| plot_pca {DEP} | R Documentation |
plot_pca generates a PCA plot using the top variable proteins.
plot_pca(dep, x = 1, y = 2, indicate = c("condition", "replicate"),
label = FALSE, n = 500, point_size = 4, label_size = 3,
plot = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
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 |
A scatter plot (generated by ggplot).
# 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")