plot_pca: Plot PCA

In squirrelandr/DEP: Differential Enrichment analysis of Proteomics data

Description

plot_pca generates a PCA plot using the top variable proteins.

Usage

plot_pca(dep, x = 1, y = 2, indicate = c("condition", "replicate"),
  label = FALSE, n = 500, point_size = 4, label_size = 3, plot = TRUE)

Arguments

dep	SummarizedExperiment, Data object for which differentially enriched proteins are annotated (output from test_diff() and add_rejections()).
x	Integer(1), Sets the principle component to plot on the x-axis.
y	Integer(1), Sets the principle component to plot on the y-axis.
indicate	Character, Sets the color, shape and facet_wrap of the plot based on columns from the experimental design (colData).
label	Logical, Whether or not to add sample labels.
n	Integer(1), Sets the number of top variable proteins to consider.
point_size	Integer(1), Sets the size of the points.
label_size	Integer(1), Sets the size of the labels.
plot	Logical(1), If TRUE (default) the PCA plot is produced. Otherwise (if FALSE), the data which the PCA plot is based on are returned.

Value

A scatter plot (generated by ggplot).

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 PCA
plot_pca(dep)
plot_pca(dep, indicate = "condition")

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