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Normalisation

Source: R/funcs_normalize.R
normalization_methods.Rd

Provides several methods to normalize quantitative data from a SummarizedExperiment 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

Category of normalization method to show. Either "all", "withTracking" or "withoutTracking".

qData

A data.frame with quantitative data to normalize.

conds

A character() vector which is the names of conditions for each sample in the dataset.

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

A float between 0 and 1 indicating the span of loess smoothing window.

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(subR25pept)
qData <- SummarizedExperiment::assay(subR25pept[[1]])
conds <- design.qf(subR25pept)$Condition



#normalized <- GlobalQuantileAlignment(qData)

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

normalized <- QuantileCentering(
SummarizedExperiment::assay(subR25pept), 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")