OlinkAnalyze (version 4.2.0)

olink_anova: Function which performs an ANOVA per protein

Description

Performs an ANOVA F-test for each assay (by OlinkID) in every panel using car::Anova and Type III sum of squares. The function handles both factor and numerical variables 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 = TRUE). Character columns in the input dataframe are automatically converted to factors (specified in a message if verbose = TRUE). Numerical variables are not converted to factors. Control samples should be removed before using this function. Control assays (AssayType is not "assay", or Assay contains "control" or "ctrl") should be removed before using this function. If a numerical variable is to be used as a factor, this conversion needs to be done on the dataframe before the function call.

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 is specified in a message if verbose = TRUE.
For covariates, crossed analyses need to be specified explicitly, i.e. two main effects will not be expanded with a c('A','B') notation. Main effects present in the variable takes precedence. The formula notation of the final model is specified in a message if verbose = TRUE.

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.

Usage

olink_anova(
  df,
  variable,
  outcome = "NPX",
  covariates = NULL,
  model_formula,
  return.covariates = FALSE,
  verbose = TRUE
)

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

• df: "numeric" degrees of freedom

• sumsq: "numeric" sum of square

• meansq: "numeric" mean of square

• statistic: "numeric" value of the statistic

• p.value: "numeric" nominal p-value

• Adjusted_pval: "numeric" adjusted p-value for the test (Benjamini&Hochberg)

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

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.

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 ':' or '*' notation.

outcome

Character. The dependent variable. Default: NPX.

covariates

Single character value or character array. Default: NULL. Covariates to include. Takes ':' or '*' notation. Crossed analysis will not be inferred from main effects.

model_formula

(optional) Symbolic description of the model to be fitted in standard formula notation (e.g. "NPX~A*B"). If provided, this will override the outcome, variable and covariates arguments. Can be a string or of class stats::formula().

return.covariates

Boolean. Default: False. Returns F-test results for the covariates. Note: Adjusted p-values will be NA for the covariates.

verbose

Boolean. Default: True. If information about removed samples, factor conversion and final model formula is to be printed to the console.

Examples

# \donttest{

library(dplyr)

npx_df <- npx_data1 |> filter(!grepl('control|ctrl',SampleID, ignore.case = TRUE))

#One-way ANOVA, no covariates.
#Results in a model NPX~Time
anova_results <- olink_anova(df = npx_df, variable = "Time")

#Two-way ANOVA, one main effect covariate.
#Results in model NPX~Treatment*Time+Site.
anova_results <- olink_anova(df = npx_df,
                             variable=c("Treatment:Time"),
                             covariates="Site")

#One-way ANOVA, interaction effect covariate.
#Results in model NPX~Treatment+Site:Time+Site+Time.
anova_results <- olink_anova(df = npx_df,
                             variable="Treatment",
                             covariates="Site:Time")# }