olink_normalization_bridge {OlinkAnalyze}

Bridge normalization of all proteins between two NPX projects.

Description

Normalizes two NPX projects (data frames) using shared samples.

Usage

olink_normalization_bridge(
  project_1_df,
  project_2_df,
  bridge_samples,
  project_1_name = "P1",
  project_2_name = "P2",
  project_ref_name = "P1"
)

Arguments

project_1_df

Data frame of the first project (required).

project_2_df

Data frame of the second project (required).

bridge_samples

Named list of 2 arrays containing SampleID of shared samples to be used for the calculation of adjustment factor. The names of the two arrays should be DF1 and DF2 corresponding to projects 1 and 2, respectively. Arrays should be of equal length and index of each entry should correspond to the same sample. (required)

project_1_name

Name of the first project (default: P1).

project_2_name

Name of the second project (default: P2).

project_ref_name

Name of the project to be used as reference set. Needs to be one of the project_1_name or project_2_name. It marks the project to which the other project will be adjusted to (default: P1).

Details

This function is a wrapper of olink_normalization.

In bridging normalization one of the projects is adjusted to another using shared samples (bridge samples). It is not necessary for the shared samples to be named the same in each project. Adjustment between the two projects is made using the median of the paired differences between the shared samples. The two data frames are inputs project_1_df and project_2_df, the one being adjusted to is specified in the input project_ref_name and the shared samples are specified in bridge_samples.

Value

A "tibble" of NPX data in long format containing normalized NPX values, including adjustment factors and name of project.

Examples

npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")
npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# Find overlapping samples, but exclude Olink control
overlap_samples <- dplyr::intersect(unique(npx_df1$SampleID),
                                    unique(npx_df2$SampleID))
overlap_samples_list <- list("DF1" = overlap_samples,
                             "DF2" = overlap_samples)

# Normalize
olink_normalization_bridge(project_1_df = npx_df1,
                           project_2_df = npx_df2,
                           bridge_samples = overlap_samples_list,
                           project_1_name = "P1",
                           project_2_name = "P2",
                           project_ref_name = "P1")