EigenMS Normalization

EigenMS fits an analysis of variance model to estimate the effects of the experimental factors on the data using the knowledge about the experimental design, and then applies singular value decomposition to identify systematic trends contributing to significant variation not explained by the experimental factors Log2-scaled data should be used as input (on_raw = FALSE).

Usage

eigenMSNorm(se, ain = "log2", aout = "EigenMS", on_raw = FALSE)

Arguments

se

SummarizedExperiment containing all necessary information of the proteomic dataset

ain

String which assay should be used as input

aout

String which assay should be used to save normalized data

on_raw

Boolean specifying whether normalization should be performed on raw or log2-scaled data

Value

SummarizedExperiment containing the EigenMS normalized data as assay (on log2 scale)

Examples

data(tuberculosis_TMT_se)
tuberculosis_TMT_se <- eigenMSNorm(tuberculosis_TMT_se, ain = "log2",
                                    aout = "EigenMS", on_raw = FALSE)
#> [1] "Data dimentions: "
#> [1] 301  20
#> [1] "Treatmenet groups:"
#>  [1] ref HC  PTB HC  PTB HC  PTB TBL TBL TBL ref Rx  HC  Rx  HC  Rx  HC  PTB PTB
#> [20] PTB
#> Levels: ref HC PTB TBL Rx
#> [1] "Selecting complete peptides"
#> [1] "Got 2+ treatment grps"
#> [1] "Computing SVD, estimating Eigentrends..."
#> [1] "Number of treatments:  5"
#> [1] "Number of complete peptides (and samples) used in SVD"
#> [1]  5 20
#> [1] "Number of treatment groups (in svd.id):  5"
#> [1] "Starting Bootstrap....."
#> [1] "Iteration  50"
#> [1] "Iteration  100"
#> [1] "Iteration  150"
#> [1] "Iteration  200"
#> [1] "Iteration  250"
#> [1] "Iteration  300"
#> [1] "Iteration  350"
#> [1] "Iteration  400"
#> [1] "Iteration  450"
#> [1] "Iteration  500"
#> [1] "Number of significant eigenpeptides/trends 1"
#> [1] "Preparing to plot..."
#> [1] "Unique number of treatment combinations: 5"
#> [1] "Normalizing..."
#> [1] "Processing peptide  100"
#> [1] "Done with normalization!!!"