Kate dev

R/LIONESS.R

@@ -170,16 +170,33 @@ lionessPy <- function(expr_file, motif_file=NULL, ppi_file=NULL, computing="cpu"
 #' "coopNet" is the cooperative network
 #' @examples
 #' data(pandaToyData)
-#' linonessRes <- lioness(pandaToyData$motif,
-#'     pandaToyData$expression[,1:3],pandaToyData$ppi,hamming=1,progress=FALSE)
+#' lionessRes <- lioness(expr = pandaToyData$expression[,1:3], motif = pandaToyData$motif, ppi = pandaToyData$ppi,hamming=1,progress=FALSE)
 #' @references
 #' Kuijjer, M.L., Tung, M., Yuan, G., Quackenbush, J. and Glass, K., 2015. 
 #' Estimating sample-specific regulatory networks. arXiv preprint arXiv:1505.06440.
-lioness <- function(motif,expr,ppi=NULL, network.inference.method, ...){
-    N <- ncol(expr)
+lioness = function(expr, motif = NULL, ppi = NULL, network.inference.method = "panda", ...){
+  N <- ncol(expr)
+  if(network.inference.method == "panda")
+  {
     fullnet <- panda(motif, expr, ppi, ...)
-    lapply(seq_len(N), function(i){
-        print(paste("Computing network for sample ",i))
-        N*fullnet@regNet - (N-1)* panda(motif, expr[,-i], ppi, ...)@regNet
-    })
+    return(lapply(seq_len(N), function(i) {
+      print(paste("Computing network for sample ", i))
+      N * fullnet@regNet - (N - 1) * panda(motif, expr[, -i], 
+                                           ppi, ...)@regNet
+    }))
+  }
+  if(network.inference.method == "pearson")
+  {
+    fullnet <- cor(t(expr))
+
+    return(lapply(seq_len(N), function(i) {
+      print(paste("Computing network for sample ", i))
+      N * fullnet - (N - 1) * cor(t(expr[,-i]))
+    }))
+  }
+  if(!(network.inference.method %in% c("panda","pearson")))
+  {
+    stop(paste(c("The method",network.inference.method,"is not yet supported."),collapse=" "))
+    geterrmessage()
+  }
 }

README.md

@@ -162,7 +162,7 @@ devtools::check()
 ```
 
 The master branch on github should always be in good shape, so please to pull request to the **devel** branch.
-If the contribution is specific to pandaR, please contribute to its seperate GitHub page by [pull request](https://github.com/jnpaulson/pandaR). 
+If the contribution is specific to pandaR, please contribute to its separate GitHub page by [pull request](https://github.com/jnpaulson/pandaR). 
 
 ## License
 The software is free and is licensed under the GNU General License v3.0, see the file [LICENSE](LICENSE) for details.

tests/testthat/test-lioness.R

@@ -42,5 +42,55 @@ test_that("lionessPy() function works", {
 
 })
 
+test_that("lioness() function works for network.inference.method = 'panda'", {
+
+  print("[LIONESS] lioness() This test needs to be implemented!")
+  # expect_equal(1,-1) # dummy test failure
+})
+
+test_that("lioness() function works for network.inference.method = 'pearson'", {
+
+  # create toy data
+  x1 = c(1,1,2,3)
+  x2 = c(1,2,3,4)
+  X = matrix(c(x1,x2),ncol=2,byrow=F)
+
+  # extract correlation networks needed for LIONESS
+  fullNet = cor(X)
+  subNet1 = cor(X[-1,])
+  subNet2 = cor(X[-2,])
+  subNet3 = cor(X[-3,])
+  subNet4 = cor(X[-4,])
+
+  # construct LIONESS sample-specific correlation networks manually
+  N = 4
+  ssNet1 = N*(fullNet-subNet1) + subNet1
+  ssNet2 = N*(fullNet-subNet2) + subNet2
+  ssNet3 = N*(fullNet-subNet3) + subNet3
+  ssNet4 = N*(fullNet-subNet4) + subNet4
+
+  # run LIONESS on the toy data
+  lionessNets = lioness(expr=t(X),network.inference.method="pearson")
+
+  # assert equal
+  # there is no file I/O or rounding here
+  # so differences should be machine zero in R
+  expect_equal(lionessNets[[1]],ssNet1,tolerance=1e-15) 
+  expect_equal(lionessNets[[2]],ssNet2,tolerance=1e-15) 
+  expect_equal(lionessNets[[3]],ssNet3,tolerance=1e-15) 
+  expect_equal(lionessNets[[4]],ssNet4,tolerance=1e-15) 
+
+})
+
+test_that("lioness() function throws appropriate error for undefined network.inference.method", {
 
+  # create toy data
+  x1 = c(1,1,2,3)
+  x2 = c(1,2,3,4)
+  X = matrix(c(x1,x2),ncol=2,byrow=F)
+
+  # run LIONESS on the toy data w/undefined method
+  expect_error(lioness(expr=t(X),network.inference.method="divination"))
+
+})