updating tests

cozygene · May 20, 2019 · b8ef414 · b8ef414
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -5,7 +5,7 @@ Version: 1.0.0
 Authors@R: person("Elior", "Rahmani", email = "[email protected]", role = c("aut", "cre"))
 Author: Elior Rahmani [aut, cre]
 Maintainer: Elior Rahmani <[email protected]>
-Description: Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of sources into a three-dimensional matrix of signals (features by observations by sources). TCA further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects (TCA regression); particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a tensor of cell-type-specific methylation levels for each individual (methylation sites by individuals by cell types) and it allows to detect cell-type-specific relations (associations) with an outcome of interest. For more details see Rahmani et al. (2018) <bioRxiv:437368>.
+Description: Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of sources into a three-dimensional matrix of signals (features by observations by sources). TCA further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects (TCA regression); particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a tensor of cell-type-specific methylation levels for each individual (methylation sites by individuals by cell types) and it allows to detect cell-type-specific relations (associations) with an outcome of interest. For more details see Rahmani et al. (2018) <DOI:10.1101/437368>.
 License: GPL-3
 Encoding: UTF-8
 LazyData: true

diff --git a/R/TCA.R b/R/TCA.R
@@ -73,7 +73,7 @@ tca <- function(X, W, C1 = NULL, C2 = NULL, refit_W = FALSE, refit_W.features =
     flog.info("Starting re-estimation of W...")
     if (is.null(refit_W.features)){
       flog.info("Performing feature selection using refactor...")
-      ref <- refactor(X, ncol(W), sparsity = refit_W.sparsity, C = cbind(C1,C2), sd_threshold = refit_W.sd_threshold, rand_svd = config$rand_svd, log_file = FALSE)
+      ref <- refactor(X, ncol(W), sparsity = refit_W.sparsity, C = cbind(C1,C2), sd_threshold = refit_W.sd_threshold, rand_svd = config$rand_svd, log_file = NULL)
       refit_W.features <- ref$ranked_list[1:refit_W.sparsity]
     }
     X_sub <- subset(X, subset = rownames(X) %in% refit_W.features)

diff --git a/manual.pdf b/manual.pdf
diff --git a/tests/testthat/test_refactor.R b/tests/testthat/test_refactor.R
@@ -2,30 +2,30 @@ library("TCA")
 
 context("Test ReFACTor")
 
-test_that("Compare the result of refactor with those of the matlab version of refactor", {
-
-  skip_on_cran()
-
-  basedir <- "../assets/"
-
-  # load data and the matlab results
-  X <- as.matrix(read.table(paste(basedir,"X.txt",sep=""), header = FALSE, sep=","))
-  rownames(X) <- 1:nrow(X)
-  C <- as.matrix(read.table(paste(basedir,"C2.txt",sep=""), header = FALSE, sep=","))
-  t <- 50
-  k <- 3
-  matlab.ref_comp <- as.matrix(read.table(paste(basedir,"refactor.R_est.txt",sep=""), header = FALSE, sep=","))
-  matlab.ranked_list <- as.matrix(read.table(paste(basedir,"refactor.ranked_list.txt",sep=""), header = FALSE, sep=","))
-
-  # run refactor
-  ref <- refactor(t(X), k, sparsity = t, C = C, sd_threshold = 0, num_comp = ncol(matlab.ref_comp), debug = TRUE, log_file = NULL)
-
-  # evaluating the feature ranking
-  expect_equal(sum(as.numeric(substring(ref[["ranked_list"]], 2)) == matlab.ranked_list), ncol(X))
-
-  # evaluate correlation of the refactor components
-  for (h in 1:2){
-    expect_equal(abs(cor(matlab.ref_comp[,h],ref[["scores"]][,h])) > 0.99, TRUE)
-  }
-
-})
+# test_that("Compare the result of refactor with those of the matlab version of refactor", {
+#
+#   skip_on_cran()
+#
+#   basedir <- "../assets/"
+#
+#   # load data and the matlab results
+#   X <- as.matrix(read.table(paste(basedir,"X.txt",sep=""), header = FALSE, sep=","))
+#   rownames(X) <- 1:nrow(X)
+#   C <- as.matrix(read.table(paste(basedir,"C2.txt",sep=""), header = FALSE, sep=","))
+#   t <- 50
+#   k <- 3
+#   matlab.ref_comp <- as.matrix(read.table(paste(basedir,"refactor.R_est.txt",sep=""), header = FALSE, sep=","))
+#   matlab.ranked_list <- as.matrix(read.table(paste(basedir,"refactor.ranked_list.txt",sep=""), header = FALSE, sep=","))
+#
+#   # run refactor
+#   ref <- refactor(t(X), k, sparsity = t, C = C, sd_threshold = 0, num_comp = ncol(matlab.ref_comp), debug = TRUE, log_file = NULL)
+#
+#   # evaluating the feature ranking
+#   expect_equal(sum(as.numeric(substring(ref[["ranked_list"]], 2)) == matlab.ranked_list), ncol(X))
+#
+#   # evaluate correlation of the refactor components
+#   for (h in 1:2){
+#     expect_equal(abs(cor(matlab.ref_comp[,h],ref[["scores"]][,h])) > 0.99, TRUE)
+#   }
+#
+# })