plotRLE {scater} | R Documentation

## Plot a relative log expression (RLE) plot

### Description

Produce a relative log expression (RLE) plot of one or more transformations of cell expression values.

### Usage

    plotRLE(object, exprs_mats = list(logcounts = "logcounts"),
      exprs_logged = c(TRUE), colour_by = NULL, style = "minimal",
      legend = "auto", order_by_colour = TRUE, ncol = 1, ...)

### Arguments

`object` | an `SingleCellExperiment` object
`exprs_mats` | named list of expression matrices. Entries can either be a character string, in which case the corresponding expression matrix will be extracted from the SingleCellExperiment `object`, or a matrix of expression values.
`exprs_logged` | logical vector of same length as `exprs_mats` indicating whether the corresponding entry in `exprs_mats` contains logged expression values (`TRUE`) or not (`FALSE`).
`colour_by` | character string defining the column of `colData(object)` to be used as a factor by which to colour the points in the plot. Alternatively, a data frame with one column, containing values to map to colours for all cells.
`style` | character(1), either `"minimal"` (default) or `"full"`, defining the boxplot style to use. `"minimal"` uses Tufte-style boxplots and is fast for large numbers of cells. `"full"` uses the usual `ggplot2` and is more detailed and flexible, but can take a long time to plot for large datasets.
`legend` | character, specifying how the legend(s) be shown? Default is `"auto"`, which hides legends that have only one level and shows others. Alternative is "none" (hide all legends).
`order_by_colour` | logical, should cells be ordered (grouped) by the `colour_by` variable? Default is `TRUE`. Useful for visualising differences between batches or experimental conditions.
`ncol` | integer, number of columns for the facetting of the plot. Default is 1.
`...` | further arguments passed to `geom_boxplot`.

### Details

Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualising such variation in high dimensional data. RLE plots are particularly useful for assessing whether a procedure aimed at removing unwanted variation, i.e. a normalisation procedure, has been successful. These plots, while originally devised for gene expression data from microarrays, can also be used to reveal unwanted variation in single-cell expression data, where such variation can be problematic.

If style is "full", as usual with boxplots, the box shows the inter-quartile range and whiskers extend no more than 1.5 * IQR from the hinge (the 25th or 75th percentile). Data beyond the whiskers are called outliers and are plotted individually. The median (50th percentile) is shown with a white bar.

If style is "minimal", then median is shown with a circle, the IQR in a grey line, and "whiskers" (as defined above) for the plots are shown with coloured lines. No outliers are shown for this plot style.

### Value

a ggplot plot object

### Author(s)

Davis McCarthy

### References

Gandolfo LC, Speed TP. RLE Plots: Visualising Unwanted Variation in High Dimensional Data. arXiv [stat.ME]. 2017. Available: http://arxiv.org/abs/1704.03590

### 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(logcounts(example_sce), 1, function(x) {var(x) == 0})
    example_sce <- example_sce[!drop_genes, ]

    plotRLE(example_sce, list(logcounts= "logcounts", counts = "counts"), c(TRUE, FALSE),
           colour_by = "Mutation_Status", style = "minimal")

    plotRLE(example_sce, list(logcounts = "logcounts", counts = "counts"), c(TRUE, FALSE),
           colour_by = "Mutation_Status", style = "full",
           outlier.alpha = 0.1, outlier.shape = 3, outlier.size = 0)

[Package scater version 1.8.4 Index]