Calculate the p values for specific category network samples
calc_pvalues_network(
assayData,
metadata,
padj_method,
categories_length,
regression_method = "lm",
category_network
)a list of p values
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.
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.
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.
integer number indicating the number of categories
whether to use robust linear modelling to calculate link p values. Options are 'lm' (default) or 'rlm'. The lm implementation is faster and lighter.
network table for a specific category