The simplest case is if you want to convine the pattern profile for gene expression data and proteomic data. It will use the first element as the base for the integration. Then, it will loop through clusters and run degPatterns in the second data set to detect patterns that match this one.
degMerge(matrix_list, cluster_list, metadata_list, summarize = "group", time = "time", col = "condition", scale = TRUE, mapping = NULL)
list expression data for each element
list df item from degPattern output
list data.frames from each element
with design experiment. Normally
character column to use to group samples
character column to use as x-axes in figures
character column to color samples in figures
boolean scale by row expression matrix
data.frame mapping table in case elements use different ID in the row.names of expression matrix. For instance, when integrating miRNA/mRNA.
A data.frame with information on what genes are in each cluster in all data set, and the correlation value for each pair cluster comparison.