add mode parameter to lioness

R/LIONESS.R

@@ -164,6 +164,7 @@ lionessPy <- function(expr_file, motif_file=NULL, ppi_file=NULL, computing="cpu"
 #' Options include "pearson". 
 #' @param ncores int specifying the number of cores to be used. Default is 1. 
 #' (Note: constructing panda networks can be memory-intensive, and the number of cores should take into consideration available memory.)
+#' @param union Aggregation mode between three input networks: Union (default), intersection, legacy (maps on motif network).
 #' @param ... additional arguments for panda analysis
 #' @keywords keywords
 #' @importFrom matrixStats rowSds
@@ -186,7 +187,7 @@ lionessPy <- function(expr_file, motif_file=NULL, ppi_file=NULL, computing="cpu"
 #' Kuijjer, M.L., Tung, M., Yuan, G., Quackenbush, J. and Glass, K., 2015. 
 #' Estimating sample-specific regulatory networks. arXiv preprint arXiv:1505.06440.
 #' Kuijjer, M.L., Hsieh, PH., Quackenbush, J. et al. lionessR: single sample network inference in R. BMC Cancer 19, 1003 (2019). https://doi.org/10.1186/s12885-019-6235-7
-lioness = function(expr, motif = NULL, ppi = NULL, network.inference.method = "panda", ncores = 1, ...){
+lioness = function(expr, motif = NULL, ppi = NULL, network.inference.method = "panda", ncores = 1, mode = "union", ...){
 
   # If both the PPI and motif are NULL, default to Pearson instead of PANDA.
   if(is.null(motif) && is.null(ppi) && network.inference.method == "panda"){
@@ -200,11 +201,11 @@ lioness = function(expr, motif = NULL, ppi = NULL, network.inference.method = "p
   }
   if(network.inference.method == "panda")
   {
-    fullnet <- panda(motif, expr, ppi, ...)
+    fullnet <- panda(motif, expr, ppi, mode = mode, ...)
     return(mclapply(seq_len(N), function(i) {
       print(paste("Computing network for sample ", i))
       N * fullnet@regNet - (N - 1) * panda(motif, expr[, -i], 
-                                           ppi, ...)@regNet
+                                           ppi, mode = mode, ...)@regNet
     }, mc.cores = ncores))
   }
   if(network.inference.method == "pearson")