Decorate LFQData with Methods for transforming Intensities

Source: R/LFQDataTransformer.R

Decorate LFQData with Methods for transforming Intensities

Decorate LFQData with Methods for transforming Intensities

Public fields

lfq

LFQData object

Methods

Public methods

Method new()

initialize

Usage

LFQDataTransformer$new(lfqdata)

Arguments

lfqdata

LFQData object to transform

Method log2()

log2 transform data

Usage

LFQDataTransformer$log2(force = FALSE)

Arguments

force

if FALSE, then data already log2 transformed will not be transformed a second time. TRUE force log transformation.

Returns

LFQDataTransformer

Method get_scales()

get mean and variance and standard deviation in each sample

Usage

LFQDataTransformer$get_scales()

Returns

list with means and mads

Method robscale()

robust scale data

Usage

LFQDataTransformer$robscale(
  preserveMean = TRUE,
  colname = "transformedIntensity"
)

Arguments

preserveMean

should original mean value be preserved TRUE, if FALSE then center at zero

colname

new name of transformed column

Returns

LFQDataTransformer (self)

Method robscale_subset()

log2 transform and robust scale data based on subset

Usage

LFQDataTransformer$robscale_subset(
  lfqsubset,
  preserveMean = TRUE,
  colname = "transformedIntensity"
)

Arguments

lfqsubset

LFQData subset to use for normalization

preserveMean

should original mean value be preserved TRUE, if FALSE then center at zero

colname

- how to name the transformed intensities, default transformedIntensity

Returns

LFQDataTransformer (self)

Method intensity_array()

Transforms intensities

Usage

LFQDataTransformer$intensity_array(.func = log2, force = FALSE)

Arguments

.func

transformation function working with arrays e.g. log2, log10, asinh etc.

force

transformation on already transformed data.

Returns

LFQDataTransformer (self)

Method intensity_matrix()

pass a function which works with matrices, e.g., vsn::justvsn

Usage

LFQDataTransformer$intensity_matrix(.func = robust_scale, force = FALSE)

Arguments

.func

any function taking a matrix and returning a matrix (columns sample, rows feature e.g. base::scale) default robust_scale

force

transformation on data already transformed

Returns

LFQDataTransformer (self)

Method clone()

The objects of this class are cloneable with this method.

Usage

LFQDataTransformer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples


istar <- prolfqua_data('data_ionstar')$filtered()
#> Column added : nr_peptide_Id_IN_protein_Id
istar$config <- old2new(istar$config)
data <- istar$data |> dplyr::filter(protein_Id %in% sample(protein_Id, 100))
lfqdata <- LFQData$new(data, istar$config)

lfqcopy <- lfqdata$get_copy()
lfqTrans <- lfqcopy$get_Transformer()

x <- lfqTrans$intensity_array(log2)
#> Column added : log2_peptide.intensity
x$lfq$config$table$is_response_transformed
#> [1] TRUE
x <- x$intensity_matrix(robust_scale)
#> Warning: data already transformed. If you still want to log2 tranform, set force = TRUE
plotter <- x$lfq$get_Plotter()
plotter$intensity_distribution_density()


# transform by asinh root and scale

lfqcopy <- lfqdata$get_copy()
lfqTrans <- lfqcopy$get_Transformer()
x <- lfqTrans$intensity_array(asinh)
#> Column added : asinh_peptide.intensity
mads1 <- mean(x$get_scales()$mads)
x <- lfqTrans$intensity_matrix(robust_scale, force = TRUE)
#> Warning: Expected 2 pieces. Additional pieces discarded in 15520 rows [1, 2, 3, 4, 5, 6,
#> 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
#> Joining with `by = join_by(protein_Id, sampleName, peptide_Id)`
mads2 <- mean(x$get_scales()$mads)

stopifnot(abs(mads1 - mads2) < 1e-8)


stopifnot(abs(mean(x$get_scales()$medians)) < 1e-8)
lfqcopy <- lfqdata$get_copy()
lfqTrans <- lfqcopy$get_Transformer()
lfqTrans$log2()
#> Column added : log2_peptide.intensity
before <- lfqTrans$get_scales()
lfqTrans$robscale()
#> data is : TRUE
#> Warning: Expected 2 pieces. Additional pieces discarded in 15520 rows [1, 2, 3, 4, 5, 6,
#> 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
#> Joining with `by = join_by(protein_Id, sampleName, peptide_Id)`
after <- lfqTrans$get_scales()
stopifnot(abs(mean(before$medians) - mean(after$medians)) < 1e-8)
stopifnot(abs(mean(before$mads) - mean(after$mads)) < 1e-8)

# normalize data using vsn
lfqcopy <- lfqdata$get_copy()
lfqTrans <- lfqcopy$get_Transformer()
lfqTransCheck <- lfqcopy$get_Transformer()

lfqTransCheck$log2()
#> Column added : log2_peptide.intensity
lfqTransCheck$get_scales()
#> $medians
#>     a~10     a~11     a~20     a~21     b~02     b~09     b~12     b~19 
#> 24.72115 25.15540 25.16233 25.17072 24.83373 24.76536 25.13049 25.13891 
#>     c~03     c~08     c~13     c~18     d~04     d~07     d~14     d~17 
#> 24.90253 24.72028 24.84177 25.19270 24.82138 24.70535 25.06052 25.17819 
#>     e~05     e~06     e~15     e~16 
#> 24.72917 24.72036 25.11136 25.13985 
#> 
#> $mads
#>     a~10     a~11     a~20     a~21     b~02     b~09     b~12     b~19 
#> 2.238950 2.243746 2.196464 2.227671 2.212583 2.312846 2.220689 2.214160 
#>     c~03     c~08     c~13     c~18     d~04     d~07     d~14     d~17 
#> 2.275791 2.302519 2.222436 2.234537 2.171028 2.127855 2.126487 2.265475 
#>     e~05     e~06     e~15     e~16 
#> 2.142237 2.196353 2.162166 2.148171 
#> 
lfqTransCheck$lfq$get_Plotter()$intensity_distribution_density()


if(require("vsn")){
 res <- lfqTrans$intensity_matrix( .func = vsn::justvsn)
 res$lfq$get_Plotter()$intensity_distribution_density()
 res$get_scales()
}
#> Warning: Expected 2 pieces. Additional pieces discarded in 15520 rows [1, 2, 3, 4, 5, 6,
#> 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
#> Joining with `by = join_by(protein_Id, sampleName, peptide_Id)`
#> $medians
#>     a~10     a~11     a~20     a~21     b~02     b~09     b~12     b~19 
#> 24.89048 24.92424 24.96236 25.00745 24.85800 24.95805 24.91216 24.97514 
#>     c~03     c~08     c~13     c~18     d~04     d~07     d~14     d~17 
#> 24.97357 24.98283 25.01958 25.00426 24.94905 24.94866 24.90674 25.02897 
#>     e~05     e~06     e~15     e~16 
#> 24.94858 24.95029 24.99417 25.00696 
#> 
#> $mads
#>     a~10     a~11     a~20     a~21     b~02     b~09     b~12     b~19 
#> 2.238950 2.243746 2.196464 2.227671 2.212583 2.312846 2.220689 2.214160 
#>     c~03     c~08     c~13     c~18     d~04     d~07     d~14     d~17 
#> 2.275791 2.302519 2.222436 2.234537 2.171028 2.127855 2.126487 2.265475 
#>     e~05     e~06     e~15     e~16 
#> 2.142237 2.196353 2.162166 2.148171 
#> 
if(require("preprocessCore")){
quant <- function(y){
 ynorm <- preprocessCore::normalize.quantiles(y)
 rownames(ynorm) <- rownames(y)
 colnames(ynorm) <- colnames(y)
 return(ynorm)
}
 res <- lfqTrans$intensity_matrix( .func = quant)
 res$lfq$get_Plotter()$intensity_distribution_density()
}
#> Warning: data already transformed. If you still want to log2 tranform, set force = TRUE