Plot correlation matrix — plot

plot_cor generates a Pearson correlation matrix.

Usage

plot_cor(
  x,
  significant = TRUE,
  lower = -1,
  upper = 1,
  pal = "PRGn",
  pal_rev = FALSE,
  indicate = NULL,
  font_size = 12,
  plot = TRUE,
  add_values = FALSE,
  value_size = 10,
  digits = 2,
  ...
)

Arguments

x: 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.
add_values: Logical(1). Display the distance value on heatmap.
value_size: Numeric(1). Font size of distance.
digits: Integer(1), indicating the number of decimal places of distance.
...: Additional arguments for Heatmap function as depicted in Heatmap

Value

A heatmap plot (generated by Heatmap)

Examples

# Load example
data(Silicosis_pg)
data <- Silicosis_pg
data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

# Make SummarizedExperiment
ecols <- grep("LFQ.", colnames(data_unique))


## Load experiement design
data(Silicosis_ExpDesign)
exp_design <- Silicosis_ExpDesign
se <- make_se(data_unique, ecols, exp_design)

# Filter and normalize
filt <- filter_se(se, thr = 0, fraction = 0.4, filter_formula = ~ Reverse != "+" & Potential.contaminant!="+")
#> filter base on missing number is <= 0 in at least one condition.
#> filter base on missing number fraction < 0.4 in each row
#> filter base on giving formula 

norm <- normalize_vsn(filt)
#> vsn2: 8762 x 20 matrix (1 stratum). 
#> Please use 'meanSdPlot' to verify the fit.

# Impute missing values using different functions
imputed <- impute(norm, fun = "MinProb", q = 0.05)
#> Imputing along margin 2 (samples/columns).
#> [1] 0.3026531

# Test for differentially expressed proteins
diff <- test_diff(imputed, "control", "PBS")
#> Tested contrasts: W10_vs_PBS, W2_vs_PBS, W4_vs_PBS, W6_vs_PBS, W9_vs_PBS
#> Strimmer's qvalue(t)
dep <- add_rejections(diff, alpha = 0.05, lfc = 1)

# Plot correlation matrix
plot_cor(dep)