olink_normalization: Normalize two Olink datasets

In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink

Normalize two Olink datasets

Description

Normalizes two Olink datasets to each other, or one Olink dataset to a reference set of medians values.

Usage

olink_normalization(
  df1,
  df2 = NULL,
  overlapping_samples_df1,
  overlapping_samples_df2 = NULL,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1",
  reference_medians = NULL,
  format = FALSE
)

Arguments

df1	First dataset to be used for normalization (required).
df2	Second dataset to be used for normalization. Required for bridge and subset normalization.
overlapping_samples_df1	Character vector of samples to be used for the calculation of adjustment factors in df1 (required).
overlapping_samples_df2	Character vector of samples to be used for the calculation of adjustment factors in df2. Required for subset normalization.
df1_project_nr	Project name of first dataset (required).
df2_project_nr	Project name of second dataset. Required for bridge and subset normalization.
reference_project	Project to be used as reference project. Should be one of df1_project_nr and df2_project_nr. Required for bridge and subset normalization.
reference_medians	Dataset with columns "OlinkID" and "Reference_NPX". Required for reference median normalization.
format	Boolean that controls whether the normalized dataset will be formatted for input to downstream analysis. Only applicable for cross-product bridge normalization.

Details

The function handles four different types of normalization:

• Bridge normalization: One of the datasets is adjusted to another using overlapping samples (bridge samples). Overlapping samples need to have the same identifiers in both datasets. Normalization is performed using the median of the pair-wise differences between the bridge samples in the two datasets. The two datasets are provided as df1 and df2, and the one being adjusted to is specified in the input reference_project; overlapping samples are specified in overlapping_samples_df1. Only overlapping_samples_df1 should be provided regardless of the dataset used as reference_project.

• Subset normalization: One of the datasets is adjusted to another using a subset of samples from each. Normalization is performed using the differences of the medians between the subsets from the two datasets. Both overlapping_samples_df1 and overlapping_samples_df2 need to be provided, and sample identifiers do not need to be the same.

  • A special case of subset normalization occurs when all samples (except control samples and samples with QC warnings) from each dataset are used for normalization; this special case is called intensity normalization. In intensity normalization all unique sample identifiers from df1 are provided as input in overlapping_samples_df1 and all unique sample identifiers from df2 are provided as input in overlapping_samples_df2.

• Reference median normalization: One of the datasets (df1) is adjusted to a predefined set of adjustment factors. This is effectively subset normalization, but using differences of medians to pre-recorded median values. df1, overlapping_samples_df1, df1_project_nr and reference_medians need to be specified. Dataset df1 is normalized using the differences in median between the overlapping samples and the reference medians.

• Cross-product normalization: One of the datasets is adjusted to another using the median of pair-wise differences of overlapping samples (bridge samples) or quantile smoothing using overlapping samples as reference to adjust the distributions. Overlapping samples need to have the same identifiers in both datasets. The two datasets are provided as df1 and df2, and the one being adjusted to is specified in the input reference_project; Note that in cross-product normalization the reference project is predefined, and in case the argument reference_project does not match the expected reference project an error will be returned. Overlapping samples are specified in overlapping_samples_df1. Only overlapping_samples_df1 should be provided regardless of the dataset used as reference_project. This functionality does not modify the column with original quantification values (e.g. NPX), instead it normalizes it with 2 different approaches in columns "MedianCenteredNPX" and "QSNormalizedNPX", and provides a recommendation in "BridgingRecommendation" about which of the two columns is to be used.

The output dataset is df1 if reference median normalization, or df2 appended to df1 if bridge, subset or cross-product normalization. The output dataset contains all original columns from the original dataset(s), and the columns:

• "Project" and "Adj_factor" in case of reference median, bridge and subset normalization. The former marks the project of origin based on df1_project_nr and df2_project_nr, and the latter the adjustment factor that was applied to the non-reference dataset.

• "Project", "OlinkID_E3072", "MedianCenteredNPX", "QSNormalizedNPX", "BridgingRecommendation" in case of cross-product normalization. The columns correspond to the project of origin based on df1_project_nr and df2_project_nr, the assay identifier in the non-reference project, the bridge-normalized quantification value, the quantile smoothing-normalized quantification value, and the recommendation about which of the two normalized values is more suitable for downstream analysis.

Value

Tibble or ArrowObject with the normalized dataset.

Examples

# prepare datasets
npx_df1 <- npx_data1 |>
  dplyr::mutate(
    Normalization = "Intensity"
  )
npx_df2 <- npx_data2 |>
  dplyr::mutate(
    Normalization = "Intensity"
  )

# bridge normalization

# overlapping samples - exclude control samples
overlap_samples <- intersect(x = npx_df1$SampleID,
                             y = npx_df2$SampleID) |>
  (\(x) x[!grepl("^CONTROL_SAMPLE", x)])()

# normalize
olink_normalization(
  df1 = npx_df1,
  df2 = npx_df2,
  overlapping_samples_df1 = overlap_samples,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1"
)

# subset normalization

# find a suitable subset of samples from each dataset:
# exclude control samples
# exclude samples that do not pass QC
df1_samples <- npx_df1 |>
  dplyr::group_by(
    dplyr::pick(
      dplyr::all_of("SampleID")
    )
  )|>
  dplyr::filter(
    all(.data[["QC_Warning"]] == 'Pass')
  ) |>
  dplyr::ungroup() |>
  dplyr::filter(
    !grepl(pattern = "^CONTROL_SAMPLE", x = .data[["SampleID"]])
  ) |>
  dplyr::pull(
    .data[["SampleID"]]
  ) |>
  unique()
df2_samples <- npx_df2 |>
  dplyr::group_by(
    dplyr::pick(
      dplyr::all_of("SampleID")
    )
  )|>
  dplyr::filter(
    all(.data[["QC_Warning"]] == 'Pass')
  ) |>
  dplyr::ungroup() |>
  dplyr::filter(
    !grepl(pattern = "^CONTROL_SAMPLE", x = .data[["SampleID"]])
  ) |>
  dplyr::pull(
    .data[["SampleID"]]
  ) |>
  unique()

# select a subset of samples from each set from above
df1_subset <- sample(x = df1_samples, size = 16L)
df2_subset <- sample(x = df2_samples, size = 20L)

# normalize
olink_normalization(
  df1 = npx_df1,
  df2 = npx_df2,
  overlapping_samples_df1 = df1_subset,
  overlapping_samples_df2 = df2_subset,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1"
)

# special case of subset normalization using all samples
olink_normalization(
  df1 = npx_df1,
  df2 = npx_df2,
  overlapping_samples_df1 = df1_samples,
  overlapping_samples_df2 = df2_samples,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1"
)

# reference median normalization

# For the sake of this example, set the reference median to 1
ref_med_df <- npx_data1 |>
  dplyr::select(
    dplyr::all_of(
      c("OlinkID")
    )
  ) |>
  dplyr::distinct() |>
  dplyr::mutate(
    Reference_NPX = runif(n = dplyr::n(),
                          min = -1,
                          max = 1)
  )

# normalize
olink_normalization(
  df1 = npx_df1,
  overlapping_samples_df1 = df1_subset,
  reference_medians = ref_med_df
)

# cross-product normalization

# get reference samples
overlap_samples_product <- intersect(
  x = unique(OlinkAnalyze:::data_ht_small$SampleID),
  y = unique(OlinkAnalyze:::data_3k_small$SampleID)
) |>
  (\(.) .[!grepl("CONTROL", .)])()

# normalize
olink_normalization(
  df1 = OlinkAnalyze:::data_ht_small,
  df2 = OlinkAnalyze:::data_3k_small,
  overlapping_samples_df1 = overlap_samples_product,
  df1_project_nr = "proj_ht",
  df2_project_nr = "proj_3k",
  reference_project = "proj_ht",
  format = FALSE
)

OlinkAnalyze documentation built on April 4, 2025, 3:26 a.m.