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Perform Median Normalization

Source: R/medianNormalize.R
medianNormalize.Rd

Performs median normalization on a soma_adat object that has already undergone standard data processing for array-based SomaScan studies.

Median normalization is a common, scale-based normalization technique that corrects for assay-derived technical variation by applying sample-specific linear scaling to expression measurements. Typical sources of assay variation include robotic and manual liquid handling, manufactured consumables such as buffers and plastic goods, laboratory instrument calibration, ambient environmental conditions, inter-operator differences, and other sources of technical variation. Median normalization can improve assay precision and reduce technical variation that can mask true biological signal.

The method scales each sample so that the center of the within-sample analyte distribution aligns to a defined reference, thereby correcting global intensity shifts without altering relative differences between measurements within a sample. For assay formats with multiple dilution groups (e.g., 1:5 or 20%; 1:200 or 0.5%; 1:20,000 or 0.005%), separate scale factors are calculated for each dilution because each dilution group is processed separately during the assay. For each sample, the ratio of reference RFU / observed RFU is calculated for every SeqId. The median ratio within each dilution group is selected as the scale factor and applied to all SeqIds for that sample within the associated dilution bin.

Usage

medianNormalize(adat, reference = NULL, by = "SampleType", verbose = TRUE)

Arguments

adat

A soma_adat object created using read_adat(), containing RFU values that have been hybridization normalized and plate scaled.

reference

Optional. Reference for median normalization. Can be:

  • NULL (default): Calculate an internal reference from study samples by taking the median of each SeqId within the sample grouping.

  • A soma_adat object: Extract reference from this ADAT

  • A data.frame: Use provided reference data directly

When providing an external reference data.frame it must contain:

SeqId

Character column containing the SeqId identifiers mapping to those in the soma_adat object. Must be in "10000-28" format, not "seq.10000.28" format.

Reference

Numeric column containing the reference RFU values for each SeqId.

by

Character vector. Grouping variable(s) for grouped median normalization. Must be column name(s) in the ADAT. Normalization will be performed within each group separately. Default is "SampleType". Note that only study samples (SampleType == 'Sample') are normalized; QC, Calibrator, and Buffer samples are automatically excluded.

verbose

Logical. Should progress messages be printed? Default is TRUE.

Value

A soma_adat object with median normalization applied and RFU values adjusted. The existing NormScale_* columns are updated to include the effects of both plate scale normalization and median normalization.

Data Requirements

This function is designed for data in standard SomaLogic deliverable formats. Specific ADAT file requirements:

  1. Intact ADAT file, with available data processing information in the header section. Specifically, the ProcessSteps field must be present and correctly represent the data processing steps present in the data table.

  2. Minimal standard processing, the function assumes a standard SomaScan data deliverable with minimally standard HybNorm and PlateScale steps applied.

Primary use cases:

  • Combining data sets from the same overarching experiment or sample population and normalize to a common reference that were originally processed separately and each normalized "within study".

  • Normalize fundamentally different types of samples separately (by group). For instance, lysate samples from different cell lines that will be analyzed separately should likely be median normalized within each cell type. Lysis buffer background samples would also be expected to be normalized separately.

Important Considerations

  • A core assumption of median normalization is that the majority of analytes are not differentially expressed; consequently, users should validate this assumption by inspecting scale-factor distributions for systematic bias between the biological groups intended for comparison.

  • Note this function does not perform the adaptive normalization by maximum likelihood (ANML) method which leverages a population-based reference that iteratively down-selects the set of analytes to include for the normalization calculation.

  • This function requires unnormalized data as input. If study samples have already undergone median normalization (ANML or standard), first use reverseMedianNormalize() to remove existing normalization.

Examples

if (FALSE) { # \dontrun{
# Starting with unnormalized ADAT
unnormalized_adat <- read_adat("unnormalized_study_data.adat")

# Internal reference from study samples (default)
med_norm_adat <- medianNormalize(unnormalized_adat)

# Reference from another ADAT
ref_adat <- read_adat("reference_file.adat")
med_norm_adat <- medianNormalize(unnormalized_adat, reference = ref_adat)

# External reference as a data.frame - requires `SeqId` and `Reference` columns
ref_data <- read.csv("reference_file.csv")
med_norm_adat <- medianNormalize(unnormalized_adat, reference = ref_data)

# Custom grouping by multiple variables
# Use when total protein load changes due to analysis conditions
med_norm_adat <- medianNormalize(unnormalized_adat, by = c("Sex", "SampleType"))

# If you already have normalized data, first reverse the normalization
normalized_adat <- read_adat("normalized_study_data.adat")
unnormalized_adat <- reverseMedianNormalize(normalized_adat)
custom_norm_adat <- medianNormalize(unnormalized_adat, reference = new_reference)
} # }