Usage
## S3 method for class 'splsda1fact':
pheatmap.multilevel(result, cluster = NULL,
color=colorRampPalette(rev(c("#D73027", "#FC8D59", "#FEE090", "#FFFFBF",
"#E0F3F8", "#91BFDB", "#4575B4")))(100),
col_sample=NULL, col_stimulation=NULL,label_annotation=NULL,
breaks = NA, border_color = "grey60", cellwidth = NA, cellheight = NA,
scale = "none", cluster_rows = TRUE, cluster_cols = TRUE,
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
clustering_method = "complete", treeheight_row = ifelse(cluster_rows, 50, 0),
treeheight_col = ifelse(cluster_cols, 50, 0),
legend = TRUE, annotation = NA, annotation_colors = NA, annotation_legend = TRUE,
show_rownames = TRUE, show_colnames = TRUE, fontsize = 10, fontsize_row = fontsize,
fontsize_col = fontsize, filename = NA, width = NA, height = NA,order_sample=NULL,
...)
## S3 method for class 'splsda2fact':
pheatmap.multilevel(result, cluster = NULL,
color=colorRampPalette(rev(c("#D73027", "#FC8D59", "#FEE090", "#FFFFBF",
"#E0F3F8", "#91BFDB", "#4575B4")))(100),
col_sample=NULL, col_stimulation=NULL, col_time=NULL,
label_color_stimulation=NULL,label_color_time=NULL,
label_annotation=NULL,
breaks = NA, border_color = "grey60", cellwidth = NA, cellheight = NA,
scale = "none", cluster_rows = TRUE, cluster_cols = TRUE,
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean",
clustering_method = "complete", treeheight_row = ifelse(cluster_rows, 50, 0),
treeheight_col = ifelse(cluster_cols, 50, 0),
legend = TRUE, annotation = NA, annotation_colors = NA, annotation_legend = TRUE,
show_rownames = TRUE, show_colnames = TRUE, fontsize = 10, fontsize_row = fontsize,
fontsize_col = fontsize, filename = NA, width = NA, height = NA,order_sample=NULL,
...)Arguments
result
a mode result from function multilevel
cluster
by default set to NULL
col_sample
vector of colors indicating the color of each individual
col_stimulation
vector of colors indicating the color of each condition
col_time
if two-factor analysis, vector of colors indicating the color of the factors of the second condition
label_color_stimulation
character vector indicating the label of the first factor, see details
label_color_time
character vector indicating the label of the second factor, see details
label_annotation
set to NULL by default.
order_sample
vector indicatin the reordering of the samples, set to NULL by default.
color
vector of colors used in heatmap.
breaks
a sequence of numbers that covers the range of values in mat and is one element longer than color vector. Used for mapping values to colors. Useful, if needed to map certain values to certain colors, to certain values. If value is NA then the breaks are c
border_color
color of cell borders on heatmap, use NA if no border should be
drawn.
cellwidth
individual cell width in points. If left as NA, then the values depend on the size of plotting window.
cellheight
individual cell height in points. If left as NA, then the values depend on the size of plotting window.
scale
character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. Corresponding values are "row", "column" and "none"
cluster_rows
boolean values determining if rows should be clustered,
cluster_cols
boolean values determining if columns should be clustered.
clustering_distance_rows
distance measure used in clustering rows. Possible values are "correlation" for Pearson correlation and all the distances supported by dist, such as "euclidean", etc. If the value is clustering_distance_cols
distance measure used in clustering columns. Possible values the same as for clustering_distance_rows.
clustering_method
clustering method used. Accepts the same values as hclust. treeheight_row
the height of a tree for rows, if these are clustered.
Default value 50 points.
treeheight_col
the height of a tree for columns, if these are clustered.
Default value 50 points.
legend
boolean value that determines if legend should be drawn or not.
annotation
data frame that specifies the annotations shown on top of the columns. Each row defines the features for a specific column. The columns in the data and rows in the annotation are matched using corresponding row and column names. Note that color schemes ta
annotation_colors
list for specifying annotation track colors manually. It is possible to define the colors for only some of the features. Check examples for details.
annotation_legend
boolean value showing if the legend for annotation tracks
should be drawn.
show_rownames
boolean specifying if column names are be shown.
show_colnames
boolean specifying if column names are be shown.
fontsize
base fontsize for the plot
fontsize_row
fontsize for rownames (Default: fontsize)
fontsize_col
fontsize for colnames (Default: fontsize)
filename
file path where to save the picture. Filetype is decided by
the extension in the path. Currently following formats are supported: png, pdf, tiff,
bmp, jpeg. Even if the plot does not fit into the plotting window, the file size is
calculated so that the
width
manual option for determining the output file width in
height
manual option for determining the output file height in inches.
...
graphical parameters for the text used in plot. Parameters passed to grid.text, see gpar.