Export Data

Metaboprep can export data to various formats.

Setup

Create a Metaboprep object as described in the Getting Started vignette.

library(metaboprep)
dat       <- read_metabolon_v1(system.file("extdata", "metabolon_v1_example.xlsx", package = "metaboprep"))
m         <- Metaboprep(data = dat$data[,,1], samples = dat$samples, features = dat$features)
m         <- quality_control(metaboprep = m)
#> 
#> ── Starting Metabolite QC Process ──────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [10ms]
#> 
#> ℹ Sample & Feature Summary Statistics for raw data
#> ✔ Sample & Feature Summary Statistics for raw data [1s]
#> 
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [26ms]
#> 
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [18ms]
#> 
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [23m…
#> 
#> ℹ Assessing for sample missingness at specified level of >=50% - excluding 0 sa…
#> ✔ Assessing for sample missingness at specified level of >=50% - excluding 0 sa…
#> 
#> ℹ Assessing for feature missingness at specified level of >=50% - excluding 0 f…
#> ✔ Assessing for feature missingness at specified level of >=50% - excluding 0 f…
#> 
#> ℹ Calculating total peak abundance outliers at +/- 5 Sdev - excluding 0 sample(…
#> ✔ Calculating total peak abundance outliers at +/- 5 Sdev - excluding 0 sample(…
#> 
#> ℹ Running sample data PCA outlier analysis at +/- 5 Sdev
#> ✔ Running sample data PCA outlier analysis at +/- 5 Sdev [18ms]
#> 
#> ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#> ✔ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#> 
#> ℹ Creating final QC dataset...
#> ✔ Creating final QC dataset... [1s]
#> 
#> ℹ Metabolite QC Process Completed
#> ✔ Metabolite QC Process Completed [16ms]
#> 

Export Metaboprep

# where to put the files
output_dir <- file.path(getwd(), "output")

# run export
export(m, directory = output_dir, format = "metaboprep")
#> Exporting in metaboprep format to /home/runner/work/metaboprep/metaboprep/vignettes/output

# view output directory files
files <- list.files(output_dir, full.names = TRUE, recursive = TRUE)
unname(sapply(files, function(path) {
  parts <- strsplit(path, .Platform$file.sep)[[1]]
  paste(tail(parts, 4), collapse = .Platform$file.sep)
}))
#>  [1] "output/metaboprep_export_2025_07_04/input/config.yml"         
#>  [2] "output/metaboprep_export_2025_07_04/input/data.tsv"           
#>  [3] "output/metaboprep_export_2025_07_04/input/feature_summary.tsv"
#>  [4] "output/metaboprep_export_2025_07_04/input/features.tsv"       
#>  [5] "output/metaboprep_export_2025_07_04/input/sample_summary.tsv" 
#>  [6] "output/metaboprep_export_2025_07_04/input/samples.tsv"        
#>  [7] "output/metaboprep_export_2025_07_04/qc/config.yml"            
#>  [8] "output/metaboprep_export_2025_07_04/qc/data.tsv"              
#>  [9] "output/metaboprep_export_2025_07_04/qc/feature_summary.tsv"   
#> [10] "output/metaboprep_export_2025_07_04/qc/feature_tree.RDS"      
#> [11] "output/metaboprep_export_2025_07_04/qc/features.tsv"          
#> [12] "output/metaboprep_export_2025_07_04/qc/sample_summary.tsv"    
#> [13] "output/metaboprep_export_2025_07_04/qc/samples.tsv"           
#> [14] "output/metaboprep_export_2025_07_04/qc/var_exp.tsv"