as.liger: Converting other classes of data to a liger object

In rliger: Linked Inference of Genomic Experimental Relationships

Converting other classes of data to a liger object

Description

This function converts data stored in SingleCellExperiment (SCE), Seurat object or a merged sparse matrix (dgCMatrix) into a liger object. This is designed for a container object or matrix that already contains multiple datasets to be integerated with LIGER. For individual datasets, please use createLiger instead.

Usage

## S3 method for class 'dgCMatrix'
as.liger(object, datasetVar = NULL, modal = NULL, ...)

## S3 method for class 'SingleCellExperiment'
as.liger(object, datasetVar = NULL, modal = NULL, ...)

## S3 method for class 'Seurat'
as.liger(object, datasetVar = NULL, modal = NULL, assay = NULL, ...)

seuratToLiger(object, datasetVar = NULL, modal = NULL, assay = NULL, ...)

as.liger(object, ...)

Arguments

object	Object.
datasetVar	Specify the dataset belonging by: 1. Select a variable from existing metadata in the object (e.g. colData column); 2. Specify a vector/factor that assign the dataset belonging. 3. Give a single character string which means that all data is from one dataset (must not be a metadata variable, otherwise it is understood as 1.). Default NULL gathers things into one dataset and names it "sample" for dgCMatrix, attempts to find variable "sample" from SCE or "orig.ident" from Seurat.
modal	Modality setting for each dataset. See createLiger.
...	Additional arguments passed to createLiger
assay	Name of assay to use. Default NULL uses current active assay.

Details

For Seurat V5 structure, it is highly recommended that users make use of its split layer feature, where things like "counts", "data", and "scale.data" can be held for each dataset in the same Seurat object, e.g. with "count.ctrl", "count.stim", not merged. If a Seurat object with split layers is given, datasetVar will be ignored and the layers will be directly used.

Value

a liger object.

Examples

# dgCMatrix (common sparse matrix class), usually obtained from other
# container object, and contains multiple samples merged in one.
matList <- rawData(pbmc)
multiSampleMatrix <- mergeSparseAll(matList)
# The `datasetVar` argument expects the variable assigning the sample source
pbmc2 <- as.liger(multiSampleMatrix, datasetVar = pbmc$dataset)
pbmc2


if (requireNamespace("SingleCellExperiment", quietly = TRUE)) {
    sce <- SingleCellExperiment::SingleCellExperiment(
        assays = list(counts = multiSampleMatrix)
    )
    sce$sample <- pbmc$dataset
    pbmc3 <- as.liger(sce, datasetVar = "sample")
    pbmc3
}

if (requireNamespace("Seurat", quietly = TRUE)) {
    seu <- SeuratObject::CreateSeuratObject(multiSampleMatrix)
    # Seurat creates variable "orig.ident" by identifying the cell barcode
    # prefixes, which is indeed what we need in this case. Users might need
    # to be careful and have it confirmed first.
    pbmc4 <- as.liger(seu, datasetVar = "orig.ident")
    pbmc4

    # As per Seurat V5 updates with layered data, specifically helpful udner the
    # scenario of dataset integration. "counts" and etc for each datasets can be
    # split into layers.
    seu5 <- seu
    seu5[["RNA"]] <- split(seu5[["RNA"]], pbmc$dataset)
    print(SeuratObject::Layers(seu5))
    pbmc5 <- as.liger(seu5)
    pbmc5
}

rliger documentation built on Oct. 30, 2024, 1:07 a.m.