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)

Arguments

matrix_list

list expression data for each element

cluster_list

list df item from degPattern output

metadata_list

list data.frames from each element with design experiment. Normally colData output

summarize

character column to use to group samples

time

character column to use as x-axes in figures

col

character column to color samples in figures

scale

boolean scale by row expression matrix

mapping

data.frame mapping table in case elements use different ID in the row.names of expression matrix. For instance, when integrating miRNA/mRNA.

Value

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.