runColDataPCA {scater} | R Documentation

## Perform PCA on column metadata

### Description

Perform a principal components analysis (PCA) on cells, based on the column metadata in a SingleCellExperiment object.

### Usage

    runColDataPCA(
      x,
      ncomponents = 2,
      variables = NULL,
      scale = TRUE,
      outliers = FALSE,
      BSPARAM = ExactParam(),
      BPPARAM = SerialParam(),
      name = "PCA_coldata"
    )

### Arguments

`x` | A SingleCellExperiment object.
`ncomponents` | Numeric scalar indicating the number of principal components to obtain.
`variables` | List of strings or a character vector indicating which variables in `colData(x)` to use. If a list, each entry can also be an AsIs vector or a data.frame, as described in `?retrieveCellInfo`.
`scale` | Logical scalar, should the expression values be standardised so that each feature has unit variance? This will also remove features with standard deviations below 1e-8.
`outliers` | Logical indicating whether outliers should be detected based on PCA coordinates.
`BSPARAM` | A BiocSingularParam object specifying which algorithm should be used to perform the PCA.
`BPPARAM` | A BiocParallelParam object specifying whether the PCA should be parallelized.
`name` | String specifying the name to be used to store the result in the `reducedDims` of the output.

### Details

This function performs PCA on variables from the column-level metadata instead of the gene expression matrix. Doing so can be occasionally useful when other forms of experimental data are stored in the `colData`, e.g., protein intensities from FACs or other cell-specific phenotypic information.

This function is particularly useful for identifying low-quality cells based on QC metrics with `outliers=TRUE`. This uses an “outlyingness” measure computed by `adjOutlyingness` in the robustbase package. Outliers are defined those cells with outlyingness values more than 5 MADs above the median, using `isOutlier`.

### Value

A SingleCellExperiment object containing the first `ncomponent` principal coordinates for each cell. By default, these are stored in the `"PCA_coldata"` entry of the `reducedDims` slot. The proportion of variance explained by each PC is stored as a numeric vector in the `"percentVar"` attribute.

If `outliers=TRUE`, the output `colData` will also contain a logical `outlier` field. This specifies the cells that correspond to the identified outliers.

### Author(s)

Aaron Lun, based on code by Davis McCarthy

### See Also

`runPCA`, for the corresponding method operating on expression data.

### Examples

    example_sce <- mockSCE()
    qc.df <- perCellQCMetrics(example_sce, subset=list(Mito=1:10))
    colData(example_sce) <- cbind(colData(example_sce), qc.df)

    # Can supply names of colData variables to 'variables',
    # as well as AsIs-wrapped vectors of interest.
    example_sce <- runColDataPCA(example_sce, variables=list(
        "sum", "detected", "subsets_Mito_percent", "altexps_Spikes_percent"
    ))
    reducedDimNames(example_sce)
    head(reducedDim(example_sce))

[Package scater version 1.22.0 Index]