library("leiden") library("reticulate") library("igraph") context("running Leiden on unweighted objects") set.seed(9000) modules <- reticulate::py_module_available("leidenalg") && reticulate::py_module_available("igraph") skip_if_no_python <- function() { if (!modules) testthat::skip("leidenalg not available for testing") } library("leiden") library("igraph") set.seed(9000) mat <- round(matrix(runif(100, max = 1), 10, 10, ), 0) sp.mat <- as(mat, Class = "dgCMatrix") graph <- igraph::graph_from_adjacency_matrix(mat, weighted = NULL) test_that("run with unweighted dense matrix", { skip_if_no_python() set.seed(9000) <<<<<<< HEAD mat <- round(matrix(runif(100, max = 1), 10, 10, ), 0) mat part_mat0 <- leiden::leiden(mat, seed = 9000) expect_equal(part_mat0, c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) }) test_that("run with unweighted sparse matrix", { skip_if_no_python() sp.mat <- as(mat, Class = "dgCMatrix") part_sp.mat0 <- leiden::leiden(sp.mat, seed = 9000) expect_equal(part_sp.mat0, c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) }) test_that("run with unweighted graph object", { skip_if_no_python() ======= mat <- round(matrix(runif(100, max = 1), 10, 10), 0) mat part_mat0 <- leiden::leiden(mat, seed = 9000, resolution_parameter = 0.8) expect_equal(table(part_mat0), structure(c(`1` = 5L, `2` = 5L), .Dim = 2L, .Dimnames = list( part_mat0 = c("1", "2")), class = "table")) expect_length(object = unique(part_mat0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 1)]), n = 1) expect_length(object = unique(part_mat0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 2)]), n = 1) }) set.seed(9000) test_that("run with unweighted sparse matrix", { skip_if_no_python() set.seed(9000) sp.mat <- as(mat, Class = "dgCMatrix") part_sp.mat0 <- leiden::leiden(sp.mat, seed = 9000) expect_equal(table(part_sp.mat0), structure(c(`1` = 5L, `2` = 5L), .Dim = 2L, .Dimnames = list( part_sp.mat0 = c("1", "2")), class = "table")) expect_length(object = unique(part_sp.mat0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 1)]), n = 1) expect_length(object = unique(part_sp.mat0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 2)]), n = 1) }) set.seed(9000) test_that("run with unweighted graph object", { skip_if_no_python() set.seed(9000) >>>>>>> 7d904bd98ec75ea8cc9bda7b50896458b3c26522 graph <- igraph::graph_from_adjacency_matrix(mat, weighted = NULL) graph part_graph0 <- leiden::leiden(graph, seed = 9000) part_graph0 <<<<<<< HEAD expect_equal(part_graph0, c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) }) test_that("same output with different input class", { skip_if_no_python() part_mat0 <- leiden::leiden(mat, seed = 9000) part_graph0 <- leiden::leiden(graph, seed = 9000) expect_equivalent(part_mat0, c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) expect_equivalent(part_graph0, c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) expect_equivalent(part_mat0, part_graph0) expect_equal(sum(diag(table(part_mat0, part_graph0))), length(part_mat0)) expect_equal(sum(diag(table(part_mat0, part_graph0))), nrow(mat)) expect_equal(sum(diag(table(part_mat0, part_graph0))), length(V(graph))) ======= expect_equal(table(part_graph0), structure(c(`1` = 5L, `2` = 5L), .Dim = 2L, .Dimnames = list( part_graph0 = c("1", "2")), class = "table")) expect_length(object = unique(part_graph0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 1)]), n = 1) expect_length(object = unique(part_graph0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 2)]), n = 1) }) set.seed(9000) test_that("same output with different input class", { skip_if_no_python() set.seed(9000) part_mat0 <- leiden::leiden(mat, seed = 9000, resolution_parameter = 0.8) set.seed(9000) part_graph0 <- leiden::leiden(graph, seed = 9000, resolution_parameter = 0.8) expect_length(object = unique(part_mat0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 1)]), n = 1) expect_length(object = unique(part_mat0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 2)]), n = 1) expect_length(object = unique(part_graph0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 1)]), n = 1) expect_length(object = unique(part_graph0[which(c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1) == 2)]), n = 1) expect_true(all(part_mat0 + part_graph0 == 3) || all(part_mat0 == part_graph0)) >>>>>>> 7d904bd98ec75ea8cc9bda7b50896458b3c26522 }) # # Test Seurat (development version) # devtools::install_github("TomKellyGenetics/seurat", ref = "pr", quiet = TRUE) # devtools::install_github("satijalab/seurat", ref = "develop", quiet = TRUE) # library("Seurat") # rownames(mat) <- colnames(mat) <- 1:10 # obj <- Seurat::CreateSeuratObject(mat) # obj <- Seurat::FindVariableFeatures(obj) # obj <- Seurat::ScaleData(obj) # obj <- Seurat::RunPCA(obj, dim = 1:3) # obj <- Seurat::FindNeighbors(obj, dim = 1:3,) # obj@graphs$RNA_nn <- Seurat:::as.Graph.matrix(mat) # obj@graphs$RNA_snn <- Seurat:::as.Graph.matrix(mat) # obj <- Seurat::FindClusters(obj, algorithm = "Leiden", method = "matrix", random.seed = 9000, weights = mat[mat !=0], resolution = 1) # part_mat <- obj@active.ident # obj <- Seurat::FindClusters(obj, algorithm = "Leiden", method = "igraph", random.seed = 9000, weights = mat[mat !=0], resolution = 1) # part_graph <- obj@active.ident # table(part_mat, part_graph) # all(part_mat == c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) # all(part_graph == c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) # sum(diag(table(part_mat, part_graph))) == length(part_mat) # sum(diag(table(part_mat, part_graph))) == nrow(mat) # sum(diag(table(part_mat, part_graph))) == length(V(graph)) context("running Leiden on weighted objects") set.seed(9000) mat <- round(matrix(runif(100, max = 3), 10, 10), 0) sp.mat <- as(mat, Class = "dgCMatrix") graph <- igraph::graph_from_adjacency_matrix(mat, weighted = NULL) test_that("run with weighted dense matrix", { skip_if_no_python() set.seed(9000) mat <- round(matrix(runif(100, max = 3), 10, 10, ), 0) mat #passing weights part_mat1 <- leiden::leiden(mat, seed = 9000, weights = t(mat)[t(mat) != 0]) expect_equal(part_mat1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) #detecting weights from matrix part_mat1 <- leiden::leiden(mat, seed = 9000) expect_equal(part_mat1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) }) test_that("run with weighted sparse matrix", { skip_if_no_python() set.seed(9000) sp.mat <- as(mat, Class = "dgCMatrix") #passing weights part_sp.mat1 <- leiden::leiden(sp.mat, seed = 9000, weights = mat[mat != 0]) expect_equal(part_sp.mat1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) #detecting weights from matrix part_sp.mat1 <- leiden::leiden(sp.mat, seed = 9000) expect_equal(part_sp.mat1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) }) test_that("run with weighted graph object", { skip_if_no_python() set.seed(9000) graph <- igraph::graph_from_adjacency_matrix(mat, weighted = NULL) graph #passing weights part_graph1 <- leiden::leiden(graph, seed = 9000, weights = edge.attributes(graph)$weight) expect_equal(part_graph1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) #detecting weights from matrix part_graph1 <- leiden::leiden(graph, seed = 9000) expect_equal(part_graph1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) }) test_that("same output with different input class", { skip_if_no_python() set.seed(9000) part_mat1 <- leiden::leiden(mat, seed = 9000) part_graph1 <- leiden::leiden(graph, seed = 9000) expect_equivalent(part_mat1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) expect_equivalent(part_graph1, c(2, 1, 1, 1, 2, 1, 1, 2, 2, 2)) expect_equivalent(part_mat1, part_graph1) expect_equal(sum(diag(table(part_mat1, part_graph1))), length(part_mat1)) expect_equal(sum(diag(table(part_mat1, part_graph1))), nrow(mat)) expect_equal(sum(diag(table(part_mat1, part_graph1))), length(V(graph))) }) # # Test Seurat (development version) # devtools::install_github("TomKellyGenetics/seurat", ref = "pr", quiet = TRUE) # devtools::install_github("satijalab/seurat", ref = "develop", quiet = TRUE) # library("Seurat") # rownames(mat) <- colnames(mat) <-1:10 # obj <- Seurat::CreateSeuratObject(mat) # obj <- Seurat::FindVariableFeatures(obj) # obj <- Seurat::ScaleData(obj) # obj <- Seurat::RunPCA(obj, dim = 1:3) # obj <- Seurat::FindNeighbors(obj, dim = 1:3,) # obj@graphs$RNA_snn <- Seurat:::as.Graph.matrix(mat) # weights <- mat[mat !=0] # obj <- Seurat::FindClusters(obj, algorithm = "Leiden", method = "matrix", random.seed = 9000, weights = weights, resolution = 1) # part_mat2 <- obj@active.ident # obj <- Seurat::FindClusters(obj, algorithm = "Leiden", method = "igraph", random.seed = 9000, weights = weights, resolution = 1) # part_graph2 <- obj@active.ident # table(part_mat, part_graph) # all(part_mat2 == c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) # all(part_graph2 == c(1, 2, 2, 2, 1, 2, 2, 1, 1, 1)) # sum(diag(table(part_mat2, part_graph2))) == length(part_mat) # sum(diag(table(part_mat2, part_graph2))) == nrow(mat) # sum(diag(table(part_mat2, part_graph2))) == length(V(graph))