plot_cor: Plot correlation matrix

In squirrelandr/DEP: Differential Enrichment analysis of Proteomics data

Description

plot_cor generates a Pearson correlation matrix.

Usage

plot_cor(dep, significant = TRUE, lower = -1, upper = 1, pal = "PRGn",
  pal_rev = FALSE, indicate = NULL, font_size = 12, plot = TRUE, ...)

Arguments

dep	SummarizedExperiment, Data object for which differentially enriched proteins are annotated (output from test_diff() and add_rejections()).
significant	Logical(1), Whether or not to filter for significant proteins.
lower	Integer(1), Sets the lower limit of the color scale.
upper	Integer(1), Sets the upper limit of the color scale.
pal	Character(1), Sets the color panel (from RColorBrewer).
pal_rev	Logical(1), Whether or not to invert the color palette.
indicate	Character, Sets additional annotation on the top of the heatmap based on columns from the experimental design (colData).
font_size	Integer(1), Sets the size of the labels.
plot	Logical(1), If TRUE (default) the correlation matrix plot is produced. Otherwise (if FALSE), the data which the correlation matrix plot is based on are returned.
...	Additional arguments for Heatmap function as depicted in Heatmap

Value

A heatmap plot (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 correlation matrix
plot_cor(dep)

squirrelandr/DEP documentation built on May 7, 2019, 9:31 a.m.