findImportantPCs {scater} | R Documentation

## Find most important principal components for a given variable

### Description

Find most important principal components for a given variable

### Usage

    findImportantPCs(object, variable = "total_features",
      plot_type = "pcs-vs-vars", exprs_values = "logcounts", ntop = 500,
      feature_set = NULL, scale_features = TRUE, theme_size = 10)

### Arguments

`object` | an SCESet object containing expression values and experimental information. Must have been appropriately prepared.
`variable` | character scalar providing a variable name (column from `colData(object)`) for which to determine the most important PCs.
`plot_type` | character string, indicating which type of plot to produce. Default, `"pairs-pcs"` produces a pairs plot for the top 5 PCs based on their R-squared with the variable of interest. A value of `"pcs-vs-vars"` produces plots of the top PCs against the variable of interest.
`exprs_values` | which slot of the `assayData` in the `object` should be used to define expression? Valid options are "counts", "tpm", "fpkm" and "logcounts" (default), or anything else in the object added manually by the user.
`ntop` | numeric scalar indicating the number of most variable features to use for the PCA. Default is `500`, but any `ntop` argument is overrided if the `feature_set` argument is non-NULL.
`feature_set` | character, numeric or logical vector indicating a set of features to use for the PCA. If character, entries must all be in `rownames(object)`. If numeric, values are taken to be indices for features. If logical, vector is used to index features and should have length equal to `nrow(object)`.
`scale_features` | logical, should the expression values be standardised so that each feature has unit variance? Default is `TRUE`.
`theme_size` | numeric scalar providing base font size for ggplot theme.

### Details

Plot the top 5 or 6 most important PCs (depending on the `plot_type` argument for a given variable. Importance here is defined as the R-squared value from a linear model regressing each PC onto the variable of interest.

### Value

a `ggplot` plot object

### Examples

    data("sc_example_counts")
    data("sc_example_cell_info")
    example_sce <- SingleCellExperiment(
    assays = list(counts = sc_example_counts), colData = sc_example_cell_info)
    example_sce <- normalize(example_sce)
    drop_genes <- apply(exprs(example_sce), 1, function(x) {var(x) == 0})
    example_sce <- example_sce[!drop_genes, ]
    example_sce <- calculateQCMetrics(example_sce)
    findImportantPCs(example_sce, variable="total_features")

[Package scater version 1.6.3 Index]