Skip to contents

Detect pre-treatment parameters

Source: R/detect.R
detectPretreatmentParameters.Rd

Detect suitable pre-treatment parameters for Binalysis or MetaboProfile class objects.

Usage

detectPretreatmentParameters(
  x,
  cls = "class",
  QCidx = "QC",
  miss_injections = TRUE,
  batch_correction = TRUE,
  threshold = 25
)

# S4 method for Binalysis
detectPretreatmentParameters(
  x,
  cls = "class",
  QCidx = "QC",
  miss_injections = TRUE,
  batch_correction = TRUE,
  threshold = 25
)

# S4 method for MetaboProfile
detectPretreatmentParameters(
  x,
  cls = "class",
  QCidx = "QC",
  miss_injections = TRUE,
  batch_correction = TRUE,
  threshold = 25
)

Arguments

x

S4 object of class Binalysis, MetaboProfile or AnalysisData

cls

the name of the sample information table column containing the sample class information

QCidx

QC sample class label

miss_injections

TRUE/FALSE. Detect the presence of possible miss injections and include parameters to remove these if necessary.

batch_correction

TRUE/FALSE. Detect if a batch correction is necessary and include parameters to perform this if necessary.

threshold

the percentage of the median TIC below which samples will be considered miss injections. This will be ignored if miss_injections = FALSE.

Value

An object of S4 class AnalysisParameters

Examples

## Retreive example file paths and sample information 
file_paths <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes') 

sample_information <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes') 

## Detect spectral binning parameters
bp <- binneR::detectParameters(file_paths)

## Perform spectral binning
bd <- binneR::binneRlyse(file_paths,sample_information,bp)
#> binneR v2.6.3 Fri Jul 21 17:27:30 2023
#> ________________________________________________________________________________
#> Scans: 5:13 
#> ________________________________________________________________________________
#> Reading raw data
#> Gathering bins
#> Removing single scan events
#> Averaging intensities across scans
#> Calculating bin metrics
#> Calculating accurate m/z
#> Building intensity matrix
#> Gathering file headers
#> 
#> Completed! [38.9S]

## Detect pre-treatment parameters
pp <- detectPretreatmentParameters(bd) 

pp
#> Parameters:
#> pre-treatment
#> 	correction
#> 		center
#> 			block = block
#> 			type = median
#> 	QC
#> 		occupancyFilter
#> 			cls = class
#> 			QCidx = QC
#> 			occupancy = 2/3
#> 		impute
#> 			cls = class
#> 			QCidx = QC
#> 			occupancy = 2/3
#> 			parallel = variables
#> 			seed = 1234
#> 		RSDfilter
#> 			cls = class
#> 			QCidx = QC
#> 			RSDthresh = 50
#> 		removeQC
#> 			cls = class
#> 			QCidx = QC
#> 	occupancyFilter
#> 		maximum
#> 			cls = class
#> 			occupancy = 2/3
#> 	impute
#> 		class
#> 			cls = class
#> 			occupancy = 2/3
#> 			seed = 1234
#> 	transform
#> 		TICnorm
#>