tests/testthat/test-simData.R
In fionarhuang/treeclimbR: An algorithm to find optimal signal levels in a tree
test_that("simData works", {
## Generate data to use as the starting point
set.seed(1L)
y <- matrix(rnbinom(120, size = 1, mu = 10), nrow = 10)
colnames(y) <- paste("S", seq_len(12), sep = "")
rownames(y) <- tinyTree$tip.label
toy_lse <- TreeSummarizedExperiment(rowTree = tinyTree,
assays = list(counts = y))
## Check that function returns error if provided with invalid input
## -------------------------------------------------------------------------
.args <- list(tree = NULL, data = NULL, obj = NULL, assay = NULL,
scenario = "BS", from.A = NULL, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_error(do.call(simData, .args),
"(!is.null(tree) && !is.null(data)) || !is.null(obj)",
fixed = TRUE)
args <- .args
args$tree <- tinyTree
args$data <- y[seq_len(5), ]
expect_error(do.call(simData, args),
"The rownames of data do not match the leaf labels.")
args$data <- "x"
expect_error(do.call(simData, args),
"data should be a matrix")
args <- .args
args$obj <- 1
expect_error(do.call(simData, args),
"'obj' must be of class 'TreeSummarizedExperiment'")
args <- .args
args$obj <- toy_lse
args$assay <- "missing"
expect_error(do.call(simData, args),
"assay %in% SummarizedExperiment::assayNames(obj) is not TRUE",
fixed = TRUE)
args0 <- .args
args0$obj <- toy_lse
args0$assay <- "counts"
args <- args0
args$scenario <- 1
expect_error(do.call(simData, args),
"'scenario' must be of class 'character'")
args$scenario <- c("BS", "US")
expect_error(do.call(simData, args),
"'scenario' must have length 1")
args$scenario <- "missing"
expect_error(do.call(simData, args),
"'scenario' must be one of")
args <- args0
args$from.A <- "missing"
expect_error(do.call(simData, args),
"The provided from.A is not a node in the tree")
args <- args0
args$from.A <- 100
expect_error(do.call(simData, args),
"The provided from.A is not a node in the tree")
args$from.A <- TRUE
expect_error(do.call(simData, args),
"from.A must be a character or numeric value")
args <- args0
args$from.A <- 14
args$from.B <- "missing"
expect_error(do.call(simData, args),
"The provided from.B is not a node in the tree")
args$from.B <- 100
expect_error(do.call(simData, args),
"The provided from.B is not a node in the tree")
args$from.B <- TRUE
expect_error(do.call(simData, args),
"from.B must be a character or numeric value")
args <- args0
args$minTip.A <- "x"
expect_error(do.call(simData, args),
"'minTip.A' must be of class 'numeric'")
args$minTip.A <- c(1, 2)
expect_error(do.call(simData, args),
"'minTip.A' must have length 1")
args$minTip.A <- 5
args$maxTip.A <- 3
expect_error(do.call(simData, args),
"No nodes fulfill the requirements")
args <- args0
args$minTip.B <- "x"
expect_error(do.call(simData, args),
"'minTip.B' must be of class 'numeric'")
args$minTip.B <- c(1, 2)
expect_error(do.call(simData, args),
"'minTip.B' must have length 1")
args <- args0
args$minPr.A <- "x"
expect_error(do.call(simData, args),
"'minPr.A' must be of class 'numeric'")
args$minPr.A <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'minPr.A' must have length 1")
args$minPr.A <- 3
expect_error(do.call(simData, args),
"'minPr.A' must be within [0,1]", fixed = TRUE)
args <- args0
args$maxPr.A <- "x"
expect_error(do.call(simData, args),
"'maxPr.A' must be of class 'numeric'")
args$maxPr.A <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'maxPr.A' must have length 1")
args$maxPr.A <- 3
expect_error(do.call(simData, args),
"'maxPr.A' must be within [0,1]", fixed = TRUE)
args$maxPr.A <- 0
expect_error(do.call(simData, args),
"maxPr.A is lower than the minimum", fixed = TRUE)
args <- args0
args$ratio <- "x"
expect_error(do.call(simData, args),
"'ratio' must be of class 'numeric'")
args$ratio <- c(1, 2)
expect_error(do.call(simData, args),
"'ratio' must have length 1")
args$ratio <- 100
expect_error(do.call(simData, args),
"Could not find two branches which fulfill the requirement")
args$ratio <- 0.0001
expect_error(do.call(simData, args),
"Could not find two branches which fulfill the requirement")
args$ratio <- 4
args$minPr.A <- 0.8
expect_error(do.call(simData, args),
"minPr.A*ratio is above the maximum value of", fixed = TRUE)
args <- args0
args$adjB <- "x"
expect_error(do.call(simData, args),
"'adjB' must be of class 'numeric'")
args$adjB <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'adjB' must have length 1")
args$adjB <- 3
expect_error(do.call(simData, args),
"'adjB' must be within [0,1]", fixed = TRUE)
args <- args0
args$pct <- "x"
expect_error(do.call(simData, args),
"'pct' must be of class 'numeric'")
args$pct <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'pct' must have length 1")
args$pct <- 3
expect_error(do.call(simData, args),
"'pct' must be within [0,1]", fixed = TRUE)
args <- args0
args$nSam <- "x"
expect_error(do.call(simData, args),
"'nSam' must be of class 'numeric'")
args <- args0
args$mu <- "x"
expect_error(do.call(simData, args),
"'mu' must be of class 'numeric'")
args <- args0
args$size <- "x"
expect_error(do.call(simData, args),
"'size' must be of class 'numeric'")
args <- args0
args$n <- "x"
expect_error(do.call(simData, args),
"'n' must be of class 'numeric'")
args$n <- c(1, 2)
expect_error(do.call(simData, args),
"'n' must have length 1")
args <- args0
args$FUN <- "x"
expect_error(do.call(simData, args),
"'FUN' must be of class 'function'")
args <- args0
args$message <- "x"
expect_error(do.call(simData, args),
"'message' must be of class 'logical'")
args$message <- c(TRUE, FALSE)
expect_error(do.call(simData, args),
"'message' must have length 1")
## Check that function works as expected with valid input
## -------------------------------------------------------------------------
## Let function choose branches
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = NULL, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 14)
expect_equal(S4Vectors::metadata(out)$branch$B, 15)
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.97)
expect_lt(sum(abs(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]) -
metadata(out)$FC)), 2.2)
expect_equal(colSums(SummarizedExperiment::assay(out, "counts")),
rep(10000, ncol(out)),
ignore_attr = TRUE)
## Let function choose branches - with restrictions
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = NULL, from.B = NULL,
minTip.A = 1, maxTip.A = 5, minTip.B = 2, maxTip.B = 6,
minPr.A = 0.1, maxPr.A = 0.9, ratio = 2, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = c(9900, 10000),
size = NULL, n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 13)
expect_equal(S4Vectors::metadata(out)$branch$B, 15)
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.97)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
c(10000, 9900))
## Fix branches - numbers
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = 18, from.B = 19,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 100,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 18)
expect_equal(S4Vectors::metadata(out)$branch$B, 19)
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Average library size should be approximately 10000
expect_lt(abs(mean(colSums(SummarizedExperiment::assay(out, "counts"))) -
10000), 20)
## Fix branch A - numbers
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = 18, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 18)
expect_equal(S4Vectors::metadata(out)$branch$B, 13)
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.89)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
## Fix branches - node labels
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = "Node_18", from.B = "Node_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Fix branches - aliases
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = "alias_18", from.B = "alias_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Multiple matrices
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = "alias_18", from.B = "alias_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 2, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_null(SummarizedExperiment::assayNames(out))
expect_length(SummarizedExperiment::assays(out), 2)
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Scenario US; fix branches - node labels
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "US", from.A = "Node_18", from.B = "Node_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.7)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
expect_true(all(S4Vectors::metadata(out)$FC[c("t9", "t4")] < 1))
expect_true(all(S4Vectors::metadata(out)$FC[c("t10", "t1")] > 1))
expect_false(S4Vectors::metadata(out)$FC["t9"] ==
S4Vectors::metadata(out)$FC["t4"])
## Scenario US; switch branches
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "US", from.A = "Node_19", from.B = "Node_18",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_19")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_18")
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.7)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
expect_true(all(S4Vectors::metadata(out)$FC[c("t9", "t4")] < 1))
expect_true(all(S4Vectors::metadata(out)$FC[c("t10", "t1")] > 1))
expect_false(S4Vectors::metadata(out)$FC["t9"] ==
S4Vectors::metadata(out)$FC["t4"])
## Scenario SS; switch branches
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = "Node_19", from.B = "Node_18",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_19")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_18")
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
## Scenario SS; switch branches - specify size
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = "Node_19", from.B = "Node_18",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 0.5,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_19")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_18")
## Scenario SS; different branches - specify size
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = "Node_13", from.B = "Node_15",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 0.5,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_13")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_15")
## Scenario SS; set adjB
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = NULL, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = 0.8,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 0.5,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 14)
expect_equal(S4Vectors::metadata(out)$branch$B, 15)
## Test helper functions
## -------------------------------------------------------------------------
pars <- S4Vectors::metadata(parEstimate(toy_lse))$assays.par
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 0,
maxTip.B = 2, minPr.A = 0,
maxPr.A = 1, ratio = 1)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 18)
expect_equal(pk$B, 19)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 2)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t1", "t10")]), 4)) ## 19
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t4", "t9")]), 4)) ## 18
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 3,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 1)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 18)
expect_equal(pk$B, 13)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 3)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t2", "t6", "t7")]), 4)) ## 13
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t4", "t9")]), 4)) ## 18
expect_warning({
expect_error({
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 6,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 1)
}, "invalid first argument")
}, "no non-missing arguments")
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = 14, minTip.A = 0,
maxTip.A = Inf, minTip.B = 6,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 1)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 14)
expect_equal(pk$B, 15)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 6)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t1", "t4", "t5", "t8", "t9", "t10")]), 4)) ## 15
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t6", "t7")]), 4)) ## 18
expect_error({
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 0,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 10)
}, "Could not find two branches")
pk <- .infLoc(tree = rowTree(toy_lse), data = pars,
from.A = 14, from.B = 15)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 14)
expect_equal(pk$B, 15)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 6)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t1", "t4", "t5", "t8", "t9", "t10")]), 4)) ## 15
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t6", "t7")]), 4)) ## 18
})
fionarhuang/treeclimbR documentation built on Jan. 1, 2025, 9:02 p.m.
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test_that("simData works", {
## Generate data to use as the starting point
set.seed(1L)
y <- matrix(rnbinom(120, size = 1, mu = 10), nrow = 10)
colnames(y) <- paste("S", seq_len(12), sep = "")
rownames(y) <- tinyTree$tip.label
toy_lse <- TreeSummarizedExperiment(rowTree = tinyTree,
assays = list(counts = y))
## Check that function returns error if provided with invalid input
## -------------------------------------------------------------------------
.args <- list(tree = NULL, data = NULL, obj = NULL, assay = NULL,
scenario = "BS", from.A = NULL, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_error(do.call(simData, .args),
"(!is.null(tree) && !is.null(data)) || !is.null(obj)",
fixed = TRUE)
args <- .args
args$tree <- tinyTree
args$data <- y[seq_len(5), ]
expect_error(do.call(simData, args),
"The rownames of data do not match the leaf labels.")
args$data <- "x"
expect_error(do.call(simData, args),
"data should be a matrix")
args <- .args
args$obj <- 1
expect_error(do.call(simData, args),
"'obj' must be of class 'TreeSummarizedExperiment'")
args <- .args
args$obj <- toy_lse
args$assay <- "missing"
expect_error(do.call(simData, args),
"assay %in% SummarizedExperiment::assayNames(obj) is not TRUE",
fixed = TRUE)
args0 <- .args
args0$obj <- toy_lse
args0$assay <- "counts"
args <- args0
args$scenario <- 1
expect_error(do.call(simData, args),
"'scenario' must be of class 'character'")
args$scenario <- c("BS", "US")
expect_error(do.call(simData, args),
"'scenario' must have length 1")
args$scenario <- "missing"
expect_error(do.call(simData, args),
"'scenario' must be one of")
args <- args0
args$from.A <- "missing"
expect_error(do.call(simData, args),
"The provided from.A is not a node in the tree")
args <- args0
args$from.A <- 100
expect_error(do.call(simData, args),
"The provided from.A is not a node in the tree")
args$from.A <- TRUE
expect_error(do.call(simData, args),
"from.A must be a character or numeric value")
args <- args0
args$from.A <- 14
args$from.B <- "missing"
expect_error(do.call(simData, args),
"The provided from.B is not a node in the tree")
args$from.B <- 100
expect_error(do.call(simData, args),
"The provided from.B is not a node in the tree")
args$from.B <- TRUE
expect_error(do.call(simData, args),
"from.B must be a character or numeric value")
args <- args0
args$minTip.A <- "x"
expect_error(do.call(simData, args),
"'minTip.A' must be of class 'numeric'")
args$minTip.A <- c(1, 2)
expect_error(do.call(simData, args),
"'minTip.A' must have length 1")
args$minTip.A <- 5
args$maxTip.A <- 3
expect_error(do.call(simData, args),
"No nodes fulfill the requirements")
args <- args0
args$minTip.B <- "x"
expect_error(do.call(simData, args),
"'minTip.B' must be of class 'numeric'")
args$minTip.B <- c(1, 2)
expect_error(do.call(simData, args),
"'minTip.B' must have length 1")
args <- args0
args$minPr.A <- "x"
expect_error(do.call(simData, args),
"'minPr.A' must be of class 'numeric'")
args$minPr.A <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'minPr.A' must have length 1")
args$minPr.A <- 3
expect_error(do.call(simData, args),
"'minPr.A' must be within [0,1]", fixed = TRUE)
args <- args0
args$maxPr.A <- "x"
expect_error(do.call(simData, args),
"'maxPr.A' must be of class 'numeric'")
args$maxPr.A <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'maxPr.A' must have length 1")
args$maxPr.A <- 3
expect_error(do.call(simData, args),
"'maxPr.A' must be within [0,1]", fixed = TRUE)
args$maxPr.A <- 0
expect_error(do.call(simData, args),
"maxPr.A is lower than the minimum", fixed = TRUE)
args <- args0
args$ratio <- "x"
expect_error(do.call(simData, args),
"'ratio' must be of class 'numeric'")
args$ratio <- c(1, 2)
expect_error(do.call(simData, args),
"'ratio' must have length 1")
args$ratio <- 100
expect_error(do.call(simData, args),
"Could not find two branches which fulfill the requirement")
args$ratio <- 0.0001
expect_error(do.call(simData, args),
"Could not find two branches which fulfill the requirement")
args$ratio <- 4
args$minPr.A <- 0.8
expect_error(do.call(simData, args),
"minPr.A*ratio is above the maximum value of", fixed = TRUE)
args <- args0
args$adjB <- "x"
expect_error(do.call(simData, args),
"'adjB' must be of class 'numeric'")
args$adjB <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'adjB' must have length 1")
args$adjB <- 3
expect_error(do.call(simData, args),
"'adjB' must be within [0,1]", fixed = TRUE)
args <- args0
args$pct <- "x"
expect_error(do.call(simData, args),
"'pct' must be of class 'numeric'")
args$pct <- c(0.1, 0.2)
expect_error(do.call(simData, args),
"'pct' must have length 1")
args$pct <- 3
expect_error(do.call(simData, args),
"'pct' must be within [0,1]", fixed = TRUE)
args <- args0
args$nSam <- "x"
expect_error(do.call(simData, args),
"'nSam' must be of class 'numeric'")
args <- args0
args$mu <- "x"
expect_error(do.call(simData, args),
"'mu' must be of class 'numeric'")
args <- args0
args$size <- "x"
expect_error(do.call(simData, args),
"'size' must be of class 'numeric'")
args <- args0
args$n <- "x"
expect_error(do.call(simData, args),
"'n' must be of class 'numeric'")
args$n <- c(1, 2)
expect_error(do.call(simData, args),
"'n' must have length 1")
args <- args0
args$FUN <- "x"
expect_error(do.call(simData, args),
"'FUN' must be of class 'function'")
args <- args0
args$message <- "x"
expect_error(do.call(simData, args),
"'message' must be of class 'logical'")
args$message <- c(TRUE, FALSE)
expect_error(do.call(simData, args),
"'message' must have length 1")
## Check that function works as expected with valid input
## -------------------------------------------------------------------------
## Let function choose branches
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = NULL, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 14)
expect_equal(S4Vectors::metadata(out)$branch$B, 15)
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.97)
expect_lt(sum(abs(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]) -
metadata(out)$FC)), 2.2)
expect_equal(colSums(SummarizedExperiment::assay(out, "counts")),
rep(10000, ncol(out)),
ignore_attr = TRUE)
## Let function choose branches - with restrictions
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = NULL, from.B = NULL,
minTip.A = 1, maxTip.A = 5, minTip.B = 2, maxTip.B = 6,
minPr.A = 0.1, maxPr.A = 0.9, ratio = 2, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = c(9900, 10000),
size = NULL, n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 13)
expect_equal(S4Vectors::metadata(out)$branch$B, 15)
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.97)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
c(10000, 9900))
## Fix branches - numbers
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = 18, from.B = 19,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 100,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 18)
expect_equal(S4Vectors::metadata(out)$branch$B, 19)
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Average library size should be approximately 10000
expect_lt(abs(mean(colSums(SummarizedExperiment::assay(out, "counts"))) -
10000), 20)
## Fix branch A - numbers
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = 18, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 18)
expect_equal(S4Vectors::metadata(out)$branch$B, 13)
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.89)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
## Fix branches - node labels
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = "Node_18", from.B = "Node_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Fix branches - aliases
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = "alias_18", from.B = "alias_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Multiple matrices
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "BS", from.A = "alias_18", from.B = "alias_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 2, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_null(SummarizedExperiment::assayNames(out))
expect_length(SummarizedExperiment::assays(out), 2)
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_equal(prod(unique(S4Vectors::metadata(out)$FC)), 1)
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
expect_true(round(S4Vectors::metadata(out)$branch$ratio, 5) %in%
round(S4Vectors::metadata(out)$FC, 5))
## Scenario US; fix branches - node labels
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "US", from.A = "Node_18", from.B = "Node_19",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_18")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_19")
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.7)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
expect_true(all(S4Vectors::metadata(out)$FC[c("t9", "t4")] < 1))
expect_true(all(S4Vectors::metadata(out)$FC[c("t10", "t1")] > 1))
expect_false(S4Vectors::metadata(out)$FC["t9"] ==
S4Vectors::metadata(out)$FC["t4"])
## Scenario US; switch branches
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "US", from.A = "Node_19", from.B = "Node_18",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_19")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_18")
## Calculate proportions for each entity in each sample, average within
## group, check that it agrees reasonably with simulated FCs
v <- sweep(assay(out, "counts"), 2, colSums(assay(out, "counts")), "/")
expect_gt(cor(rowMeans(v[, 51:100]) / rowMeans(v[, 1:50]),
S4Vectors::metadata(out)$FC), 0.7)
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
expect_true(all(S4Vectors::metadata(out)$FC[c("t9", "t4")] < 1))
expect_true(all(S4Vectors::metadata(out)$FC[c("t10", "t1")] > 1))
expect_false(S4Vectors::metadata(out)$FC["t9"] ==
S4Vectors::metadata(out)$FC["t4"])
## Scenario SS; switch branches
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = "Node_19", from.B = "Node_18",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = NULL,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_19")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_18")
expect_equal(unique(colSums(SummarizedExperiment::assay(out, "counts"))),
10000)
## Scenario SS; switch branches - specify size
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = "Node_19", from.B = "Node_18",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 0.5,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_19")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_18")
## Scenario SS; different branches - specify size
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = "Node_13", from.B = "Node_15",
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = NULL,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 0.5,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, "alias_13")
expect_equal(S4Vectors::metadata(out)$branch$B, "alias_15")
## Scenario SS; set adjB
set.seed(1)
out <- simData(tree = NULL, data = NULL, obj = toy_lse, assay = "counts",
scenario = "SS", from.A = NULL, from.B = NULL,
minTip.A = 0, maxTip.A = Inf, minTip.B = 0, maxTip.B = Inf,
minPr.A = 0, maxPr.A = 1, ratio = 4, adjB = 0.8,
pct = 0.6, nSam = c(50, 50), mu = 10000, size = 0.5,
n = 1, FUN = sum, message = FALSE)
expect_s4_class(out, "TreeSummarizedExperiment")
expect_equal(nrow(out), nrow(toy_lse))
expect_equal(ncol(out), 100)
expect_equal(SummarizedExperiment::assayNames(out), "counts")
expect_equal(TreeSummarizedExperiment::rowTree(out),
TreeSummarizedExperiment::rowTree(toy_lse))
expect_equal(rownames(out), rownames(toy_lse))
expect_equal(out$group, rep(c("C1", "C2"), each = 50))
expect_type(S4Vectors::metadata(out), "list")
expect_s3_class(S4Vectors::metadata(out)$branch, "data.frame")
expect_equal(S4Vectors::metadata(out)$branch$A, 14)
expect_equal(S4Vectors::metadata(out)$branch$B, 15)
## Test helper functions
## -------------------------------------------------------------------------
pars <- S4Vectors::metadata(parEstimate(toy_lse))$assays.par
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 0,
maxTip.B = 2, minPr.A = 0,
maxPr.A = 1, ratio = 1)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 18)
expect_equal(pk$B, 19)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 2)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t1", "t10")]), 4)) ## 19
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t4", "t9")]), 4)) ## 18
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 3,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 1)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 18)
expect_equal(pk$B, 13)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 3)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t2", "t6", "t7")]), 4)) ## 13
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t4", "t9")]), 4)) ## 18
expect_warning({
expect_error({
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 6,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 1)
}, "invalid first argument")
}, "no non-missing arguments")
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = 14, minTip.A = 0,
maxTip.A = Inf, minTip.B = 6,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 1)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 14)
expect_equal(pk$B, 15)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 6)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t1", "t4", "t5", "t8", "t9", "t10")]), 4)) ## 15
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t6", "t7")]), 4)) ## 18
expect_error({
pk <- .pickLoc(tree = rowTree(toy_lse), data = pars,
from.A = "alias_18", minTip.A = 0,
maxTip.A = Inf, minTip.B = 0,
maxTip.B = Inf, minPr.A = 0,
maxPr.A = 1, ratio = 10)
}, "Could not find two branches")
pk <- .infLoc(tree = rowTree(toy_lse), data = pars,
from.A = 14, from.B = 15)
expect_s3_class(pk, "data.frame")
expect_named(pk, c("A", "B", "ratio", "A_tips", "B_tips", "A_prop", "B_prop"))
expect_equal(pk$A, 14)
expect_equal(pk$B, 15)
expect_equal(pk$A_tips, 2)
expect_equal(pk$B_tips, 6)
expect_equal(round(pk$B_prop, 4), round(sum(pars$pi[c("t1", "t4", "t5", "t8", "t9", "t10")]), 4)) ## 15
expect_equal(round(pk$A_prop, 4), round(sum(pars$pi[c("t6", "t7")]), 4)) ## 18
})
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