calc_pvalues_percentile: Compute interaction p values for a single percentile value

Description

Compute interaction p values for a single percentile value

Usage

calc_pvalues_percentile(
  assayData,
  metadata,
  categories_length,
  category_median_list,
  padj_method,
  percentile,
  contrasts,
  regression_method,
  edges,
  sig_edges_count
)

Value

The list of float numbers of the significant pvalues for a single percentile

Arguments

assayData: a matrix or data.frame (or list of matrices or data.frames for multi-omic analysis) containing normalised assay data. Sample IDs must be in columns and probe IDs (genes, proteins...) in rows. For multi omic analysis, it is highly recommended to use a named list of data. If unnamed, sequential names (assayData1, assayData2, etc.) will be assigned to identify each matrix or data.frame.
metadata: a named vector, matrix, or data.frame containing sample annotations or categories. If matrix or data.frame, each row should correspond to a sample, with columns representing different sample characteristics (e.g., treatment group, condition, time point). The colname of the sample characteristic to be used for differential analysis must be specified in category_variable. Rownames must match the sample IDs used in assayData. If named vector, each element must correspond to a sample characteristic to be used for differential analysis, and names must match sample IDs used in the colnames of assayData. Continuous variables are not allowed.
categories_length: integer number indicating the number of categories
category_median_list: list of category data.frames
padj_method: a character string indicating the p values correction method for multiple test adjustment. It can be either one of the methods provided by the p.adjust function from stats (bonferroni, BH, hochberg, etc.) or "q.value" for Storey's q values, or "none" for unadjusted p values. When using "q.value" the qvalue package must be installed first.
percentile: a float number indicating the percentile to use.
contrasts: data.frame containing the categories contrasts in rows
regression_method: whether to use robust linear modelling to calculate link p values. Options are 'lm' (default) or 'rlm'. The lm implementation is faster and lighter.
edges: network of biological interactions in the form of a table of class data.frame with two columns: "from" and "to".
sig_edges_count: number of significant edges (p < 0.05)