Import Nightingale Metabolomic Data
nightingale.RmdImport Nightingale data directly into a Metaboprep object
Read in the Nightingale Health data using the
read_nightingale function. Here we will read in the example
data provided with the package, and convert it directly into a
Metaboprep S7 object.
library(metaboprep)
# example file
filepath <- system.file("extdata", "nightingale_v1_example.xlsx", package = "metaboprep")
# import
mydata <- read_nightingale(filepath, return_Metaboprep = TRUE)Quick look at data structure of the imported data
str(mydata)
#> <metaboprep::Metaboprep>
#> @ data : num [1:50, 1:12, 1] 3.85 NA 5.2 3.01 2.68 ...
#> .. - attr(*, "dimnames")=List of 3
#> .. ..$ : chr [1:50] "ind1" "ind2" "ind3" "ind4" ...
#> .. ..$ : chr [1:12] "Total-C" "non-HDL-C" "Remnant-C" "VLDL-C" ...
#> .. ..$ : chr "input"
#> @ samples :'data.frame': 50 obs. of 37 variables:
#> .. $ sample_id : chr "ind1" "ind2" "ind3" "ind4" ...
#> .. $ informed_sample_type : chr "Serum" "Serum" "Serum" "Serum" ...
#> .. $ sample_excluded : chr NA NA NA NA ...
#> .. $ sample_notes : chr NA NA NA NA ...
#> .. $ edta_plasma : chr "0" "1" "0" "0" ...
#> .. $ citrate_plasma : chr "0" "0" "0" "0" ...
#> .. $ low_ethanol : chr "0" "0" "0" "0" ...
#> .. $ medium_ethanol : chr "0" "1" "0" "0" ...
#> .. $ high_ethanol : chr "0" "0" "0" "0" ...
#> .. $ isopropyl_alcohol : chr "0" "0" "0" "0" ...
#> .. $ 1methyl2pyrrolidone : chr "0" "0" "0" "0" ...
#> .. $ polysaccharides : chr "0" "0" "0" "0" ...
#> .. $ aminocaproic_acid : chr "0" "0" "0" "0" ...
#> .. $ low_glucose : chr "0" "0" "0" "0" ...
#> .. $ high_lactate : chr "0" "0" "0" "0" ...
#> .. $ high_pyruvate : chr "0" "0" "0" "0" ...
#> .. $ low_glutamine__high_glutamate : chr "0" "0" "0" "0" ...
#> .. $ gluconolactone : chr "0" "0" "0" "0" ...
#> .. $ low_protein : chr "1" "1" "0" "0" ...
#> .. $ unexpected_amino_acid_signals : chr "0" "0" "0" "0" ...
#> .. $ unidentified_macromolecules : chr "0" "0" "0" "0" ...
#> .. $ unidentified_small_molecule (a): chr "0" "0" "0" "0" ...
#> .. $ unidentified_small_molecule (b): chr "0" "0" "0" "0" ...
#> .. $ unidentified_small_molecule (c): chr "0" "0" "0" "0" ...
#> .. $ below_limit_of_quantification : chr "1" "1" "1" "1" ...
#> .. $ totalc : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA 1 NA NA NA NA NA 1 NA NA ...
#> .. $ nonhdlc : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA 1 NA NA 1 NA NA NA 1 NA ...
#> .. $ remnantc : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA NA NA NA NA NA NA NA NA ...
#> .. $ vldlc : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA 1 NA NA NA NA NA NA NA ...
#> .. $ clinical_ldlc : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA NA NA NA NA NA NA NA NA ...
#> .. $ ldlc : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA NA NA NA 1 NA NA NA NA ...
#> .. $ hdlc : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA NA NA NA NA NA NA NA NA ...
#> .. $ totaltg : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA NA NA NA NA NA NA NA NA ...
#> .. $ vldltg : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA 1 NA NA NA NA NA NA NA NA ...
#> .. $ ldltg : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA 1 NA NA NA NA NA NA NA ...
#> .. $ hdltg : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": 1 NA NA NA NA NA NA NA NA NA ...
#> .. $ omega3_pct : Factor w/ 1 level "Value was rejected by automatic sample and measurement quality control": NA NA NA NA NA NA NA NA NA NA ...
#> @ features :'data.frame': 12 obs. of 6 variables:
#> .. $ feature_id : chr "Total-C" "non-HDL-C" "Remnant-C" "VLDL-C" ...
#> .. $ csv_column_name: chr "Total_C" "non_HDL_C" "Remnant_C" "VLDL_C" ...
#> .. $ biomarker_name : chr "Total cholesterol" "Total cholesterol minus HDL-C" "Remnant cholesterol (non-HDL, non-LDL -cholesterol)" "VLDL cholesterol" ...
#> .. $ unit : chr "mmol/l" "mmol/l" "mmol/l" "mmol/l" ...
#> .. $ group : chr "Cholesterol" "Cholesterol" "Cholesterol" "Cholesterol" ...
#> .. $ subgroup : chr NA NA NA NA ...
#> @ exclusions :List of 2
#> .. $ samples :List of 5
#> .. ..$ user_excluded : chr(0)
#> .. ..$ extreme_sample_missingness : chr(0)
#> .. ..$ user_defined_sample_missingness : chr(0)
#> .. ..$ user_defined_sample_totalpeakarea: chr(0)
#> .. ..$ user_defined_sample_pca_outlier : chr(0)
#> .. $ features:List of 3
#> .. ..$ user_excluded : chr(0)
#> .. ..$ extreme_feature_missingness : chr(0)
#> .. ..$ user_defined_feature_missingness: chr(0)
#> @ feature_summary: num[0 , 0 , 0 ]
#> @ sample_summary : num[0 , 0 , 0 ]QC Nightingale
Perform the QC steps using the quality_control
function.
mydata <- mydata |>
quality_control(source_layer = "input",
sample_missingness = 0.2,
feature_missingness = 0.2,
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,
feature_selection = "max_var_exp",
features_exclude_but_keep = NULL
)
#>
#> ── Starting Metabolite QC Process ──────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [7ms]
#>
#> ℹ Sample & Feature Summary Statistics for raw data
#> ✔ Sample & Feature Summary Statistics for raw data [137ms]
#>
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [22ms]
#>
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 2 sample(s) [16ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [20m…
#>
#> 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 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 [20ms]
#>
#> 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... [104ms]
#>
#> ℹ Metabolite QC Process Completed
#> ✔ Metabolite QC Process Completed [13ms]Quick summary of the metaboprep object following QC
summary(mydata)
#> Metaboprep Object Summary
#> --------------------------
#> Samples : 50
#> Features : 12
#> Data Layers : 2
#> Layer Names : input, qc
#>
#> Sample Summary Layers : input, qc
#> Feature Summary Layers: input, qc
#>
#> Sample Annotation (metadata):
#> Columns: 39
#> Names : sample_id, informed_sample_type, sample_excluded, sample_notes, edta_plasma, citrate_plasma, low_ethanol, medium_ethanol, high_ethanol, isopropyl_alcohol, 1methyl2pyrrolidone, polysaccharides, aminocaproic_acid, low_glucose, high_lactate, high_pyruvate, low_glutamine__high_glutamate, gluconolactone, low_protein, unexpected_amino_acid_signals, unidentified_macromolecules, unidentified_small_molecule (a), unidentified_small_molecule (b), unidentified_small_molecule (c), below_limit_of_quantification, totalc, nonhdlc, remnantc, vldlc, clinical_ldlc, ldlc, hdlc, totaltg, vldltg, ldltg, hdltg, omega3_pct, reason_excluded, excluded
#>
#> Feature Annotation (metadata):
#> Columns: 8
#> Names : feature_id, csv_column_name, biomarker_name, unit, group, subgroup, reason_excluded, excluded
#>
#> Exclusion Codes Summary:
#>
#> Sample Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_sample_missingness | 2
#> user_defined_sample_missingness | 1
#> 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 | 0