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R/iheatmap.R
In iheatmapr: Interactive, Complex Heatmaps
#' iheatmap
#'
#' Make a farily standard interactive heatmap with optional clustering and
#' row and column annotations. For more flexibility and options, see the
#' \code{\link{main_heatmap}} function and other modular functions as described
#' in vignette.
#' @param data matrix of values to be plotted as heatmap
#' @param x x xaxis labels, by default colnames of data
#' @param y y axis labels, by default rownames of data
#' @param cluster_rows "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of rows respectively
#' @param cluster_cols "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of columnsrespectively
#' @param row_clusters vector of pre-determined row cluster assignment
#' @param col_clusters vector of pre-determined column cluster assignment
#' @param row_k number of clusters for rows, needed if cluster_rows is kmeans or
#' optional if hclust
#' @param col_k number of clusters for columns, needed if cluster_rows is kmeans
#' or optional if hclust
#' @param row_clust_dist distance function to use for row clustering if
#' hierarchical clustering
#' @param col_clust_dist distance function to use for column clustering
#' if hierarchical clustering
#' @param name Name for colorbar
#' @param scale scale matrix by rows, cols or none
#' @param scale_method what method to use for scaling, either none, standardize,
#' center, normalize
#' @param colors name of RColorBrewer palette or vector of colors for main
#' heatmap
#' @param row_annotation row annotation data.frame
#' @param col_annotation column annotation data.frame
#' @param col_clusters_colors colors for col clusters annotation heatmap
#' @param row_clusters_colors colors for row clusters annotation heatmap
#' @param row_clusters_name name for row clusters colorbar
#' @param col_clusters_name name for col clusters colorbar
#' @param show_row_clusters_colorbar show the colorbar for row clusters?
#' @param show_col_clusters_colorbar show the colorbar for column clusters?
#' @param col_annotation_colors list of colors for col annotations heatmap
#' @param row_annotation_colors list of colors for row annotations heatmap
#' @param colorbar_grid colorbar grid parameters, should be result from
#' \code{\link{setup_colorbar_grid}}
#' @param layout list of layout attributes to pass to plotly,
#' eg. list(font = list(size = 15))
#' @param row_labels axis labels for y axis
#' @param col_labels axis labels for x axis
#' @param row_title x axis title
#' @param col_title y axis title
#' @param source source name for use with shiny
#' @param ... additional argument to iheatmap
#' @details
#' By default, no scaling is done of rows or columns. This can be changed by
#' specifying the 'scale' argument. There are three options for scaling
#' methods. "standardize" subtracts the mean and divides by standard deviation,
#' "center" just subtracts the mean, and "normalize" divides by the sum of the
#' values. "normalize" should only be used for data that is all positive! If
#' alternative scaling is desired, the scaling should be done prior to calling
#' the iheatmap function.
#' @return \code{\link{Iheatmap-class}} object, which can be printed to generate
#' an interactive graphic
#' @export
#' @author Alicia Schep
#'
#' @seealso \code{\link{iheatmap}}, \code{\link{add_iheatmap}},
#' \code{\link{to_widget}}
#' @include main_heatmap.R
#' @rdname iheatmap
#' @name iheatmap
#' @aliases iheatmap,matrix-method
#' @examples
#' mat <- matrix(rnorm(24), nrow = 6)
#' annotation = data.frame(gender = c(rep("M", 3),rep("F",3)),
#' age = c(20,34,27,19,23,30))
#' hm <- iheatmap(mat,
#' cluster_rows = "hclust",
#' cluster_cols = "kmeans",
#' col_k = 3,
#' row_annotation = annotation)
#'
#' # Print heatmap if interactive session
#' if (interactive()) hm
setMethod("iheatmap", c(data = "matrix"),
function(data,
x = default_x(data),
y = default_y(data),
cluster_rows = c("none","hclust","kmeans"),
cluster_cols = c("none","hclust","kmeans"),
row_clusters = NULL,
col_clusters = NULL,
row_k = NULL,
col_k = NULL,
row_clust_dist = stats::dist,
col_clust_dist = stats::dist,
name = "Signal",
scale = c("none","rows","cols"),
scale_method = c("standardize","center","normalize"),
colors = NULL,
col_clusters_colors = NULL,
col_clusters_name = "Col<br>Clusters",
row_clusters_colors = NULL,
row_clusters_name = "Row<br>Clusters",
show_row_clusters_colorbar = TRUE,
show_col_clusters_colorbar = TRUE,
row_annotation = NULL,
col_annotation = NULL,
row_annotation_colors = NULL,
col_annotation_colors = NULL,
row_labels = NULL,
col_labels = NULL,
row_title = NULL,
col_title = NULL,
colorbar_grid = setup_colorbar_grid(),
layout = list(),
source = "iheatmapr",
...){
cluster_rows <- match.arg(cluster_rows)
cluster_cols <- match.arg(cluster_cols)
scale <- match.arg(scale)
scale_method <- match.arg(scale_method)
if (scale != "none") data <- scale_mat(data,
scale = scale,
scale_method = scale_method)
## Build plot
if (is.null(colors))
colors <- pick_continuous_colors(0,
min(data, na.rm = TRUE),
max(data, na.rm = TRUE))
p <- main_heatmap(data,
x = x,
y = y,
name = name,
colors = colors,
colorbar_grid = colorbar_grid,
layout = layout,
source = source,
...)
if (cluster_rows != "none"){
p <-
add_row_clustering(p,
method = cluster_rows,
name = row_clusters_name,
k = row_k,
groups = row_clusters,
colors = row_clusters_colors,
show_colorbar = show_col_clusters_colorbar)
}
if (cluster_cols != "none"){
p <-
add_col_clustering(p,
method = cluster_cols,
name = col_clusters_name,
k = col_k,
groups = col_clusters,
colors = col_clusters_colors,
show_colorbar = show_col_clusters_colorbar)
}
if (!is.null(row_annotation)){
p <-
add_row_annotation(p,
row_annotation,
colors = row_annotation_colors)
}
if (!is.null(col_annotation)){
p <-
add_col_annotation(p,
col_annotation,
colors = col_annotation_colors,
side = "bottom")
}
if (!is.null(row_labels)){
if (isTRUE(row_labels)){
p <- add_row_labels(p, side = "right")
} else if (row_labels) {
p <- add_row_labels(p, ticktext = row_labels,
side = "right")
}
}
if (!is.null(col_labels)){
if (isTRUE(col_labels)){
p <- add_col_labels(p)
} else if (col_labels) {
p <- add_col_labels(p, ticktext = col_labels)
}
}
if (!is.null(col_title)){
p <- add_col_title(p, col_title)
}
if (!is.null(row_title)){
p <- add_row_title(p, row_title, side = "left")
}
validObject(p)
p
})
#' add_iheatmap
#'
#' @param p iheatmap object
#' @param data matrix of values to be plotted as heatmap
#' @param x x xaxis labels, by default colnames of data
#' @param y y axis labels, by default rownames of data
#' @param cluster_rows "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of rows respectively
#' @param cluster_cols "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of columnsrespectively
#' @param row_clusters vector of pre-determined row cluster assignment
#' @param col_clusters vector of pre-determined column cluster assignment
#' @param row_k number of clusters for rows, needed if cluster_rows is kmeans or
#' optional if hclust
#' @param col_k number of clusters for columns, needed if cluster_rows is kmeans
#' or optional if hclust
#' @param row_clust_dist distance function to use for row clustering if
#' hierarchical clustering
#' @param col_clust_dist distance function to use for column clustering if
#' hierarchical clustering
#' @param name Name for colorbar
#' @param scale scale matrix by rows, cols or none
#' @param scale_method what method to use for scaling, either standardize,
#' center, normalize
#' @param colors name of RColorBrewer palette or vector of colors for main heatmap
#' @param row_annotation row annotation data.frame
#' @param col_annotation column annotation data.frame
#' @param col_clusters_colors colors for col clusters annotation heatmap
#' @param row_clusters_colors colors for row clusters annotation heatmap
#' @param row_clusters_name name for row clusters colorbar
#' @param col_clusters_name name for col clusters colorbar
#' @param show_row_clusters_colorbar show the colorbar for row clusters?
#' @param show_col_clusters_colorbar show the colorbar for column clusters?
#' @param col_annotation_colors list of colors for col annotations heatmap
#' @param row_annotation_colors list of colors for row annotations heatmap
#' @param row_labels axis labels for y axis
#' @param col_labels axis labels for x axis
#' @param row_title x axis title
#' @param col_title y axis title
#' @param buffer amount of space to leave empty before this plot, relative to
#' size of first heatmap
#' @param ... additional argument to add_iheatmap
#' @export
#' @details
#' By default, no scaling is done of rows or columns. This can be changed by
#' specifying the 'scale' argument. There are three options for scaling
#' methods. "standardize" subtracts the mean and divides by standard deviation,
#' "center" just subtracts the mean, and "normalize" divides by the sum of the
#' values. "normalize" should only be used for data that is all positive! If
#' alternative scaling is desired, the scaling should be done prior to calling
#' the iheatmap function.
#' @author Alicia Schep
#' @rdname add_iheatmap
#' @name add_iheatmap
#' @return \code{\link{Iheatmap-class}} object, which can be printed to generate
#' an interactive graphic
#' @aliases add_iheatmap,IheatmapHorizontal,matrix-method
#' add_iheatmap,IheatmapVertical,matrix-method
#' @seealso \code{\link{iheatmap}}, \code{\link{main_heatmap}}
#' @examples
#'
#' mat <- matrix(rnorm(24), nrow = 6)
#' mat2 <- matrix(rnorm(24), nrow = 6)
#' annotation = data.frame(gender = c(rep("M", 3),rep("F",3)),
#' age = c(20,34,27,19,23,30))
#' hm <- iheatmap(mat,
#' cluster_rows = "hclust",
#' cluster_cols = "hclust",
#' col_k = 3) %>%
#' add_iheatmap(mat2,
#' cluster_cols = "hclust",
#' col_k = 3,
#' row_annotation = annotation)
#'
#' # Print heatmap if interactive session
#' if (interactive()) hm
setMethod("add_iheatmap", c(p = "IheatmapHorizontal", data = "matrix"),
function(p,
data,
x = default_x(data),
cluster_cols = c("none","hclust","kmeans","groups"),
col_clusters = NULL,
col_k = NULL,
col_clust_dist = stats::dist,
name = "Signal",
scale = c("none","rows","cols"),
scale_method = c("standardize","center","normalize"),
colors = NULL,
col_clusters_colors = NULL,
col_clusters_name = "Col<br>Clusters",
show_col_clusters_colorbar = TRUE,
row_annotation = NULL,
col_annotation = NULL,
row_annotation_colors = NULL,
col_annotation_colors = NULL,
row_labels = NULL,
col_labels = NULL,
row_title = NULL,
col_title = NULL,
buffer = 0.2,
...){
cluster_cols <- match.arg(cluster_cols)
scale <- match.arg(scale)
scale_method <- match.arg(scale_method)
if (scale != "none") data <- scale_mat(data, scale = scale,
scale_method = scale_method)
## Add plot items
if (is.null(colors))
colors <- pick_continuous_colors(0, min(data, na.rm = TRUE),
max(data, na.rm = TRUE), p)
p <- add_main_heatmap(p,
data,
x = x,
name = name,
buffer = buffer,
colors = colors,
...)
if (cluster_cols != "none"){
p <-
add_col_clustering(p,
method = cluster_cols,
name = col_clusters_name,
k = col_k,
groups = col_clusters,
colors = col_clusters_colors,
show_colorbar = show_col_clusters_colorbar)
}
if (!is.null(row_annotation)){
p <- add_row_annotation(p,
row_annotation,
colors = row_annotation_colors)
}
if (!is.null(col_annotation)){
p <- add_col_annotation(p,
col_annotation,
colors = col_annotation_colors,
side = "bottom")
}
if (!is.null(row_labels)){
if (isTRUE(row_labels)){
p <- add_row_labels(p, side = "right")
} else if (row_labels) {
p <- add_row_labels(p, ticktext = row_labels,
side = "right")
}
}
if (!is.null(col_labels)){
if (isTRUE(col_labels)){
p <- add_col_labels(p)
} else if (col_labels) {
p <- add_col_labels(p, ticktext = col_labels)
}
}
if (!is.null(col_title)){
p <- add_col_title(p, col_title)
}
if (!is.null(row_title)){
p <- add_row_title(p, row_title, side = "left")
}
validObject(p)
p
})
#' @rdname add_iheatmap
setMethod("add_iheatmap", c(p = "IheatmapVertical", data = "matrix"),
function(p,
data,
y = default_y(data),
cluster_rows = c("none","hclust","kmeans","groups"),
row_clusters = NULL,
row_k = NULL,
row_clust_dist = stats::dist,
name = "Signal",
scale = c("none","rows","cols"),
scale_method = c("standardize","center","normalize"),
colors = NULL,
row_clusters_colors = NULL,
row_clusters_name = "Col<br>Clusters",
show_row_clusters_colorbar = TRUE,
row_annotation = NULL,
col_annotation = NULL,
row_annotation_colors = NULL,
col_annotation_colors = NULL,
row_labels = NULL,
col_labels = NULL,
row_title = NULL,
col_title = NULL,
buffer = 0.2,
...){
cluster_rows <- match.arg(cluster_rows)
scale <- match.arg(scale)
scale_method <- match.arg(scale_method)
if (scale != "none") data <- scale_mat(data, scale = scale,
scale_method = scale_method)
## Add plot items
if (is.null(colors))
colors <- pick_continuous_colors(0, min(data, na.rm = TRUE),
max(data, na.rm = TRUE), p)
p <- add_main_heatmap(p,
data,
y = y,
name = name,
buffer = buffer,
colors = colors,
...)
if (cluster_rows != "none"){
p <-
add_row_clustering(p,
method = cluster_rows,
name = row_clusters_name,
k = row_k,
groups = row_clusters,
colors = row_clusters_colors,
show_colorbar = show_row_clusters_colorbar)
}
if (!is.null(row_annotation)){
p <- add_row_annotation(p,
row_annotation,
colors = row_annotation_colors)
}
if (!is.null(col_annotation)){
p <- add_col_annotation(p,
col_annotation,
colors = col_annotation_colors,
side = "bottom")
}
if (!is.null(row_labels)){
if (isTRUE(row_labels)){
p <- add_row_labels(p, side = "right")
} else if (row_labels) {
p <- add_row_labels(p, ticktext = row_labels,
side = "right")
}
}
if (!is.null(col_labels)){
if (isTRUE(col_labels)){
p <- add_col_labels(p)
} else if (col_labels) {
p <- add_col_labels(p, ticktext = col_labels)
}
}
if (!is.null(col_title)){
p <- add_col_title(p, col_title)
}
if (!is.null(row_title)){
p <- add_row_title(p, row_title, side = "left")
}
validObject(p)
p
})
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#' iheatmap
#'
#' Make a farily standard interactive heatmap with optional clustering and
#' row and column annotations. For more flexibility and options, see the
#' \code{\link{main_heatmap}} function and other modular functions as described
#' in vignette.
#' @param data matrix of values to be plotted as heatmap
#' @param x x xaxis labels, by default colnames of data
#' @param y y axis labels, by default rownames of data
#' @param cluster_rows "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of rows respectively
#' @param cluster_cols "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of columnsrespectively
#' @param row_clusters vector of pre-determined row cluster assignment
#' @param col_clusters vector of pre-determined column cluster assignment
#' @param row_k number of clusters for rows, needed if cluster_rows is kmeans or
#' optional if hclust
#' @param col_k number of clusters for columns, needed if cluster_rows is kmeans
#' or optional if hclust
#' @param row_clust_dist distance function to use for row clustering if
#' hierarchical clustering
#' @param col_clust_dist distance function to use for column clustering
#' if hierarchical clustering
#' @param name Name for colorbar
#' @param scale scale matrix by rows, cols or none
#' @param scale_method what method to use for scaling, either none, standardize,
#' center, normalize
#' @param colors name of RColorBrewer palette or vector of colors for main
#' heatmap
#' @param row_annotation row annotation data.frame
#' @param col_annotation column annotation data.frame
#' @param col_clusters_colors colors for col clusters annotation heatmap
#' @param row_clusters_colors colors for row clusters annotation heatmap
#' @param row_clusters_name name for row clusters colorbar
#' @param col_clusters_name name for col clusters colorbar
#' @param show_row_clusters_colorbar show the colorbar for row clusters?
#' @param show_col_clusters_colorbar show the colorbar for column clusters?
#' @param col_annotation_colors list of colors for col annotations heatmap
#' @param row_annotation_colors list of colors for row annotations heatmap
#' @param colorbar_grid colorbar grid parameters, should be result from
#' \code{\link{setup_colorbar_grid}}
#' @param layout list of layout attributes to pass to plotly,
#' eg. list(font = list(size = 15))
#' @param row_labels axis labels for y axis
#' @param col_labels axis labels for x axis
#' @param row_title x axis title
#' @param col_title y axis title
#' @param source source name for use with shiny
#' @param ... additional argument to iheatmap
#' @details
#' By default, no scaling is done of rows or columns. This can be changed by
#' specifying the 'scale' argument. There are three options for scaling
#' methods. "standardize" subtracts the mean and divides by standard deviation,
#' "center" just subtracts the mean, and "normalize" divides by the sum of the
#' values. "normalize" should only be used for data that is all positive! If
#' alternative scaling is desired, the scaling should be done prior to calling
#' the iheatmap function.
#' @return \code{\link{Iheatmap-class}} object, which can be printed to generate
#' an interactive graphic
#' @export
#' @author Alicia Schep
#'
#' @seealso \code{\link{iheatmap}}, \code{\link{add_iheatmap}},
#' \code{\link{to_widget}}
#' @include main_heatmap.R
#' @rdname iheatmap
#' @name iheatmap
#' @aliases iheatmap,matrix-method
#' @examples
#' mat <- matrix(rnorm(24), nrow = 6)
#' annotation = data.frame(gender = c(rep("M", 3),rep("F",3)),
#' age = c(20,34,27,19,23,30))
#' hm <- iheatmap(mat,
#' cluster_rows = "hclust",
#' cluster_cols = "kmeans",
#' col_k = 3,
#' row_annotation = annotation)
#'
#' # Print heatmap if interactive session
#' if (interactive()) hm
setMethod("iheatmap", c(data = "matrix"),
function(data,
x = default_x(data),
y = default_y(data),
cluster_rows = c("none","hclust","kmeans"),
cluster_cols = c("none","hclust","kmeans"),
row_clusters = NULL,
col_clusters = NULL,
row_k = NULL,
col_k = NULL,
row_clust_dist = stats::dist,
col_clust_dist = stats::dist,
name = "Signal",
scale = c("none","rows","cols"),
scale_method = c("standardize","center","normalize"),
colors = NULL,
col_clusters_colors = NULL,
col_clusters_name = "Col<br>Clusters",
row_clusters_colors = NULL,
row_clusters_name = "Row<br>Clusters",
show_row_clusters_colorbar = TRUE,
show_col_clusters_colorbar = TRUE,
row_annotation = NULL,
col_annotation = NULL,
row_annotation_colors = NULL,
col_annotation_colors = NULL,
row_labels = NULL,
col_labels = NULL,
row_title = NULL,
col_title = NULL,
colorbar_grid = setup_colorbar_grid(),
layout = list(),
source = "iheatmapr",
...){
cluster_rows <- match.arg(cluster_rows)
cluster_cols <- match.arg(cluster_cols)
scale <- match.arg(scale)
scale_method <- match.arg(scale_method)
if (scale != "none") data <- scale_mat(data,
scale = scale,
scale_method = scale_method)
## Build plot
if (is.null(colors))
colors <- pick_continuous_colors(0,
min(data, na.rm = TRUE),
max(data, na.rm = TRUE))
p <- main_heatmap(data,
x = x,
y = y,
name = name,
colors = colors,
colorbar_grid = colorbar_grid,
layout = layout,
source = source,
...)
if (cluster_rows != "none"){
p <-
add_row_clustering(p,
method = cluster_rows,
name = row_clusters_name,
k = row_k,
groups = row_clusters,
colors = row_clusters_colors,
show_colorbar = show_col_clusters_colorbar)
}
if (cluster_cols != "none"){
p <-
add_col_clustering(p,
method = cluster_cols,
name = col_clusters_name,
k = col_k,
groups = col_clusters,
colors = col_clusters_colors,
show_colorbar = show_col_clusters_colorbar)
}
if (!is.null(row_annotation)){
p <-
add_row_annotation(p,
row_annotation,
colors = row_annotation_colors)
}
if (!is.null(col_annotation)){
p <-
add_col_annotation(p,
col_annotation,
colors = col_annotation_colors,
side = "bottom")
}
if (!is.null(row_labels)){
if (isTRUE(row_labels)){
p <- add_row_labels(p, side = "right")
} else if (row_labels) {
p <- add_row_labels(p, ticktext = row_labels,
side = "right")
}
}
if (!is.null(col_labels)){
if (isTRUE(col_labels)){
p <- add_col_labels(p)
} else if (col_labels) {
p <- add_col_labels(p, ticktext = col_labels)
}
}
if (!is.null(col_title)){
p <- add_col_title(p, col_title)
}
if (!is.null(row_title)){
p <- add_row_title(p, row_title, side = "left")
}
validObject(p)
p
})
#' add_iheatmap
#'
#' @param p iheatmap object
#' @param data matrix of values to be plotted as heatmap
#' @param x x xaxis labels, by default colnames of data
#' @param y y axis labels, by default rownames of data
#' @param cluster_rows "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of rows respectively
#' @param cluster_cols "none","hclust", or "k-means" for no clustering,
#' hierarchical clustering, and k-means clustering of columnsrespectively
#' @param row_clusters vector of pre-determined row cluster assignment
#' @param col_clusters vector of pre-determined column cluster assignment
#' @param row_k number of clusters for rows, needed if cluster_rows is kmeans or
#' optional if hclust
#' @param col_k number of clusters for columns, needed if cluster_rows is kmeans
#' or optional if hclust
#' @param row_clust_dist distance function to use for row clustering if
#' hierarchical clustering
#' @param col_clust_dist distance function to use for column clustering if
#' hierarchical clustering
#' @param name Name for colorbar
#' @param scale scale matrix by rows, cols or none
#' @param scale_method what method to use for scaling, either standardize,
#' center, normalize
#' @param colors name of RColorBrewer palette or vector of colors for main heatmap
#' @param row_annotation row annotation data.frame
#' @param col_annotation column annotation data.frame
#' @param col_clusters_colors colors for col clusters annotation heatmap
#' @param row_clusters_colors colors for row clusters annotation heatmap
#' @param row_clusters_name name for row clusters colorbar
#' @param col_clusters_name name for col clusters colorbar
#' @param show_row_clusters_colorbar show the colorbar for row clusters?
#' @param show_col_clusters_colorbar show the colorbar for column clusters?
#' @param col_annotation_colors list of colors for col annotations heatmap
#' @param row_annotation_colors list of colors for row annotations heatmap
#' @param row_labels axis labels for y axis
#' @param col_labels axis labels for x axis
#' @param row_title x axis title
#' @param col_title y axis title
#' @param buffer amount of space to leave empty before this plot, relative to
#' size of first heatmap
#' @param ... additional argument to add_iheatmap
#' @export
#' @details
#' By default, no scaling is done of rows or columns. This can be changed by
#' specifying the 'scale' argument. There are three options for scaling
#' methods. "standardize" subtracts the mean and divides by standard deviation,
#' "center" just subtracts the mean, and "normalize" divides by the sum of the
#' values. "normalize" should only be used for data that is all positive! If
#' alternative scaling is desired, the scaling should be done prior to calling
#' the iheatmap function.
#' @author Alicia Schep
#' @rdname add_iheatmap
#' @name add_iheatmap
#' @return \code{\link{Iheatmap-class}} object, which can be printed to generate
#' an interactive graphic
#' @aliases add_iheatmap,IheatmapHorizontal,matrix-method
#' add_iheatmap,IheatmapVertical,matrix-method
#' @seealso \code{\link{iheatmap}}, \code{\link{main_heatmap}}
#' @examples
#'
#' mat <- matrix(rnorm(24), nrow = 6)
#' mat2 <- matrix(rnorm(24), nrow = 6)
#' annotation = data.frame(gender = c(rep("M", 3),rep("F",3)),
#' age = c(20,34,27,19,23,30))
#' hm <- iheatmap(mat,
#' cluster_rows = "hclust",
#' cluster_cols = "hclust",
#' col_k = 3) %>%
#' add_iheatmap(mat2,
#' cluster_cols = "hclust",
#' col_k = 3,
#' row_annotation = annotation)
#'
#' # Print heatmap if interactive session
#' if (interactive()) hm
setMethod("add_iheatmap", c(p = "IheatmapHorizontal", data = "matrix"),
function(p,
data,
x = default_x(data),
cluster_cols = c("none","hclust","kmeans","groups"),
col_clusters = NULL,
col_k = NULL,
col_clust_dist = stats::dist,
name = "Signal",
scale = c("none","rows","cols"),
scale_method = c("standardize","center","normalize"),
colors = NULL,
col_clusters_colors = NULL,
col_clusters_name = "Col<br>Clusters",
show_col_clusters_colorbar = TRUE,
row_annotation = NULL,
col_annotation = NULL,
row_annotation_colors = NULL,
col_annotation_colors = NULL,
row_labels = NULL,
col_labels = NULL,
row_title = NULL,
col_title = NULL,
buffer = 0.2,
...){
cluster_cols <- match.arg(cluster_cols)
scale <- match.arg(scale)
scale_method <- match.arg(scale_method)
if (scale != "none") data <- scale_mat(data, scale = scale,
scale_method = scale_method)
## Add plot items
if (is.null(colors))
colors <- pick_continuous_colors(0, min(data, na.rm = TRUE),
max(data, na.rm = TRUE), p)
p <- add_main_heatmap(p,
data,
x = x,
name = name,
buffer = buffer,
colors = colors,
...)
if (cluster_cols != "none"){
p <-
add_col_clustering(p,
method = cluster_cols,
name = col_clusters_name,
k = col_k,
groups = col_clusters,
colors = col_clusters_colors,
show_colorbar = show_col_clusters_colorbar)
}
if (!is.null(row_annotation)){
p <- add_row_annotation(p,
row_annotation,
colors = row_annotation_colors)
}
if (!is.null(col_annotation)){
p <- add_col_annotation(p,
col_annotation,
colors = col_annotation_colors,
side = "bottom")
}
if (!is.null(row_labels)){
if (isTRUE(row_labels)){
p <- add_row_labels(p, side = "right")
} else if (row_labels) {
p <- add_row_labels(p, ticktext = row_labels,
side = "right")
}
}
if (!is.null(col_labels)){
if (isTRUE(col_labels)){
p <- add_col_labels(p)
} else if (col_labels) {
p <- add_col_labels(p, ticktext = col_labels)
}
}
if (!is.null(col_title)){
p <- add_col_title(p, col_title)
}
if (!is.null(row_title)){
p <- add_row_title(p, row_title, side = "left")
}
validObject(p)
p
})
#' @rdname add_iheatmap
setMethod("add_iheatmap", c(p = "IheatmapVertical", data = "matrix"),
function(p,
data,
y = default_y(data),
cluster_rows = c("none","hclust","kmeans","groups"),
row_clusters = NULL,
row_k = NULL,
row_clust_dist = stats::dist,
name = "Signal",
scale = c("none","rows","cols"),
scale_method = c("standardize","center","normalize"),
colors = NULL,
row_clusters_colors = NULL,
row_clusters_name = "Col<br>Clusters",
show_row_clusters_colorbar = TRUE,
row_annotation = NULL,
col_annotation = NULL,
row_annotation_colors = NULL,
col_annotation_colors = NULL,
row_labels = NULL,
col_labels = NULL,
row_title = NULL,
col_title = NULL,
buffer = 0.2,
...){
cluster_rows <- match.arg(cluster_rows)
scale <- match.arg(scale)
scale_method <- match.arg(scale_method)
if (scale != "none") data <- scale_mat(data, scale = scale,
scale_method = scale_method)
## Add plot items
if (is.null(colors))
colors <- pick_continuous_colors(0, min(data, na.rm = TRUE),
max(data, na.rm = TRUE), p)
p <- add_main_heatmap(p,
data,
y = y,
name = name,
buffer = buffer,
colors = colors,
...)
if (cluster_rows != "none"){
p <-
add_row_clustering(p,
method = cluster_rows,
name = row_clusters_name,
k = row_k,
groups = row_clusters,
colors = row_clusters_colors,
show_colorbar = show_row_clusters_colorbar)
}
if (!is.null(row_annotation)){
p <- add_row_annotation(p,
row_annotation,
colors = row_annotation_colors)
}
if (!is.null(col_annotation)){
p <- add_col_annotation(p,
col_annotation,
colors = col_annotation_colors,
side = "bottom")
}
if (!is.null(row_labels)){
if (isTRUE(row_labels)){
p <- add_row_labels(p, side = "right")
} else if (row_labels) {
p <- add_row_labels(p, ticktext = row_labels,
side = "right")
}
}
if (!is.null(col_labels)){
if (isTRUE(col_labels)){
p <- add_col_labels(p)
} else if (col_labels) {
p <- add_col_labels(p, ticktext = col_labels)
}
}
if (!is.null(col_title)){
p <- add_col_title(p, col_title)
}
if (!is.null(row_title)){
p <- add_row_title(p, row_title, side = "left")
}
validObject(p)
p
})
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