Generate a heatmap visualization of a features x samples matrix of measurements.
plot_heatmap( tomic, feature_var = NULL, sample_var = NULL, value_var = NULL, cluster_dim = "both", distance_measure = "dist", hclust_method = "ward.D2", change_threshold = Inf, plot_type = "grob", max_display_features = 800 )
variable from "features" to use as a unique feature label.
variable from "samples" to use as a unique sample label.
which variable in "measurements" to use for quantification.
rows, columns, or both
variable to use for computing dis-similarity
method from stats::hclust to use for clustering
values with a more extreme absolute change will be thresholded to this value.
plotly (for interactivity) or grob (for a static ggplot)
aggregate and downsample distinct feature to this number to speed to up heatmap rendering.
a ggplot2 grob
library(dplyr) tomic <- brauer_2008_triple %>% filter_tomic( filter_type = "category", filter_table = "features", filter_variable = "BP", filter_value = c( "protein biosynthesis", "rRNA processing", "response to stress" ) ) plot_heatmap( tomic = tomic, value_var = "expression", change_threshold = 5, cluster_dim = "rows", plot_type = "grob", distance_measure = "corr" )