ExperimentColorMap-class: ExperimentColorMap class

In csoneson/iSEE: Interactive SummarizedExperiment Explorer

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)

csoneson/iSEE documentation built on Oct. 12, 2024, 5:41 p.m.