Visualize normalization

plot_normalization generates boxplots of all conditions for input objects, e.g. before and after normalization.

Usage

plot_normalization(se, ...)

Arguments

se

SummarizedExperiment, Data object, e.g. before normalization (output from make_se() or make_se_parse()).

...

Additional SummarizedExperiment object(s), E.g. data object after normalization (output from normalize_vsn).

Value

Boxplots of all conditions for input objects, e.g. before and after normalization (generated by ggplot). Adding components and other plot adjustments can be easily done using the ggplot2 syntax (i.e. using '+')

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.

# Plot normalization
plot_normalization(se, filt, norm)