Normalization using vsn — normalize

normalize_vsn performs variance stabilizing transformation using the vsn-package.

Usage

normalize_vsn(se)

Arguments

se: SummarizedExperiment, Proteomics data (output from make_se() or make_se_parse()). It is adviced to first remove proteins with too many missing values using filter_se().

Value

A normalized SummarizedExperiment object.

Examples

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

# Make SummarizedExperiment
ecols <- grep("LFQ.", colnames(data_unique))

# Load experiement design
data(Silicosis_ExpDesign)
exp_design <- Silicosis_ExpDesign
se <- make_se(data_unique, ecols, exp_design)
se <- make_se_parse(data_unique, ecols, mode = "delim", sep = "_")

# Filter and normalize
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.