Visualize imputation

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

# Construct SE
ecols <- grep("LFQ.", colnames(data_unique))
se <- make_se_parse(data_unique, ecols,mode = "delim")

# Filter and normalization
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.

# imputation
imputed <- impute(norm, fun = "MinProb", q = 0.01)
#> Imputing along margin 2 (samples/columns).
#> [1] 0.3026531

# Plot imputation
plot_imputation(filt, norm, imputed)