olink_ordinal_regression_posthoc: Function which performs an posthoc test per protein.

In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink

Function which performs an posthoc test per protein.

Description

Performs a post hoc ANOVA test using emmeans::emmeans with Tukey p-value adjustment per assay (by OlinkID) for each panel at confidence level 0.95. See olink_anova for details of input notation.

The function handles both factor and numerical variables and/or covariates. The posthoc test for a numerical variable compares the difference in means of the ordinal outcome variable (default: NPX) for 1 standard deviation difference in the numerical variable, e.g. mean ordinal NPX at mean(numerical variable) versus mean NPX at mean(numerical variable) + 1*SD(numerical variable).

Usage

olink_ordinal_regression_posthoc(
  df,
  olinkid_list = NULL,
  variable,
  covariates = NULL,
  effect,
  effect_formula,
  mean_return = FALSE,
  post_hoc_padjust_method = "tukey",
  check_log = NULL,
  verbose = TRUE
)

olink_ordinalRegression_posthoc(
  df,
  olinkid_list = NULL,
  variable,
  covariates = NULL,
  effect,
  effect_formula,
  mean_return = FALSE,
  post_hoc_padjust_method = "tukey",
  check_log = NULL,
  verbose = TRUE
)

Arguments

df	NPX data frame in long format with at least protein name (Assay), OlinkID, UniProt, Panel and a factor with at least 3 levels.
olinkid_list	Character vector of OlinkID's on which to perform post hoc analysis. If not specified, all assays in df are used.
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.
effect	Term on which to perform post-hoc. Character vector. Must be subset of or identical to variable.
effect_formula	(optional) A character vector specifying the names of the predictors over which estimated marginal means are desired as defined in the emmeans package. May also be a formula. If provided, this will override the effect argument. See ?emmeans::emmeans() for more information.
mean_return	Boolean. If true, returns the mean of each factor level rather than the difference in means (default). Note that no p-value is returned for mean_return = TRUE and no adjustment is performed.
post_hoc_padjust_method	P-value adjustment method to use for post-hoc comparisons within an assay. Options include tukey, sidak, bonferroni and none.
check_log	A named list returned by check_npx(). If NULL, check_npx() will be run internally using df.
verbose	Boolean. Default: True. If information about removed samples, factor conversion and final model formula is to be printed to the console.

Value

Tibble of posthoc tests for specified effect, arranged by ascending adjusted 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

• contrast: "character" the groups that were compared

• estimate: "numeric" difference in mean of the ordinal NPX between groups

• 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", "emmeans"))) {
  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"
  )

  significant_assays <- ordinalRegression_results |>
    dplyr::filter(
      .data[["Threshold"]] == "Significant"
      & .data[["term"]] == "Time"
    ) |>
    dplyr::pull(
      .data[["OlinkID"]]
    ) |>
    unique()

  # Posthoc test
  ordRegr_results_posthoc <- OlinkAnalyze::olink_ordinal_regression_posthoc(
    df = npx_df,
    variable = c("Treatment:Time"),
    olinkid_list = significant_assays,
    effect = "Time",
    check_log = check_log
  )
}

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