Omiprep can export
data to various formats.
Create a Omiprep 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 = "omiprep"),
sheet="OrigScale",
return_Omiprep = FALSE)
# build the Omiprep class object
mydata <- Omiprep(data = datain$data, samples = datain$samples, features = datain$features)## Adding suppressWarnings() to avoid deparse() error when rendering vignette with S7 method warnings
mydata <- suppressWarnings( quality_control(mydata, cores = 1) )
#>
#> ── Starting Omics QC Process ───────────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [6ms]
#>
#> ℹ Sample & Feature Summary Statistics for raw data
#> ℹ Number of informative PCs (Scree acceleration factor): 2
#> ℹ Sample & Feature Summary Statistics for raw data✔ Sample & Feature Summary Statistics for raw data [1.7s]
#>
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [21ms]
#>
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [23ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 5 feature(s) [24m…
#>
#> ℹ Assessing for sample missingness at specified level of >=20% - excluding 0 sa…
#> ✔ Assessing for sample missingness at specified level of >=20% - excluding 1 sa…
#>
#> ℹ Assessing for feature missingness at specified level of >=20% - excluding 0 f…
#> ✔ Assessing for feature missingness at specified level of >=20% - excluding 46 …
#>
#> ℹ Calculating total sum abundance outliers at +/- 5 Sdev - excluding 0 sample(s)
#> ✔ Calculating total sum abundance outliers at +/- 5 Sdev - excluding 0 sample(s…
#>
#> 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…
#> ℹ Number of informative PCs (Scree acceleration factor): 2
#> ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…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...
#> ℹ Number of informative PCs (Scree acceleration factor): 2
#> ℹ Creating final QC dataset...
#> ℹ Creating final QC dataset...── Step timings ──
#> ℹ Creating final QC dataset...
#> ℹ Creating final QC dataset...
#> step seconds pct
#> validation 0.00 0.0
#> summarise_raw 1.67 32.5
#> copy_layer 0.00 0.0
#> extreme_sample_missingness 0.00 0.0
#> extreme_feature_missingness 0.00 0.0
#> sample_missingness 0.00 0.0
#> total_sum_abundance 0.02 0.4
#> summarise_pca 1.63 31.7
#> summarise_final 1.60 31.1
#> total 5.14 100.0
#> ✔ Creating final QC dataset... [1.7s]
#>
#> ℹ 'Omics QC Process Completed
#> ✔ 'Omics QC Process Completed [18ms]This will generate a folder containing an input and qc directory, each containing flat text versions of data, features, samples, feature summaries, and sample summaries.
# where to put the files
output_dir <- file.path(tempdir(), "output")
# run export
export(mydata, directory = output_dir, format = "omiprep")
#> Exporting in omiprep format to:
#> /tmp/RtmpEQsH46/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$file.sep)
}))
#> [1] "output/omiprep_export_2026_06_25/input/config.yml"
#> [2] "output/omiprep_export_2026_06_25/input/data.tsv"
#> [3] "output/omiprep_export_2026_06_25/input/feature_summary.tsv"
#> [4] "output/omiprep_export_2026_06_25/input/features.tsv"
#> [5] "output/omiprep_export_2026_06_25/input/sample_summary.tsv"
#> [6] "output/omiprep_export_2026_06_25/input/samples.tsv"
#> [7] "output/omiprep_export_2026_06_25/qc/config.yml"
#> [8] "output/omiprep_export_2026_06_25/qc/data.tsv"
#> [9] "output/omiprep_export_2026_06_25/qc/feature_summary.tsv"
#> [10] "output/omiprep_export_2026_06_25/qc/feature_tree.RDS"
#> [11] "output/omiprep_export_2026_06_25/qc/features.tsv"
#> [12] "output/omiprep_export_2026_06_25/qc/sample_summary.tsv"
#> [13] "output/omiprep_export_2026_06_25/qc/samples.tsv"
#> [14] "output/omiprep_export_2026_06_25/qc/var_exp.tsv"This will generate a Comets formated excel sheet
# where to put the files
output_dir <- file.path(tempdir(), "output")
# run export
export(mydata, directory = output_dir, format = "comets")
#> Exporting data layer `qc` in comets format to /tmp/RtmpEQsH46/output/omiprep_comets_export_2026_06_25.xlsx
#> Export complete.
# view output directory files
files <- list.files(output_dir, full.names = TRUE, recursive = TRUE)
w = grep("comets", files)
unname(sapply(files[w], function(path) {
parts <- strsplit(path, .Platform$file.sep)[[1]]
paste(tail(parts, 2), collapse = .Platform$file.sep)
}))
#> [1] "output/omiprep_comets_export_2026_06_25.xlsx"This will generate a csv file with your feature abundance/concentration/intensity data ready for upload to (MetaboAnalyst)[https://www.metaboanalyst.ca].
# where to put the files
output_dir <- file.path(tempdir(), "output")
# run export
export(mydata, directory = output_dir, format = "metaboanalyst")
#> Exporting in export_metaboanalyst format to /tmp/RtmpEQsH46/output
#> Exporting data layer `qc` in metaboanalyst format to /tmp/RtmpEQsH46/output/omiprep_metaboanalyst_export_2026_06_25.csv
# view output directory files
files <- list.files(output_dir, full.names = TRUE, recursive = TRUE)
w = grep("metaboanalyst", files)
unname(sapply(files[w], function(path) {
parts <- strsplit(path, .Platform$file.sep)[[1]]
paste(tail(parts, 2), collapse = .Platform$file.sep)
}))
#> [1] "output/omiprep_metaboanalyst_export_2026_06_25.csv"