comment sparse-dense conv, update tests

R/find_genes_of_interest.R

@@ -92,11 +92,15 @@ find_genes_of_interest<-function(
         subset_goodGenes <- intersect(rownames(testMat),goodGenes)
         testMat <- testMat[subset_goodGenes,]
         }
-    res_kruskal<- matrixTests::row_kruskalwallis(x=as.matrix(testMat),g=region)
+
+    #we lose sparsity here, it is necessary for row tests (dunn, kruskal)
+    testMat <- as.matrix(testMat)
+
+    res_kruskal<- matrixTests::row_kruskalwallis(x=testMat,g=region)
     qq <- qvalue::qvalue(res_kruskal$pvalue,fdr.level = fdr.level,
                             pfdr = FALSE, pi0 = 1)
     res_kruskal <- cbind(res_kruskal,p.adj = qq$qvalues)
-    res_dunn_test <- row.dunn.test(in.data=as.matrix(testMat), region=region,
+    res_dunn_test <- row.dunn.test(in.data=testMat, region=region,
                                         pattern1=pattern1, pattern2=pattern2)
     rownames(res_dunn_test) <- rownames(res_kruskal)
     ind <- rownames(res_kruskal[which(res_kruskal$p.adj<fdr.level),])

tests/testthat/test-find_genes_of_interest.R

@@ -1,14 +1,19 @@
-test_that('row dunn test works with both sparse and dense matrices',{
+test_that('row dunn test works with dense matrix but not sparse',{
     dense <- matrix(rnorm(100), ncol = 10)
     sparse <- Matrix::Matrix(dense, sparse = TRUE)
 
     region <- matrix(sample(c(NA, "Interacting"), 10, replace = TRUE), ncol = 1)
     region <- factor(region)
 
+    patnames <- levels(region)[which(levels(region)!="Interacting")]
+
+    pattern1 <- patnames[1]
+    pattern2 <- patnames[2]
+
     expect_true("dgCMatrix" %in% class(sparse))
-    expect_error(row.dunn.test(sparse, region),
+    expect_error(row.dunn.test(sparse, region, pattern1, pattern2),
         "Argument 'x' must be a matrix or a vector")
 
     expect_true("matrix" %in% class(dense))
-    expect_no_error(row.dunn.test(dense, region))
+    expect_no_error(row.dunn.test(dense, region, pattern1, pattern2))
 })