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Normalisation

Source: R/funcs_normalize.R
normalization_methods.Rd

Provides several methods to normalize quantitative data from a MSnSet object. They are organized in six main families : GlobalQuantileAlignement, sumByColumns, QuantileCentering, MeanCentering, LOESS, vsn For the first family, there is no type. For the five other families, two type categories are available : "Overall" which means that the value for each protein (ie line in the expression data tab) is computed over all the samples ; "within conditions" which means that the value for each protein (ie line in the SummarizedExperiment::assay() data tab) is computed condition by condition.

Usage

normalizeMethods(target = "all")

GlobalQuantileAlignment(qData)

SumByColumns(qData, conds = NULL, type = NULL, subset.norm = NULL)

QuantileCentering(
  qData,
  conds = NULL,
  type = "overall",
  subset.norm = NULL,
  quantile = 0.15
)

MeanCentering(
  qData,
  conds,
  type = "overall",
  subset.norm = NULL,
  scaling = FALSE
)

vsn(qData, conds, type = NULL)

LOESS(qData, conds, type = "overall", span = 0.7)

Arguments

target

xxx

qData

xxx

conds

xxx

type

"overall" (shift all the sample distributions at once) or "within conditions" (shift the sample distributions within each condition at a time).

subset.norm

A vector of index indicating rows to be used for normalization

quantile

A float that corresponds to the quantile used to align the data.

scaling

A boolean that indicates if the variance of the data have to be forced to unit (variance reduction) or not.

span

xxx

Value

xxx

A normalized numeric matrix

A normalized numeric matrix

A normalized numeric matrix

A normalized numeric matrix

A normalized numeric matrix

A normalized numeric matrix

Author

Samuel Wieczorek, Thomas Burger, Helene Borges, Anais Courtier, Enora Fremy

Examples


## Get the list of methods
normalizeMethods()
#> [1] "GlobalQuantileAlignment" "SumByColumns"           
#> [3] "QuantileCentering"       "MeanCentering"          
#> [5] "LOESS"                   "vsn"                    


data(Exp1_R25_pept, package="DaparToolshedData")
obj <- Exp1_R25_pept
qData <- SummarizedExperiment::assay(obj[[1]])
conds <- design.qf(obj)$Condition



#normalized <- GlobalQuantileAlignment(qData)

normalized <- SumByColumns(qData, conds,
    type = "within conditions",
    subset.norm = seq_len(10)
)

normalized <- QuantileCentering(SummarizedExperiment::assay(obj[[1]]), conds,
type = "within conditions", subset.norm = seq_len(10)
)

normalized <- MeanCentering(qData, conds, type = "overall")

# normalized <- vsn(qData, conds, type = "overall")

normalized <- LOESS(qData, conds, type = "overall")