respectively.

by

Character scalar A string indicating whether to append modules to the "rows" or "cols" side information of x. (Default: "rows")

key

Character vector A vector specifying one or more variables of x side information based on which modules should be appended. (Default: "row.names")

as

Character scalar A string specifying whether modules ids or names should be used. For the latter, a third column with names must exist in modules. (Default: "ids")

Value

An object of the same type as x with additional columns in its side information, each containing information on membership to a certain module.

Examples

library(mia)
#> Loading required package: MultiAssayExperiment
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#> 
#> Attaching package: ‘generics’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
#>     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
#>     unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
#> Loading required package: SingleCellExperiment
#> Loading required package: TreeSummarizedExperiment
#> Loading required package: Biostrings
#> Loading required package: XVector
#> 
#> Attaching package: ‘Biostrings’
#> The following object is masked from ‘package:base’:
#> 
#>     strsplit
#> This is mia version 1.19.8
#> - Online documentation and vignettes: https://microbiome.github.io/mia/
#> - Online book 'Orchestrating Microbiome Analysis (OMA)': https://microbiome.github.io/OMA/docs/devel/
library(miaViz)
#> Loading required package: ggplot2
#> Warning: package ‘ggplot2’ was built under R version 4.7.0
#> Loading required package: ggraph
#> 
#> Attaching package: ‘miaViz’
#> The following object is masked from ‘package:mia’:
#> 
#>     plotNMDS

# Import datasets
data("Tengeler2020", package = "mia")
data("butyrate", package = "ariadne")

# Rename experiment object
tse <- Tengeler2020

# Get butyrate-producer module membership
modules <- getModules(tse, butyrate, key = "Genus")

# Get modules based on multiple variables given in order of priority
modules <- getModules(tse, butyrate, key = c("Genus", "Family"))

# Add modules to experiment as names instead of ids
tse <- addModules(tse, butyrate, key = "Genus", as = "names")

# Generate relative abundance table
tse <- transformAssay(tse, method = "relabundance")

# Agglomerate features by membership to butyrate-producer module
mod.se <- agglomerateByModule(tse, by = "rows", group = "butyrate")

# Plot relative abundance of butyrate producers
plotAbundance(mod.se, assay.type = "relabundance")
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).