Import Nightingale Metabolomic Data
nightingale.RmdNightingale data is…
Import Nightingale data
library(metaboprep)
# example file
filepath <- system.file("extdata", "nightingale_v2_example.xlsx", package = "metaboprep")
# import
dat <- read_nightingale(filepath, return_Metaboprep = FALSE)
# view structure
str(dat)
#> List of 3
#> $ data : num [1:50, 1:15] 2.84e-10 3.86e-11 9.59e-11 2.21e-10 1.24e-10 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:50] "ind1" "ind2" "ind3" "ind4" ...
#> .. ..$ : chr [1:15] "XXL-VLDL-P" "XXL-VLDL-L" "XXL-VLDL-PL" "XXL-VLDL-C" ...
#> $ samples :'data.frame': 50 obs. of 5 variables:
#> ..$ sample_id : chr [1:50] "ind1" "ind2" "ind3" "ind4" ...
#> ..$ high_pyruvate : int [1:50] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ high_lactate : int [1:50] 0 0 0 0 0 0 0 1 0 0 ...
#> ..$ low_glutamine__high_glutamate: int [1:50] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ plasma_sample : int [1:50] 0 0 0 0 0 0 0 0 0 0 ...
#> $ features:'data.frame': 15 obs. of 1 variable:
#> ..$ feature_id: chr [1:15] "XXL-VLDL-P" "XXL-VLDL-L" "XXL-VLDL-PL" "XXL-VLDL-C" ...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 : 50
#> Features : 15
#> Data Layers : 1
#> Layer Names : input
#>
#> Sample Summary Layers : none
#> Feature Summary Layers: none
#>
#> Sample Annotation (metadata):
#> Columns: 5
#> Names : sample_id, high_pyruvate, high_lactate, low_glutamine__high_glutamate, plasma_sample
#>
#> Feature Annotation (metadata):
#> Columns: 1
#> Names : feature_id
#>
#> 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 Nightingale
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 [8ms]
#>
#> ℹ Sample & Feature Summary Statistics for raw data
#> ✔ Sample & Feature Summary Statistics for raw data [150ms]
#>
#> 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) [19ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [18m…
#>
#> 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 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…
#> ! The stated max PCs [max_num_pcs=10] to use in PCA outlier assessment is greater than the number of available informative PCs [7]
#> 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... [117ms]
#>
#> ℹ Metabolite QC Process Completed
#> ✔ Metabolite QC Process Completed [20ms]
# view
summary(m)
#> Metaboprep Object Summary
#> --------------------------
#> Samples : 50
#> Features : 15
#> 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, high_pyruvate, high_lactate, low_glutamine__high_glutamate, plasma_sample, reason_excluded, excluded
#>
#> Feature Annotation (metadata):
#> Columns: 3
#> Names : feature_id, reason_excluded, excluded
#>
#> 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 | 0