plot_heatmap: Plot a heatmap

In DEP: Differential Enrichment analysis of Proteomics data

Description

plot_heatmap generates a heatmap of all significant proteins.

Usage

plot_heatmap(dep, type = c("contrast", "centered"), kmeans = FALSE,
  k = 6, col_limit = 6, indicate = NULL,
  clustering_distance = c("euclidean", "maximum", "manhattan",
  "canberra", "binary", "minkowski", "pearson", "spearman", "kendall",
  "gower"), row_font_size = 6, col_font_size = 10, plot = TRUE, ...)

Arguments

dep	SummarizedExperiment, Data object for which differentially enriched proteins are annotated (output from test_diff() and add_rejections()).
type	'contrast' or 'centered', The type of data scaling used for plotting. Either the fold change ('contrast') or the centered log2-intensity ('centered').
kmeans	Logical(1), Whether or not to perform k-means clustering.
k	Integer(1), Sets the number of k-means clusters.
col_limit	Integer(1), Sets the outer limits of the color scale.
indicate	Character, Sets additional annotation on the top of the heatmap based on columns from the experimental design (colData). Only applicable to type = 'centered'.
clustering_distance	"euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall" or "gower", Function used to calculate clustering distance (for proteins and samples). Based on Heatmap and daisy.
row_font_size	Integer(1), Sets the size of row labels.
col_font_size	Integer(1), Sets the size of column labels.
plot	Logical(1), If TRUE (default) the heatmap is produced. Otherwise (if FALSE), the data which the heatmap is based on are returned.
...	Additional arguments for Heatmap function as depicted in Heatmap

Value

A heatmap (generated by Heatmap)

Examples

# Load example
data <- UbiLength
data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",]
data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

# Make SummarizedExperiment
columns <- grep("LFQ.", colnames(data_unique))
exp_design <- UbiLength_ExpDesign
se <- make_se(data_unique, columns, exp_design)

# Filter, normalize and impute missing values
filt <- filter_missval(se, thr = 0)
norm <- normalize_vsn(filt)
imputed <- impute(norm, fun = "MinProb", q = 0.01)

# Test for differentially expressed proteins
diff <- test_diff(imputed, "control", "Ctrl")
dep <- add_rejections(diff, alpha = 0.05, lfc = 1)

# Plot heatmap
plot_heatmap(dep)
plot_heatmap(dep, 'centered', kmeans = TRUE, k = 6, row_font_size = 3)
plot_heatmap(dep, 'contrast', col_limit = 10, row_font_size = 3)

DEP documentation built on Nov. 8, 2020, 7:49 p.m.