quality_control
quality_control.Rmd
library(omiprep)
# example file
filepath <- system.file("extdata", "metabolon_v2_example.xlsx", package = "omiprep")
# import data as a list object rather than directly as a Omiprep object
mydata <- read_metabolon(filepath,
sheet = 'Batch-normalized Data', ## read in the batch normalized but NOT imputed tab
feature_sheet = "Chemical Annotation", ## tab name of the feature data
feature_id_col = "COMP_ID", ## in this instance features in batch normalized..
## ..tab are annotated to column name COMP_ID. Verify in your data.
sample_sheet = "Sample Meta Data", ## tab name of the sample data
sample_id_col = "PARENT_SAMPLE_NAME", ## column name of sample IDs
return_Omiprep = TRUE ## return omiprep object
)Identify the Xenobiotics to exclude from the QC steps
xenos <- mydata@features[!is.na(mydata@features$SUPER_PATHWAY) &
mydata@features$SUPER_PATHWAY == "Xenobiotics", "feature_id"]
## how many xenobiotics identified
length(xenos)
#> [1] 39Run the quality control pipeline
# run QC
mydata <- mydata |>
quality_control(source_layer = "input",
sample_missingness = 0.2,
feature_missingness = 0.2,
total_sum_abundance_sd = 5,
outlier_udist = 5,
outlier_treatment = "leave_be",
winsorize_quantile = 1.0,
tree_cut_height = 0.5,
pc_outlier_sd = 5,
feature_selection = "least_missingness", ## We suggest using `least_missingness`
## when working with data, like Metabolon,
## with high missingness.
## Default is "max_var_exp".
features_exclude_but_keep = xenos, ## Exclude xenobiotics from QC, but retain them in the final dataset.
## We suggest this as xenos can have extreme missingness and are
## commonly qc'd from a data set. However, they may be appropriate
## to model as a binary present|absent trait. A choice for the researcher.
cores = 1
)
#>
#> ── Starting Omics QC Process ───────────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [9ms]
#>
#> ℹ Excluding 0 features from sample summary analysis but keeping in output data
#> ✔ Excluding 39 features from sample summary analysis but keeping in output data…
#>
#> 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 [1.5s]
#>
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [20ms]
#>
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [18ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 4 feature(s) [18m…
#>
#> 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 37 …
#>
#> 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 [17ms]
#>
#> 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 1.44 31.0
#> 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 0.01 0.2
#> summarise_pca 1.68 36.2
#> summarise_final 1.28 27.6
#> total 4.64 100.0
#> ✔ Creating final QC dataset... [1.3s]
#>
#> ℹ 'Omics QC Process Completed
#> ✔ 'Omics QC Process Completed [13ms]View a summary of the Omiprep object
# view summary
summary(mydata)
#> Omiprep Object Summary
#> --------------------------
#> Samples : 125
#> Features : 253
#> Data Layers : 2
#> Layer Names : input, qc
#>
#> Sample Summary Layers : input, qc
#> Feature Summary Layers: input, qc
#>
#> Sample Annotation (metadata):
#> Columns: 12
#> Names : sample_id, PARENT_SAMPLE_NAME, CLIENT_IDENTIFIER, LC/MS Polar, LC/MS Neg, LC/MS Pos Early, LC/MS Pos Late, sex, age, bmi, reason_excluded, excluded
#>
#> Feature Annotation (metadata):
#> Columns: 16
#> Names : feature_id, COMP_ID, CHEM_ID, CHEMICAL_NAME, SUPER_PATHWAY, SUB_PATHWAY, PATHWAY_SORTORDER, PLATFORM, RI, MASS, CAS, PUBCHEM, KEGG, HMDB, reason_excluded, excluded
#>
#> Exclusion Codes Summary:
#>
#> Sample Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_sample_missingness | 0
#> user_defined_sample_missingness | 1
#> user_defined_sample_totalpeakarea | 0
#> user_defined_sample_pca_outlier | 2
#>
#> Feature Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_feature_missingness | 4
#> user_defined_feature_missingness | 37
#> user_defined_feature_skewness | 0