calc_pvalues_percentile {multiDEGGs} | R Documentation |
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
)
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 |
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 |
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) |
Value
The list of float numbers of the significant pvalues for a single percentile