olink_ordinal_regression: Function that performs a two-way ordinal analysis.

In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink

Function that performs a two-way ordinal analysis.

Description

Function that performs a two-way ordinal analysis of variance can address an experimental design with two independent variables, each of which is a factor variable. The main effect of each independent variable can be tested, as well as the effect of the interaction of the two factors.

Usage

olink_ordinal_regression(
  df,
  variable,
  covariates = NULL,
  return.covariates = FALSE,
  check_log = NULL,
  verbose = TRUE
)

olink_ordinalRegression(
  df,
  variable,
  covariates = NULL,
  return.covariates = FALSE,
  check_log = NULL,
  verbose = TRUE
)

Arguments

df	NPX or Quantified_value data frame in long format with at least protein name (Assay), OlinkID, UniProt, Panel and a factor with at least 3 levels.
variable	Single character value or character array. Variable(s) to test. If length > 1, the included variable names will be used in crossed analyses. Also takes ':'/'*' notation.
covariates	Single character value or character array. Default: NULL. Covariates to include. Takes ':'/'*' notation. Crossed analysis will not be inferred from main effects.
return.covariates	Logical. Default: False. Returns F-test results for the covariates. Note: Adjusted p-values will be NA for the covariates.
check_log	A named list returned by check_npx(). If NULL, check_npx() will be run internally using df.
verbose	Logical. Default: True. If information about removed samples, factor conversion and final model formula is to be printed to the console.

Details

Performs an ANOVA F-test for each assay (by OlinkID) in every panel using stats::Anova and Type III sum of squares. Dependent variable will be treated as ordered factor. The function handles only factor and/or covariates.

Samples that have no variable information or missing factor levels are automatically removed from the analysis (specified in a message if verbose = T). Character columns in the input dataframe are automatically converted to factors (specified in a message if verbose = T). Crossed analysis, i.e. A*B formula notation, is inferred from the variable argument in the following cases:

• c('A','B')

• c('A: B')

• c('A: B', 'B') or c('A: B', 'A')

Inference and the formula notation of the final model are specified in a message if verbose = T.

Adjusted p-values are calculated by stats::p.adjust according to the Benjamini & Hochberg (1995) method (“fdr”). The threshold is determined by logic evaluation of Adjusted_pval < 0.05. Covariates are not included in the p-value adjustment.

Value

A tibble containing the ANOVA results for every protein. The tibble is arranged by ascending p-values. Columns include:#'

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• statistic: "numeric" value of the statistic

• p.value: "numeric" nominal p-value

• Adjusted_pval: "numeric" adjusted p-value for the test

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

if (rlang::is_installed(pkg = c("ordinal", "broom"))) {
  npx_df <- OlinkAnalyze::npx_data1 |>
    dplyr::filter(
    !grepl(
      pattern = "control",
      x = .data[["SampleID"]],
      ignore.case = TRUE
    )
  )
  check_log <- OlinkAnalyze::check_npx(df = npx_df)

  # Two-way Ordinal Regression with CLM.
  # Results in model NPX~Treatment+Time+Treatment:Time.
  ordinalRegression_results <- OlinkAnalyze::olink_ordinal_regression(
    df = npx_df,
    variable = "Treatment:Time"
  )
}

OlinkAnalyze documentation built on June 24, 2026, 1:06 a.m.