Import Olink Proteomic Data
olink.RmdImport Olink data
Read in the Olink data using the read_olink function.
Here we will read in the example data provided with the package, as a
list object.
library(metaboprep)
# example file
olink_file <- system.file("extdata", "olink_v1_example.txt", package="metaboprep")
# import
dat <- read_olink(olink_file,
return_Metaboprep = FALSE )Quick look to identify the types of data imported
Note that we would advocate that you read in olink data as a list
object first, as it will often have additional information that you may
wish to inspect prior to creating the Metaboprep
object.
names(dat)
#> [1] "data" "samples" "features" "controls"
#> [5] "control_metadata"Create Metaboprep object
Once imported, we pass the data to the Metaboprep() function to build
the Metaboprep class object.
mydata <- Metaboprep(data = dat$data,
features = dat$features,
samples = dat$samples)Quick summary of the metaboprep object
summary(mydata)
#> Metaboprep Object Summary
#> --------------------------
#> Samples : 90
#> Features : 100
#> Data Layers : 1
#> Layer Names : input
#>
#> Sample Summary Layers : none
#> Feature Summary Layers: none
#>
#> Sample Annotation (metadata):
#> Columns: 3
#> Names : sample_id, PlateID, QC_Warning
#>
#> Feature Annotation (metadata):
#> Columns: 8
#> Names : feature_id, UniProt, Assay, MissingFreq, Panel, Panel_Version, LOD, Normalization
#>
#> 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 Olink data
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 [14ms]
#>
#> 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 [23ms]
#>
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [16ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [21m…
#>
#> ℹ Assessing for sample missingness at specified level of >=20% - excluding 0 sa…
#> ✔ Assessing for sample missingness at specified level of >=20% - excluding 0 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 [21ms]
#>
#> 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... [997ms]
#>
#> ℹ Metabolite QC Process Completed
#> ✔ Metabolite QC Process Completed [14ms]
#> Quick summary of the metaboprep object following QC
summary(mydata)
#> Metaboprep Object Summary
#> --------------------------
#> Samples : 90
#> Features : 100
#> Data Layers : 2
#> Layer Names : input, qc
#>
#> Sample Summary Layers : input, qc
#> Feature Summary Layers: input, qc
#>
#> Sample Annotation (metadata):
#> Columns: 5
#> Names : sample_id, PlateID, QC_Warning, reason_excluded, excluded
#>
#> Feature Annotation (metadata):
#> Columns: 10
#> Names : feature_id, UniProt, Assay, MissingFreq, Panel, Panel_Version, LOD, Normalization, 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