
Export Data
export.RmdMetaboprep can export data to various formats.
Setup
Read in the data and make a Metaboprep object
Create a Metaboprep object as described in the Getting Started vignette.
# read in the metabolon data as a list object
datain <- read_metabolon(system.file("extdata", "metabolon_v1.1_example.xlsx", package = "metaboprep"),
sheet="OrigScale",
return_Metaboprep = FALSE)
# build the Metaboprep class object
mydata <- Metaboprep(data = datain$data, samples = datain$samples, features = datain$features)Run the quality control
## Adding suppressWarnings() to avoid deparse() error when rendering vignette with S7 method warnings
mydata <- suppressWarnings( quality_control(mydata) )
#>
#> ── Starting Metabolite QC Process ──────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [8ms]
#>
#> ℹ Sample & Feature Summary Statistics for raw data
#> ✔ Sample & Feature Summary Statistics for raw data [921ms]
#>
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [37ms]
#>
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [17ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [16m…
#>
#> ℹ Assessing for sample missingness at specified level of >=20% - excluding 0 sa…
#> ✔ Assessing for sample missingness at specified level of >=20% - excluding 2 sa…
#>
#> ℹ Assessing for feature missingness at specified level of >=20% - excluding 0 f…
#> ✔ Assessing for feature missingness at specified level of >=20% - 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 [17ms]
#>
#> ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#> ! The stated max PCs [max_num_pcs=10] to use in PCA outlier assessment is greater than the number of available informative PCs [2]
#> 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... [861ms]
#>
#> ℹ Metabolite QC Process Completed
#> ✔ Metabolite QC Process Completed [24ms]Export Metaboprep
# where to put the files
output_dir <- file.path(getwd(), "output")
# run export
export(mydata, 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_12_05/input/config.yml"
#> [2] "output/metaboprep_export_2025_12_05/input/data.tsv"
#> [3] "output/metaboprep_export_2025_12_05/input/feature_summary.tsv"
#> [4] "output/metaboprep_export_2025_12_05/input/features.tsv"
#> [5] "output/metaboprep_export_2025_12_05/input/sample_summary.tsv"
#> [6] "output/metaboprep_export_2025_12_05/input/samples.tsv"
#> [7] "output/metaboprep_export_2025_12_05/qc/config.yml"
#> [8] "output/metaboprep_export_2025_12_05/qc/data.tsv"
#> [9] "output/metaboprep_export_2025_12_05/qc/feature_summary.tsv"
#> [10] "output/metaboprep_export_2025_12_05/qc/feature_tree.RDS"
#> [11] "output/metaboprep_export_2025_12_05/qc/features.tsv"
#> [12] "output/metaboprep_export_2025_12_05/qc/sample_summary.tsv"
#> [13] "output/metaboprep_export_2025_12_05/qc/samples.tsv"
#> [14] "output/metaboprep_export_2025_12_05/qc/var_exp.tsv"