addDimReduction {dittoSeq} | R Documentation

## Add any dimensionality reduction space to a SingleCellExperiment object containing bulk or single-cell data

### Description

Add any dimensionality reduction space to a SingleCellExperiment object containing bulk or single-cell data

### Usage

    addDimReduction(object, embeddings, name, key = .gen_key(name))

### Arguments

`object` | the bulk or single-cell `SingleCellExperiment` object to add the dimensionality reduction to. (dittoSeq utilizes the SingleCellExperiment object even for bulk data because it provides a convenient slots for all data that dittoSeq requires)
`embeddings` | a numeric matrix or matrix-like object, with number of rows equal to ncol(object), containing the coordinates of all cells / samples within the dimensionality reduction space.
`name` | String name for the reduction slot. Example: "pca". This will become the name of the slot, and what should be provided to the `reduction.use` input when making a `dittoDimPlot`. When the name given is the same as that of a slot that already exists inside the `object`, the previous slot is replaced with the newly provided data.
`key` | String, like "PC", which sets the default axes-label prefix when this reduction is used for making a `dittoDimPlot`. If nothing is provided, a key will be automatically generated.

### Value

Outputs a `SingleCellExperiment` object with an added or replaced dimensionality reduction slot.

### Author(s)

Daniel Bunis

### See Also

`addPrcomp` for a prcomp specific PCA import wrapper

`importDittoBulk` for initial import of bulk RNAseq data into dittoSeq as a `SingleCellExperiment`.

`dittoDimPlot` for visualizing how samples group within added dimensionality reduction spaces

### Examples

    example("importDittoBulk", echo = FALSE)

    # Calculate PCA
    #   NOTE: This is typically not done with all genes in the dataset.
    #   The inclusion of this example code is not an endorsement of a particular
    #   method of PCA. Consult yourself, a bioinformatician, or literature for
    #   tips on proper techniques.
    embeds <- prcomp(t(logcounts(myRNA)), center = TRUE, scale = TRUE)$x

    myRNA <- addDimReduction(
        object = myRNA,
        embeddings = embeds,
        name = "pca",
        key = "PC")

    # Visualize conditions metadata on a PCA plot
    dittoDimPlot(myRNA, "conditions", reduction.use = "pca", size = 3)

[Package dittoSeq version 1.0.2 Index]