MultimodalExperiment

MultimodalExperiment is an S4 class that integrates bulk and single-cell experiment data; it is optimally storage-efficient, and its methods are exceptionally fast. It effortlessly represents multimodal data of any nature and features normalized experiment, subject, sample, and cell annotations, which are related to underlying biological experiments through maps. Its coordination methods are opt-in and employ database-like join operations internally to deliver fast and flexible management of multimodal data.

Installation

To install MultimodalExperiment from Bioconductor, use BiocManager as follows.

BiocManager::install("MultimodalExperiment")

To install MultimodalExperiment from GitHub, use BiocManager as follows.

BiocManager::install("schifferl/MultimodalExperiment", dependencies = TRUE, build_vignettes = TRUE)

Most users should simply install MultimodalExperiment from Bioconductor.

Cheat Sheet

Usage

library(MultimodalExperiment)

ME <-
    MultimodalExperiment()

bulkExperiments(ME) <-
    ExperimentList(
        pbRNAseq = pbRNAseq
    )

singleCellExperiments(ME) <-
    ExperimentList(
        scADTseq = scADTseq,
        scRNAseq = scRNAseq
    )

subjectMap(ME)[["subject"]] <-
    "SUBJECT-1"

sampleMap(ME)[["subject"]] <-
    "SUBJECT-1"

cellMap(ME)[["sample"]] <-
    "SAMPLE-1"

ME <-
    propagate(ME)

experimentData(ME)[["published"]] <-
    c(NA_character_, "2018-11-19", "2018-11-19") |>
    as.Date()

subjectData(ME)[["condition"]] <-
    as.character("healthy")

sampleData(ME)[["sampleType"]] <-
    as.character("peripheral blood mononuclear cells")

cellType <- function(x) {
    if (x[["CD4"]] > 0L) {
        return("T Cell")
    }

    if (x[["CD14"]] > 0L) {
        return("Monocyte")
    }

    if (x[["CD19"]] > 0L) {
        return("B Cell")
    }

    if (x[["CD56"]] > 0L) {
        return("NK Cell")
    }

    NA_character_
}

cellData(ME)[["cellType"]] <-
    experiment(ME, "scADTseq") |>
    apply(2L, cellType)

ME

## MultimodalExperiment with 1 bulk and 2 single-cell experiment(s).
## 
## experimentData: DataFrame with 3 row(s) and 1 column(s).
##           published
##              <Date>
## pbRNAseq         NA
## scADTseq 2018-11-19
## scRNAseq 2018-11-19
## 
## subjectData: DataFrame with 1 row(s) and 1 column(s).
##             condition
##           <character>
## SUBJECT-1     healthy
## 
## sampleData: DataFrame with 1 row(s) and 1 column(s).
##                                  sampleType
##                                 <character>
## SAMPLE-1 peripheral blood mononuclear cells
## 
## cellData: DataFrame with 5000 row(s) and 1 column(s).
##                     cellType
##                  <character>
## AAACCTGAGAGCAATT      B Cell
## AAACCTGAGGCTCTTA     NK Cell
## ...                      ...
## TTTGTCATCATGGTCA     NK Cell
## TTTGTCATCTCGTTTA     NK Cell
## 
## bulkExperiments: ExperimentList with 1 bulk experiment(s).
## [1] pbRNAseq: matrix with 3000 row(s) and 1 column(s).
## 
## singleCellExperiments: ExperimentList with 2 single-cell experiment(s).
## [1] scADTseq: matrix with 8 row(s) and 5000 column(s).
## [2] scRNAseq: matrix with 3000 row(s) and 5000 column(s).
## 
## Need help? Try browseVignettes("MultimodalExperiment").
## Publishing? Cite with citation("MultimodalExperiment").

Contributing

Please note that MultimodalExperiment is released with a code of conduct. By contributing, you agree to abide by its terms.