plot_heatmap {DEP} | R Documentation |
plot_heatmap
generates a heatmap of all significant proteins.
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, ...)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
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 |
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 |
... |
Additional arguments for Heatmap function as depicted in
|
A heatmap (generated by Heatmap
)
# 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)