Update non-Actions regression target values for VirtualBox.

tests/testthat/test-alignment.R

@@ -86,7 +86,7 @@ test_that("Alignment works on synthetic data", {
   batched_cds = cluster_cells(batched_cds, k=10, reduction_method="PCA", resolution=1e-3)
   plot_cells(batched_cds, reduction_method="PCA", color_cells_by="partition")
 
-  expect_equal(length(unique(partitions(batched_cds, reduction_method="PCA"))), 5)
+  expect_equal(length(unique(partitions(batched_cds, reduction_method="PCA"))), 4)
 
   #batched_cds = preprocess_cds(batched_cds, num_dim=2, residual_model_formula_str="~cell_type")
 
@@ -106,7 +106,7 @@ test_that("Alignment works on synthetic data", {
   batched_cds = cluster_cells(batched_cds, k=10, reduction_method="Aligned", resolution=1e-3)
   plot_cells(batched_cds, reduction_method="Aligned", color_cells_by="batch")
 
-  expect_equal(length(unique(partitions(batched_cds, reduction_method="Aligned"))), 10)
+  expect_equal(length(unique(partitions(batched_cds, reduction_method="Aligned"))), 9)
 
 
     #plot_cells(batched_cds, reduction_method="PCA", color_cells_by="cluster")

tests/testthat/test-cluster_cells.R

@@ -56,9 +56,8 @@ test_that("cluster_cells works", {
   expect_equal(length(cds@clusters[["UMAP"]]), 3)
   expect_equal(length(cds@clusters[["UMAP"]]$cluster_result$optim_res$membership),
                nrow(colData(cds)))
-  expect_equal(cds@clusters[["UMAP"]]$cluster_result$optim_res$membership[[1]],
-  3)
-  expect_equal(length(unique(clusters(cds, reduction_method = "UMAP"))), 17)
+  expect_equal(cds@clusters[["UMAP"]]$cluster_result$optim_res$membership[[1]], 16)
+  expect_equal(length(unique(clusters(cds, reduction_method = "UMAP"))), 20)
 
   ## louvain
   cds <- cluster_cells(cds, cluster_method = "louvain", random_seed = 100)
@@ -68,7 +67,7 @@ test_that("cluster_cells works", {
                nrow(colData(cds)))
   expect_equal(cds@clusters[["UMAP"]]$cluster_result$optim_res$membership[[1]],
                1)
-  expect_equal(length(unique(clusters(cds, reduction_method = "UMAP"))), 8)
+  expect_equal(length(unique(clusters(cds, reduction_method = "UMAP"))), 11)
 
   # non-standard opts
   cds <- cluster_cells(cds, cluster_method = "louvain", k=22, weight = TRUE,
@@ -80,7 +79,7 @@ test_that("cluster_cells works", {
                nrow(colData(cds)))
   expect_equal(cds@clusters[["UMAP"]]$cluster_result$optim_res$membership[[1]],
                1)
-  expect_equal(length(unique(clusters(cds, reduction_method = "UMAP"))), 12)
+  expect_equal(length(unique(clusters(cds, reduction_method = "UMAP"))), 13)
 
   ### tSNE
   ##leiden
@@ -102,8 +101,8 @@ test_that("cluster_cells works", {
   expect_equal(length(cds@clusters[["tSNE"]]$cluster_result$optim_res$membership),
                nrow(colData(cds)))
   expect_equal(cds@clusters[["tSNE"]]$cluster_result$optim_res$membership[[1]],
-               6)
-  expect_equal(length(unique(clusters(cds, reduction_method = "tSNE"))), 21)
+               17)
+  expect_equal(length(unique(clusters(cds, reduction_method = "tSNE"))), 22)
 
   ### PCA
 

tests/testthat/test-fit_models.R

@@ -9,6 +9,7 @@ skip_not_travis <- function ()
   skip("Not on Travis")
 }
 
+library(pryr)
 
 cds <- load_a549()
 #cds <- estimate_size_factors(cds)
@@ -321,7 +322,7 @@ test_that("fit_models() can handle cluster in model formulae",{
                                  expression_family = "quasipoisson")
   expect_equal(pos_ctrl_gene_fit$status[[1]], "OK")
   pos_ctrl_coefs = coefficient_table(pos_ctrl_gene_fit)
-  expect_equal(pos_ctrl_coefs$estimate[2], -0.0962, tolerance=1e-3)
+  expect_equal(pos_ctrl_coefs$estimate[2], -0.99, tolerance=1e-2)
 })
 
 

tests/testthat/test-graph_test.R

@@ -42,15 +42,15 @@ test_that("test graph_test returns Dex-dependent genes",{
   pos_ctrl_gene = test_cds[rowData(cds)$gene_short_name == "che-1",]
   pr_test_res = graph_test(pos_ctrl_gene)
   expect_equal(pr_test_res$status[1], "OK")
-  expect_equal(pr_test_res$morans_I, 0.65, tolerance=1e-2)
+  expect_equal(pr_test_res$morans_I, 0.661, tolerance=1e-2)
   expect_equal(pr_test_res$morans_test_statistic, 204.72, tolerance=1e-1)
   expect_lt(pr_test_res$p_value[1], 0.05)
 
   neg_ctrl_gene = test_cds[rowData(cds)$gene_short_name == "R02D3.1",]
   pr_test_res = graph_test(neg_ctrl_gene)
   expect_equal(pr_test_res$status[1], "OK")
-  expect_equal(pr_test_res$morans_I, -0.00264, tolerance=1e-4)
-  expect_equal(pr_test_res$morans_test_statistic, -0.731, tolerance=1e-2)
+  expect_equal(pr_test_res$morans_I, -0.000752, tolerance=1e-5)
+  expect_equal(pr_test_res$morans_test_statistic, -0.173, tolerance=1e-2)
   expect_gt(pr_test_res$p_value[1], 0.05)
 })
 

tests/testthat/test-io.R

@@ -280,7 +280,7 @@ test_that("save_transform_models and load_transform_models", {
   # check UMAP reduced dims matrix and nearest neighbors
   expect_equivalent(ncol(reducedDims(cds)[['UMAP']]), 2)
   expect_equivalent(nrow(reducedDims(cds)[['UMAP']]), 500)
-  expect_equivalent(reducedDims(cds)[['UMAP']][[1,1]], 1.96, tol=1e-2)
+  expect_equivalent(reducedDims(cds)[['UMAP']][[1,1]], 2.7, tol=1e-1)
   nn_res <- search_nn_index(query_matrix=reducedDims(cds)[['UMAP']], nn_index=get_cds_nn_index(cds, reduction_method='UMAP', nn_method=nn_method, verbose=FALSE), k=5, nn_control=list(method=nn_method, metric='euclidean', n_trees=50))
   expect_equivalent(nn_res[['nn.idx']][[1]], 1)
   expect_equivalent(nn_res[['nn.dists']][[1]], 0)
@@ -349,7 +349,7 @@ test_that("save_monocle_objects and load_monocle_objects", {
   # check UMAP reduced dims matrix and nearest neighbors
   expect_equivalent(ncol(reducedDims(cds)[['UMAP']]), 2)
   expect_equivalent(nrow(reducedDims(cds)[['UMAP']]), 500)
-  expect_equivalent(reducedDims(cds)[['UMAP']][[1,1]], 1.96, tol=1e-2)
+  expect_equivalent(reducedDims(cds)[['UMAP']][[1,1]], 2.7, tol=1e-1)
   nn_res <- search_nn_index(query_matrix=reducedDims(cds)[['UMAP']], nn_index=get_cds_nn_index(cds, reduction_method='UMAP', nn_method=nn_method), k=5, nn_control=list(method=nn_method, metric='euclidean', n_trees=50))
   expect_equivalent(nn_res[['nn.idx']][[1]], 1)
   expect_equivalent(nn_res[['nn.dists']][[1]], 0)

tests/testthat/test-learn_graph.R

@@ -33,32 +33,32 @@ test_that("learn_graph stays the same", {
   skip_on_travis()
   cds <- learn_graph(cds)
   expect_is(principal_graph(cds)[["UMAP"]], "igraph")
-  expect_equal(length(principal_graph(cds)[["UMAP"]]), 63)
-  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "3")
+  expect_equal(length(principal_graph(cds)[["UMAP"]]), 33)
+  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "24")
 
   # Force partition
   temp <- rep(c(1,2), length.out=length(partitions(cds)))
   names(temp) <- names(partitions(cds))
   cds@clusters[["UMAP"]]$partitions <- temp
   cds <- learn_graph(cds, use_partition = FALSE)
   expect_is(principal_graph(cds)[["UMAP"]], "igraph")
-  expect_equal(length(principal_graph(cds)[["UMAP"]]), 63)
-  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "3")
+  expect_equal(length(principal_graph(cds)[["UMAP"]]), 33)
+  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "24")
 
   cds <- learn_graph(cds)
   expect_is(principal_graph(cds)[["UMAP"]], "igraph")
-  expect_equal(length(principal_graph(cds)[["UMAP"]]), 104)
-  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "24")
+  expect_equal(length(principal_graph(cds)[["UMAP"]]), 74)
+  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "36")
 
   cds <- learn_graph(cds, close_loop = TRUE)
   expect_is(principal_graph(cds)[["UMAP"]], "igraph")
-  expect_equal(length(principal_graph(cds)[["UMAP"]]), 104)
-  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "24")
+  expect_equal(length(principal_graph(cds)[["UMAP"]]), 74)
+  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "36")
 
   cds <- learn_graph(cds, learn_graph_control = list(prune_graph = FALSE))
   expect_is(principal_graph(cds)[["UMAP"]], "igraph")
-  expect_equal(length(principal_graph(cds)[["UMAP"]]), 216)
-  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "46")
+  expect_equal(length(principal_graph(cds)[["UMAP"]]), 144)
+  expect_equal(as.character(principal_graph(cds)[["UMAP"]][[1]]$Y_1[[1]]), "8")
 })
 
 

tests/testthat/test-order_cells.R

@@ -36,21 +36,21 @@ cds <- learn_graph(cds)
 test_that("order_cells works", {
   skip_on_travis()
   cds <- order_cells(cds, root_pr_nodes = "Y_1")
-  expect_equal(max(pseudotime(cds)), 16.5, tol = 1e-1)
+  expect_equal(max(pseudotime(cds)), 11.9, tol = 1e-1)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]),  0.111, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]),  0.0538, tol = 1e-3)
   cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_10"))
-  expect_equal(max(pseudotime(cds)), 9.08, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 6.34, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 0.111, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 0.0538, tol = 1e-3)
   cds <- order_cells(cds, root_cells = "G07_B02_RT_587")
-  expect_equal(max(pseudotime(cds)), 14.9, tol = 1e-1)
+  expect_equal(max(pseudotime(cds)), 13.2, tol = 1e-1)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 2.18, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 1.5, tol = 1e-1)
   cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598"))
-  expect_equal(max(pseudotime(cds)), 8.89, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 7.26, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 2.18, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 1.5, tol = 1e-1)
 })
 
 cds <- reduce_dimension(cds, max_components = 3, umap.fast_sgd=FALSE)
@@ -62,19 +62,19 @@ test_that("order_cells works 3d", {
   cds <- order_cells(cds, root_pr_nodes = "Y_1")
   expect_equal(max(pseudotime(cds)), 10.0, tol = 1e-1)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]),  9.67e-7, tol = 1e-8)
+  expect_equal(as.numeric(pseudotime(cds)[1]),  0.664, tol = 1e-3)
   cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_10"))
-  expect_equal(max(pseudotime(cds)),  7.69, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)),  8.64, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]),  9.67e-7, tol = 1e-8)
+  expect_equal(as.numeric(pseudotime(cds)[1]),  0.664, tol = 1e-3)
   cds <- order_cells(cds, root_cells = "G07_B02_RT_587")
-  expect_equal(max(pseudotime(cds)), 13.4, tol = 1e-1)
+  expect_equal(max(pseudotime(cds)), 10.4, tol = 1e-1)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 3.17, tol = 1e-1)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3)
   cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598"))
   expect_equal(max(pseudotime(cds)), 10.2, tol = 1e-1)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 3.17, tol = 1e-1)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 0.664, tol = 1e-3)
 })
 
 cds <- cluster_cells(cds, random_seed = 100)
@@ -83,21 +83,21 @@ cds <- learn_graph(cds)
 test_that("order_cells works leiden", {
   skip_on_travis()
   cds <- order_cells(cds, root_pr_nodes = "Y_1")
-  expect_equal(max(pseudotime(cds)), 6.35, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 9.94, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 1.94, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 4.49, tol = 1e-2)
   cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_2"))
-  expect_equal(max(pseudotime(cds)), 3.76, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 4.72, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 1.94, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 4.35, tol = 1e-2)
   cds <- order_cells(cds, root_cells = "G07_B02_RT_587")
-  expect_equal(max(pseudotime(cds)), 6.35, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 8.03, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 1.94 , tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 0.121 , tol = 1e-3)
   cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598"))
-  expect_equal(max(pseudotime(cds)), 4.15, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 5.85, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 1.94 , tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 0.121 , tol = 1e-3)
 })
 
 cds <- reduce_dimension(cds, max_components = 3, umap.fast_sgd=FALSE)
@@ -107,21 +107,21 @@ cds <- learn_graph(cds)
 test_that("order_cells works leiden 3d", {
   skip_on_travis()
   cds <- order_cells(cds, root_pr_nodes = "Y_1")
-  expect_equal(max(pseudotime(cds)), 6.35, tol = 1e-1)
+  expect_equal(max(pseudotime(cds)), 9.94, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]),  1.94, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]),  4.49, tol = 1e-2)
   cds <- order_cells(cds, root_pr_nodes = c("Y_1", "Y_2"))
-  expect_equal(max(pseudotime(cds)), 3.76, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 4.72, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]),  1.94, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]),  4.35, tol = 1e-2)
   cds <- order_cells(cds, root_cells = "G07_B02_RT_587")
-  expect_equal(max(pseudotime(cds)), 6.35, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 8.03, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 1.94, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3)
   cds <- order_cells(cds, root_cells = c("G07_B02_RT_587", "F06_A01_RT_598"))
-  expect_equal(max(pseudotime(cds)), 4.15, tol = 1e-2)
+  expect_equal(max(pseudotime(cds)), 5.85, tol = 1e-2)
   expect_equal(min(pseudotime(cds)), 0)
-  expect_equal(as.numeric(pseudotime(cds)[1]), 1.94, tol = 1e-2)
+  expect_equal(as.numeric(pseudotime(cds)[1]), 0.121, tol = 1e-3)
 })
 
 

tests/testthat/test-reduce_dimension.R

@@ -27,25 +27,25 @@ test_that("reduce_dimension runs", {
   cds <- reduce_dimension(cds, umap.fast_sgd=FALSE, cores=1)
   expect_equal(nrow(reducedDims(cds)$UMAP), nrow(colData(cds)))
   expect_equal(ncol(reducedDims(cds)$UMAP), 2)
-  expect_equal(as.numeric(reducedDims(cds)$UMAP[1,1]), -2.86,
+  expect_equal(as.numeric(reducedDims(cds)$UMAP[1,1]), 2.26,
                tolerance = 1e-2)
 
   cds <- reduce_dimension(cds, max_components = 3, umap.fast_sgd=FALSE, cores=1, reduction_method = "UMAP")
   expect_equal(nrow(reducedDims(cds)$UMAP), nrow(colData(cds)))
   expect_equal(ncol(reducedDims(cds)$UMAP), 3)
-  expect_equal(as.numeric(reducedDims(cds)$UMAP[1,1]), 1.69,
-               tolerance = 1e-2)
+  expect_equal(as.numeric(reducedDims(cds)$UMAP[1,1]), 0.265,
+               tolerance = 1e-3)
 
   cds <- reduce_dimension(cds, reduction_method = "tSNE")
   expect_equal(nrow(reducedDims(cds)$tSNE), nrow(colData(cds)))
   expect_equal(ncol(reducedDims(cds)$tSNE), 2)
-  expect_equal(as.numeric(reducedDims(cds)$tSNE[1,1]), -1.16,
+  expect_equal(as.numeric(reducedDims(cds)$tSNE[1,1]), -3.74,
                tolerance = 1e-2)
 
   cds <- reduce_dimension(cds,  max_components = 3, reduction_method = "tSNE")
   expect_equal(nrow(reducedDims(cds)$tSNE), nrow(colData(cds)))
   expect_equal(ncol(reducedDims(cds)$tSNE), 3)
-  expect_equal(as.numeric(reducedDims(cds)$tSNE[1,1]), -4.29,
+  expect_equal(as.numeric(reducedDims(cds)$tSNE[1,1]), -5.29,
                tolerance = 1e-2)
 
   cds <- reduce_dimension(cds, reduction_method = "PCA")
@@ -57,14 +57,14 @@ test_that("reduce_dimension runs", {
   cds <- reduce_dimension(cds, umap.fast_sgd=FALSE, cores=1, preprocess_method = "LSI")
   expect_equal(nrow(reducedDims(cds)$UMAP), nrow(colData(cds)))
   expect_equal(ncol(reducedDims(cds)$UMAP), 2)
-  expect_equal(as.numeric(reducedDims(cds)$UMAP[1,1]), -0.163,
-               tolerance = 1e-3)
+  expect_equal(as.numeric(reducedDims(cds)$UMAP[1,1]), -1.21,
+               tolerance = 1e-2)
 
   cds <- reduce_dimension(cds, reduction_method = "tSNE", preprocess_method = "LSI")
   expect_equal(nrow(reducedDims(cds)$tSNE), nrow(colData(cds)))
   expect_equal(ncol(reducedDims(cds)$tSNE), 2)
-  expect_equal(as.numeric(reducedDims(cds)$tSNE[1,1]), -2.15,
-               tolerance = 1e-2)
+  expect_equal(as.numeric(reducedDims(cds)$tSNE[1,1]), 0.738,
+               tolerance = 1e-3)
 
   # check model
   set.seed(100)
@@ -78,7 +78,7 @@ test_that("reduce_dimension runs", {
   expect_equal(cds@reduce_dim_aux[['UMAP']][['model']][['umap_min_dist']], 0.1, tol=1e-1)
   expect_equal(cds@reduce_dim_aux[['UMAP']][['model']][['umap_n_neighbors']], 15, tol=1e1)
   expect_equal(cds@reduce_dim_aux[['UMAP']][['model']][['umap_fast_sgd']], FALSE)
-  expect_equal(cds@reduce_dim_aux[['UMAP']][['model']][['umap_model']][['embedding']][[1,1]], -1.80, tol=1e-1)
+  expect_equal(cds@reduce_dim_aux[['UMAP']][['model']][['umap_model']][['embedding']][[1,1]], 2.03, tol=1e-2)
   expect_equal(cds@reduce_dim_aux[['UMAP']][['model']][['umap_model']][['n_neighbors']][[1]], 15, tol=1e1)