Import Metabolon Metabolomic Data
metabolon.RmdMetabolon data is…
Import Metabolon data
library(metaboprep)
# example file
filepath <- system.file("extdata", "metabolon_v1.2_example.xlsx", package = "metaboprep")
# import
dat <- read_metabolon(filepath, sheet = 'OrigScale', return_Metaboprep = FALSE)
# view structure
str(dat)
#> List of 3
#> $ data : num [1:100, 1:104] 98551 43695 44899 37811 36825 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:100] "ind1" "ind2" "ind3" "ind4" ...
#> .. ..$ : chr [1:104] "123" "124" "125" "126" ...
#> $ samples :'data.frame': 100 obs. of 5 variables:
#> ..$ sample_id : chr [1:100] "ind1" "ind2" "ind3" "ind4" ...
#> ..$ parent_sample_id : chr [1:100] "ps_id" "ps_id" "ps_id" "ps_id" ...
#> ..$ client_identifier: chr [1:100] "FR01234" "FR01235" "FR01236" "FR01237" ...
#> ..$ pair : chr [1:100] "99999" "99999" "99999" "99999" ...
#> ..$ volume_extracted : chr [1:100] "100" "100" "100" "100" ...
#> $ features:'data.frame': 104 obs. of 13 variables:
#> ..$ feature_id : chr [1:104] "123" "124" "125" "126" ...
#> ..$ pathway_sortorder: chr [1:104] "1" "2" "3" "4" ...
#> ..$ biochemical : chr [1:104] "(N(1) + N(8))-acetylspermidine" "1,2,3-benzenetriol sulfate (2)" "1,2-dilinoleoyl-GPC (18:2/18:2)" "1,2-dilinoleoyl-GPE (18:2/18:2)*" ...
#> ..$ super_pathway : chr [1:104] "Amino Acid" "Xenobiotics" "Lipid" "Lipid" ...
#> ..$ sub_pathway : chr [1:104] "Polyamine Metabolism" "Chemical" "Phosphatidylcholine (PC)" "Phosphatidylethanolamine (PE)" ...
#> ..$ platform : chr [1:104] "LC/MS Pos Early" "LC/MS Neg" "LC/MS Pos Late" "LC/MS Pos Late" ...
#> ..$ chemical_id : chr [1:104] "1111" "1112" "1113" "1114" ...
#> ..$ ri : chr [1:104] "2221" "2222" "2223" "2224" ...
#> ..$ mass : chr [1:104] "111.111" "111.11199999999999" "111.113" "111.114" ...
#> ..$ cas : chr [1:104] NA NA "111-11-1" NA ...
#> ..$ pubchem : chr [1:104] NA NA "11111" "11112" ...
#> ..$ kegg : chr [1:104] NA NA NA NA ...
#> ..$ group_hmdb : chr [1:104] NA NA "HMDB123" "HMDB124" ...Create Metaboprep object
Once imported, we pass the data to the Metaboprep class
object.
m <- Metaboprep(data = dat$data,
features = dat$features,
samples = dat$samples)
# view
summary(m)
#> Metaboprep Object Summary
#> --------------------------
#> Samples : 100
#> Features : 104
#> Data Layers : 1
#> Layer Names : input
#>
#> Sample Summary Layers : none
#> Feature Summary Layers: none
#>
#> Sample Annotation (metadata):
#> Columns: 5
#> Names : sample_id, parent_sample_id, client_identifier, pair, volume_extracted
#>
#> Feature Annotation (metadata):
#> Columns: 13
#> Names : feature_id, pathway_sortorder, biochemical, super_pathway, sub_pathway, platform, chemical_id, ri, mass, cas, pubchem, kegg, group_hmdb
#>
#> Exclusion Codes Summary:
#>
#> Sample Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_sample_missingness | 0
#> user_defined_sample_missingness | 0
#> user_defined_sample_totalpeakarea | 0
#> user_defined_sample_pca_outlier | 0
#>
#> Feature Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_feature_missingness | 0
#> user_defined_feature_missingness | 0QC Metabolon
m <- m |>
quality_control(source_layer = "input",
sample_missingness = 0.5,
feature_missingness = 0.3,
total_peak_area_sd = 5,
outlier_udist = 5,
outlier_treatment = "leave_be",
winsorize_quantile = 1.0,
tree_cut_height = 0.5,
pc_outlier_sd = 5)
#>
#> ── Starting Metabolite QC Process ──────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [9ms]
#>
#> ℹ Sample & Feature Summary Statistics for raw data
#> ✔ Sample & Feature Summary Statistics for raw data [1.1s]
#>
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [25ms]
#>
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 1 sample(s) [18ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [24m…
#>
#> 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 >=30% - excluding 0 f…
#> ✔ Assessing for feature missingness at specified level of >=30% - excluding 1 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…
#> ! 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... [1.2s]
#>
#> ℹ Metabolite QC Process Completed
#> ✔ Metabolite QC Process Completed [21ms]
# view
summary(m)
#> Metaboprep Object Summary
#> --------------------------
#> Samples : 100
#> Features : 104
#> Data Layers : 2
#> Layer Names : input, qc
#>
#> Sample Summary Layers : input, qc
#> Feature Summary Layers: input, qc
#>
#> Sample Annotation (metadata):
#> Columns: 7
#> Names : sample_id, parent_sample_id, client_identifier, pair, volume_extracted, reason_excluded, excluded
#>
#> Feature Annotation (metadata):
#> Columns: 15
#> Names : feature_id, pathway_sortorder, biochemical, super_pathway, sub_pathway, platform, chemical_id, ri, mass, cas, pubchem, kegg, group_hmdb, reason_excluded, excluded
#>
#> Exclusion Codes Summary:
#>
#> Sample Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_sample_missingness | 1
#> user_defined_sample_missingness | 0
#> user_defined_sample_totalpeakarea | 0
#> user_defined_sample_pca_outlier | 0
#>
#> Feature Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_feature_missingness | 0
#> user_defined_feature_missingness | 1