profileProcess: profileProcess

In jasenfinch/profilePro: Unified Input and Output for Processing of Metabolomic Profiling Experiments

profileProcess

Description

process metabolomic profiling data

Usage

profileProcess(file_paths, sample_info, parameters)

Arguments

file_paths	character vector of file paths to use for processing
sample_info	tibble containing sample info
parameters	object of class ProfileParameters containing the parameters for processing

Value

An S4 object of class MetaboProfile

See Also

profileParameters

Examples

## Not run: 
# LCMS-RP example using the faahKO package data
## Retrieve file paths
file_paths <- list.files(
  system.file("cdf", 
            package = "faahKO"),
  full.names = TRUE,
  recursive = TRUE)[1:2]
  file_names <- basename(file_paths)
sample_names <- tools::file_path_sans_ext(file_names)

## Generate sample information table
sample_info <- tibble(fileOrder = seq_along(file_paths),
                      injOrder = seq_along(file_paths),
                      fileName = file_names,
                      batch = 1,
                      block = 1,
                      name = sample_names,
                      class = substr(sample_names,1,2))

## Generate profiling parameters
parameters <- profileParameters('LCMS-RP')
processingParameters(parameters)$peakDetection <- CentWaveParam(snthresh = 20, 
                                                                noise = 1000)
processingParameters(parameters)$retentionTimeCorrection <- ObiwarpParam()
processingParameters(parameters)$grouping <- PeakDensityParam(sampleGroups = sample_info$class,
                                                              maxFeatures = 300,
                                                              minFraction = 2/3)
## Specify parallel processing plan
plan('sequential')

## Process data
processed_data <- profileProcess(file_paths,sample_info,parameters)

# GCMS-XCMS example using the gcspikelite package data
## Retrieve file paths
file_paths <- list.files(
  system.file('data',
            package = 'gcspikelite'),
  pattern = '.CDF',
  full.names = TRUE)[1:2]
file_names <- basename(file_paths)
sample_names <- tools::file_path_sans_ext(file_names)

## Generate sample information table
sample_info <- tibble(fileOrder = seq_along(file_paths),
                      injOrder = seq_along(file_paths),
                      fileName = file_names,
                      batch = 1,
                      block = 1,
                      name = sample_names,
                      class = targets$Group[1:2])

## Generate profiling parameters
parameters <- profileParameters('GCMS-XCMS')

## Specify parallel processing plan
plan('sequential')

## Process data
processed_data <- profileProcess(file_paths,sample_info,parameters)

# GCMS-eRah example using the gcspikelite package data
## Retrieve file paths
file_paths <- list.files(
  system.file('data',
            package = 'gcspikelite'),
  pattern = '.CDF',
  full.names = TRUE)[1:2]
file_names <- basename(file_paths)
sample_names <- tools::file_path_sans_ext(file_names)

## Generate sample information table
sample_info <- tibble(fileOrder = seq_along(file_paths),
                      injOrder = seq_along(file_paths),
                      fileName = file_names,
                      batch = 1,
                      block = 1,
                      name = sample_names,
                      class = targets$Group[1:2])

## Generate profiling parameters
parameters <- profileParameters('GCMS-eRah')

## Specify parallel processing plan
plan('sequential')

## Process data
processed_data <- profileProcess(file_paths,sample_info,parameters)

## End(Not run)

jasenfinch/profilePro documentation built on Feb. 2, 2023, 9:19 p.m.