ExperimentColorMap-class {iSEE} | R Documentation

## ExperimentColorMap class

### Description

ExperimentColorMap class

### Usage

    ExperimentColorMap(
      assays = list(),
      colData = list(),
      rowData = list(),
      all_discrete = list(assays = NULL, colData = NULL, rowData = NULL),
      all_continuous = list(assays = NULL, colData = NULL, rowData = NULL),
      global_discrete = NULL,
      global_continuous = NULL,
      ...
    )

### Arguments

`assays` | List of colormaps for `assays`.
`colData` | List of colormaps for `colData`.
`rowData` | List of colormaps for `rowData`.
`all_discrete` | Colormaps applied to all undefined categorical `assays`, `colData`, and `rowData`, respectively.
`all_continuous` | Colormaps applied to all undefined continuous `assays`, `colData`, and `rowData`, respectively.
`global_discrete` | Colormap applied to all undefined categorical covariates.
`global_continuous` | Colormap applied to all undefined continuous covariates.
`...` | additional arguments passed on to the `ExperimentColorMap` constructor

### Details

Colormaps must all be functions that take at least one argument: the number of (named) colours to return as a `character` vector. This argument may be ignored in the body of the colormap function to produce constant colormaps.

### Value

An object of class `ExperimentColorMap`

### Categorical colormaps

The default categorical colormap emulates the default ggplot2 categorical color palette (Credit: https://stackoverflow.com/questions/8197559/emulate-ggplot2-default-color-palette). This palette returns a set of colors sampled in steps of equal size that correspond to approximately equal perceptual changes in color:

    function(n) {
        hues=seq(15, 375, length=(n + 1))
        hcl(h=hues, l=65, c=100)[seq_len(n)]
    }

To change the palette for all categorical variables, users must supply a colormap that returns a similar value; namely, an unnamed character vector of length `n`. For instance, using the base R palette `rainbow.colors`

    function(n) {
        rainbow(n)
    }

### Accessors

In the following code snippets, `x` is an `ExperimentColorMap` object.

`assayColorMap(x, i, ..., discrete=FALSE)`:

  Get an `assays` colormap for the specified assay `i`.

`colDataColorMap(x, i, ..., discrete=FALSE)`:

  Get a `colData` colormap for the specified `colData` column `i`.

`rowDataColorMap(x, i, ..., discrete=FALSE)`:

  Get a `rowData` colormap for the specified `rowData` column `i`.

If the colormap for `i` cannot be found, one of the default colormaps is returned. In this case, `discrete` is a logical scalar that indicates whether the colormap should be categorical. The more specialized default is first attempted - e.g., for `assayColorMap`, this would be the assay colormap specified in `assays` of `all_discrete` or `all_continuous` - before falling back to the global default in `global_discrete` or `global_continuous`. Similarly, if `i` is missing, the default discrete/continuous colormap is returned.

### Setters

In the following code snippets, `x` is an `ExperimentColorMap` object, and `i` is a character or numeric index.

`assayColorMap(x, i, ...) <- value`:

  Set an `assays` colormap.

`colDataColorMap(x, i, ...) <- value`:

  Set a `colData` colormap.

`rowDataColorMap(x, i, ...) <- value`:

  Set a `rowData` colormap.

`assay(x, i, ...) <- value`:

  Alias. Set an `assays` colormap.

### Examples

    # Example colormaps ----

    count_colors <- function(n){
      c("black", "brown", "red", "orange", "yellow")
    }
    fpkm_colors <- viridis::inferno
    tpm_colors <- viridis::plasma

    qc_color_fun <- function(n){
      qc_colors <- c("forestgreen", "firebrick1")
      names(qc_colors) <- c("Y", "N")
      return(qc_colors)
    }

    # Constructor ----

    ecm <- ExperimentColorMap(
        assays=list(
            counts=count_colors,
            tophat_counts=count_colors,
            cufflinks_fpkm=fpkm_colors,
            rsem_tpm=tpm_colors
        ),
        colData=list(
            passes_qc_checks_s=qc_color_fun
        )
    )

    # Accessors ----

    # assay colormaps
    assayColorMap(ecm, "logcounts") # [undefined --> default]
    assayColorMap(ecm, "counts")
    assayColorMap(ecm, "cufflinks_fpkm")
    assay(ecm, "cufflinks_fpkm") # alias

    # colData colormaps
    colDataColorMap(ecm, "passes_qc_checks_s")
    colDataColorMap(ecm, "undefined")

    # rowData colormaps
    rowDataColorMap(ecm, "undefined")

    # generic accessors
    assays(ecm)
    assayNames(ecm)

    # Setters ----

    assayColorMap(ecm, "counts") <- function(n){c("blue", "white", "red")}
    assay(ecm, 1) <- function(n){c("blue", "white", "red")}

    colDataColorMap(ecm, "passes_qc_checks_s") <- function(n){NULL}
    rowDataColorMap(ecm, "undefined") <- function(n){NULL}

    # Categorical colormaps ----

    # Override all discrete colormaps using the base rainbow palette
    ecm <- ExperimentColorMap(global_discrete = rainbow)
    n <- 10
    plot(1:n, col=assayColorMap(ecm, "undefined", discrete = TRUE)(n), pch=20, cex=3)

[Package iSEE version 2.6.0 Index]