plot_imputation: Visualize imputation

In arnesmits/DEP: Differential Enrichment analysis of Proteomics data

Description

plot_imputation generates density plots of all conditions for input objects, e.g. before and after imputation.

Usage

plot_imputation(se, ...)

Arguments

se	SummarizedExperiment, Data object, e.g. before imputation (output from normalize_vsn()).
...	Other SummarizedExperiment object(s), E.g. data object after imputation (output from impute()).

Value

Density plots of all conditions of all conditions for input objects, e.g. before and after imputation (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)

# Plot imputation
plot_imputation(filt, norm, imputed)

arnesmits/DEP documentation built on Aug. 7, 2019, 10:44 a.m.