panda.to.alpaca.RdALPACA(ALtered Partitions Across Community Architectures) is a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules.
[(Padi and Quackenbush 2018)])
This function compares two networks generate by panda.py in this package and finds the sets of nodes that best characterize the change in modular structure.
panda.to.alpaca( panda.net1, panda.net2, file.stem = "./alpaca", verbose = FALSE )
| panda.net1 | data.frame indicating an complete network of one condition generated by |
|---|---|
| panda.net2 | data.frame indicating an complete network of another condition generated by |
| file.stem | String indicating the folder path and prefix of result files, where all results will be stored. |
| verbose | Boolean vector indicating whether the full differential modularity matrix should also be written to a file. The default values is 'FALSE'. |
A string message showing the location of output file if file.stem is given, and a List where the first element is the membership vector and second element is the contribution score of each node to its module's total differential modularity
# refer to four input datasets files in inst/extdat treated_expression_file_path <- system.file("extdata", "expr4_matched.txt", package = "netZooR", mustWork = TRUE) control_expression_file_path <- system.file("extdata", "expr10_matched.txt", package = "netZooR", mustWork = TRUE) motif_file_path <- system.file("extdata", "chip_matched.txt", package = "netZooR", mustWork = TRUE) ppi_file_path <- system.file("extdata", "ppi_matched.txt", package = "netZooR", mustWork = TRUE) # Run PANDA for treated and control network treated_panda_net <- panda.py(expr_file = treated_expression_file_path, motif_file = motif_file_path, ppi_file = ppi_file_path, modeProcess="legacy", remove_missing = TRUE )$panda control_panda_net <- panda.py(expr_file = control_expression_file_path, motif_file = motif_file_path, ppi_file = ppi_file_path, modeProcess="legacy", remove_missing = TRUE )$panda # Run ALPACA alpaca<- panda.to.alpaca(treated_panda_net, control_panda_net, "./TB", verbose=TRUE)