| Environment | Status |
|---|---|
| BioC-release |  |
| BioC-devel |  |
The alabaster.mae package implements methods for saving and loading MultiAssayExperiment objects under the alabaster framework.
It provides a language-agnostic method for serializing the sample mappings, sample data, and the various SummarizedExperiment instances nested within the MAE.
To get started, install the package and its dependencies from Bioconductor:
# install.packages("BiocManager")
BiocManager::install("alabaster.mae")To demonstrate, let's create a mildly complicated MAE containing RNA-seq and ChIP-seq data with partial overlaps:
library(SummarizedExperiment)
rna.counts <- matrix(rpois(60, 10), ncol=6)
colnames(rna.counts) <- c("disease1", "disease2", "disease3", "control1", "control2", "control3")
rownames(rna.counts) <- c("ENSMUSG00000000001", "ENSMUSG00000000003", "ENSMUSG00000000028",
"ENSMUSG00000000031", "ENSMUSG00000000037", "ENSMUSG00000000049", "ENSMUSG00000000056",
"ENSMUSG00000000058", "ENSMUSG00000000078", "ENSMUSG00000000085")
rna.se <- SummarizedExperiment(list(counts=rna.counts))
colData(rna.se)$disease <- rep(c("disease", "control"), each=3)
chip.counts <- matrix(rpois(100, 10), ncol=4)
colnames(chip.counts) <- c("disease1", "disease2", "control1", "control3")
chip.peaks <- GRanges("chr1", IRanges(1:25*100+1, 1:25*100+100))
chip.se <- SummarizedExperiment(list(counts=chip.counts), rowRanges=chip.peaks)
library(MultiAssayExperiment)
mapping <- DataFrame(
primary = c(colnames(rna.se), colnames(chip.se)), # sample identifiers
assay = rep(c("rnaseq", "chipseq"), c(ncol(rna.se), ncol(chip.se))), # experiment name
colname = c(colnames(rna.se), colnames(chip.se)) # column names inside each experiment
)
mae <- MultiAssayExperiment(list(rnaseq=rna.se, chipseq=chip.se), sampleMap=mapping)Now we can just save it to file:
library(alabaster.mae)
tmp <- tempfile()
saveObject(mae, tmp)And easily load it back:
roundtrip <- readObject(tmp)
roundtrip
## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] rnaseq: SummarizedExperiment with 10 rows and 6 columns
## [2] chipseq: RangedSummarizedExperiment with 25 rows and 4 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat files