tests/testthat/test-selNode.R
In fionarhuang/treeclimbR: An algorithm to find optimal signal levels in a tree
test_that("selNode works", {
## Generate example data
library(TreeSummarizedExperiment)
set.seed(1)
data(tinyTree)
toyTable <- matrix(rnbinom(40, size = 1, mu = 10), nrow = 10)
colnames(toyTable) <- paste(rep(LETTERS[seq_len(2)], each = 2),
rep(seq_len(2), 2), sep = "_")
rownames(toyTable) <- tinyTree$tip.label
lse <- TreeSummarizedExperiment::TreeSummarizedExperiment(
rowTree = tinyTree, assays = list(counts = toyTable))
## Check that function returns error with invalid input
## -------------------------------------------------------------------------
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"No valid input was found")
expect_error(selNode(pr = TRUE, obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'pr' must be of class 'numeric'")
expect_error(selNode(pr = c(1, 2), obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'pr' must be within [0,1]", fixed = TRUE)
expect_error(selNode(pr = c(0.5, 0.5), obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'tree' must not be NULL")
expect_error(selNode(pr = NULL, obj = 3, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"No valid input was found")
expect_error(selNode(pr = NULL, obj = lse, assay = 10, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"subscript is out of bounds")
expect_error(selNode(pr = NULL, obj = lse, assay = "missing", data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"assay %in% SummarizedExperiment::assayNames(obj)",
fixed = TRUE)
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = "x",
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'tree' must not be NULL")
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = "x",
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
'methods::is(obj, "matrix")', fixed = TRUE)
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = toyTable,
tree = 1, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'tree' must be of class 'phylo'")
expect_error(selNode(pr = c(0.5, 0.5), obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"!is.null(names(pars)) is not TRUE", fixed = TRUE)
expect_error(selNode(pr = c(a = 0.5, b = 0.5), obj = NULL,
assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"length(pars) == length(tree$tip.label) is not TRUE",
fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = letters[seq_len(10)]),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"all(names(pars) %in% tree$tip.label) is not TRUE",
fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = "x", maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'minTip' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = c(1, 2),
maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'minTip' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = "x", minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'maxTip' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = c(1, 2),
minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'maxTip' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = "x",
maxPr = 1, skip = NULL, all = FALSE),
"'minPr' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf,
minPr = c(0.2, 0.5),
maxPr = 1, skip = NULL, all = FALSE),
"'minPr' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 2,
maxPr = 1, skip = NULL, all = FALSE),
"'minPr' must be within [0,1]", fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = "x", skip = NULL, all = FALSE),
"'maxPr' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = c(0.2, 0.5), skip = NULL, all = FALSE),
"'maxPr' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 2, skip = NULL, all = FALSE),
"'maxPr' must be within [0,1]", fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = 1),
"'all' must be of class 'logical'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = c(TRUE, FALSE)),
"'all' must have length 1")
## Check that function works as expected with valid input - pr
## -------------------------------------------------------------------------
dat <- parEstimate(obj = toyTable)
pr <- dat$pi
## No restrictions
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Duplicated node labels
tree2 <- tinyTree
tree2$node.label[2] <- "Node_11"
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tree2, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "nodeLab_alias",
"proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Restrict proportion
expect_message({
out <- selNode(pr = pr, obj = lse, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = TRUE)
}, "Ignoring obj when input is a proportions vector")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13, 17))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]),
sum(pr[c("t4", "t8", "t9")])))
expect_equal(out$numTip,
c(3, 3))
## Restrict proportion too much
expect_error(selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 0.1, skip = NULL, all = TRUE),
"All nodes have proportions exceeding maxPr")
expect_error(selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf,
minPr = 0.6, maxPr = 0.4, skip = NULL, all = TRUE),
"No nodes fulfill the requirements")
expect_error(selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf,
minPr = 0, maxPr = 1, skip = seq_len(10), all = TRUE),
"No nodes fulfill the requirements")
## Return only single node
expect_message({
out <- selNode(pr = pr, obj = NULL, assay = 1, data = toyTable,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = FALSE)
}, "Ignoring data when input is a proportions vector")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 1)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")])))
expect_equal(out$numTip,
c(3))
## Skip some nodes
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = c(16), all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(13, 14))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")])))
expect_equal(out$numTip,
c(3, 2))
## Skip, but with node label
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = c("Node_16"), all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(13, 14))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")])))
expect_equal(out$numTip,
c(3, 2))
## Skip tips
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = c("t10", "t6"), all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(17, 18))
expect_equal(out$proportion,
c(sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")])))
expect_equal(out$numTip,
c(3, 2))
## Check that function works as expected with valid input - TSE
## -------------------------------------------------------------------------
dat <- parEstimate(obj = toyTable)
pr <- dat$pi
## No restrictions
expect_message({
out <- selNode(pr = NULL, obj = lse, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
}, "Ignoring tree when input is a TSE")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Restrict proportion
expect_message({
out <- selNode(pr = NULL, obj = lse, assay = "counts", data = dat,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = TRUE)
}, "Ignoring data when input is a TSE")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13, 17))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]),
sum(pr[c("t4", "t8", "t9")])))
expect_equal(out$numTip,
c(3, 3))
## Check that function works as expected with valid input - data + tree
## -------------------------------------------------------------------------
dat <- parEstimate(obj = toyTable)
pr <- dat$pi
## No restrictions
out <- selNode(pr = NULL, obj = NULL, assay = 1, data = dat,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Restrict proportion
out <- selNode(pr = NULL, obj = NULL, assay = 1, data = dat,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13, 17))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]),
sum(pr[c("t4", "t8", "t9")])))
expect_equal(out$numTip,
c(3, 3))
})
fionarhuang/treeclimbR documentation built on Aug. 7, 2024, 12:44 p.m.
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test_that("selNode works", {
## Generate example data
library(TreeSummarizedExperiment)
set.seed(1)
data(tinyTree)
toyTable <- matrix(rnbinom(40, size = 1, mu = 10), nrow = 10)
colnames(toyTable) <- paste(rep(LETTERS[seq_len(2)], each = 2),
rep(seq_len(2), 2), sep = "_")
rownames(toyTable) <- tinyTree$tip.label
lse <- TreeSummarizedExperiment::TreeSummarizedExperiment(
rowTree = tinyTree, assays = list(counts = toyTable))
## Check that function returns error with invalid input
## -------------------------------------------------------------------------
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"No valid input was found")
expect_error(selNode(pr = TRUE, obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'pr' must be of class 'numeric'")
expect_error(selNode(pr = c(1, 2), obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'pr' must be within [0,1]", fixed = TRUE)
expect_error(selNode(pr = c(0.5, 0.5), obj = NULL, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'tree' must not be NULL")
expect_error(selNode(pr = NULL, obj = 3, assay = 1, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"No valid input was found")
expect_error(selNode(pr = NULL, obj = lse, assay = 10, data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"subscript is out of bounds")
expect_error(selNode(pr = NULL, obj = lse, assay = "missing", data = NULL,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"assay %in% SummarizedExperiment::assayNames(obj)",
fixed = TRUE)
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = "x",
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'tree' must not be NULL")
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = "x",
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
'methods::is(obj, "matrix")', fixed = TRUE)
expect_error(selNode(pr = NULL, obj = NULL, assay = 1, data = toyTable,
tree = 1, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'tree' must be of class 'phylo'")
expect_error(selNode(pr = c(0.5, 0.5), obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"!is.null(names(pars)) is not TRUE", fixed = TRUE)
expect_error(selNode(pr = c(a = 0.5, b = 0.5), obj = NULL,
assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"length(pars) == length(tree$tip.label) is not TRUE",
fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = letters[seq_len(10)]),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"all(names(pars) %in% tree$tip.label) is not TRUE",
fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = "x", maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'minTip' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = c(1, 2),
maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'minTip' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = "x", minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'maxTip' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = c(1, 2),
minPr = 0,
maxPr = 1, skip = NULL, all = FALSE),
"'maxTip' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = "x",
maxPr = 1, skip = NULL, all = FALSE),
"'minPr' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf,
minPr = c(0.2, 0.5),
maxPr = 1, skip = NULL, all = FALSE),
"'minPr' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 2,
maxPr = 1, skip = NULL, all = FALSE),
"'minPr' must be within [0,1]", fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = "x", skip = NULL, all = FALSE),
"'maxPr' must be of class 'numeric'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = c(0.2, 0.5), skip = NULL, all = FALSE),
"'maxPr' must have length 1")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 2, skip = NULL, all = FALSE),
"'maxPr' must be within [0,1]", fixed = TRUE)
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = 1),
"'all' must be of class 'logical'")
expect_error(selNode(pr = structure(rep(0.1, 10),
names = paste0("t", seq_len(10))),
obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = c(TRUE, FALSE)),
"'all' must have length 1")
## Check that function works as expected with valid input - pr
## -------------------------------------------------------------------------
dat <- parEstimate(obj = toyTable)
pr <- dat$pi
## No restrictions
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Duplicated node labels
tree2 <- tinyTree
tree2$node.label[2] <- "Node_11"
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tree2, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "nodeLab_alias",
"proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Restrict proportion
expect_message({
out <- selNode(pr = pr, obj = lse, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = TRUE)
}, "Ignoring obj when input is a proportions vector")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13, 17))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]),
sum(pr[c("t4", "t8", "t9")])))
expect_equal(out$numTip,
c(3, 3))
## Restrict proportion too much
expect_error(selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 0.1, skip = NULL, all = TRUE),
"All nodes have proportions exceeding maxPr")
expect_error(selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf,
minPr = 0.6, maxPr = 0.4, skip = NULL, all = TRUE),
"No nodes fulfill the requirements")
expect_error(selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf,
minPr = 0, maxPr = 1, skip = seq_len(10), all = TRUE),
"No nodes fulfill the requirements")
## Return only single node
expect_message({
out <- selNode(pr = pr, obj = NULL, assay = 1, data = toyTable,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = FALSE)
}, "Ignoring data when input is a proportions vector")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 1)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")])))
expect_equal(out$numTip,
c(3))
## Skip some nodes
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = c(16), all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(13, 14))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")])))
expect_equal(out$numTip,
c(3, 2))
## Skip, but with node label
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = c("Node_16"), all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(13, 14))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")])))
expect_equal(out$numTip,
c(3, 2))
## Skip tips
out <- selNode(pr = pr, obj = NULL, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = c("t10", "t6"), all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(17, 18))
expect_equal(out$proportion,
c(sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")])))
expect_equal(out$numTip,
c(3, 2))
## Check that function works as expected with valid input - TSE
## -------------------------------------------------------------------------
dat <- parEstimate(obj = toyTable)
pr <- dat$pi
## No restrictions
expect_message({
out <- selNode(pr = NULL, obj = lse, assay = 1, data = NULL,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
}, "Ignoring tree when input is a TSE")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Restrict proportion
expect_message({
out <- selNode(pr = NULL, obj = lse, assay = "counts", data = dat,
tree = NULL, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = TRUE)
}, "Ignoring data when input is a TSE")
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13, 17))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]),
sum(pr[c("t4", "t8", "t9")])))
expect_equal(out$numTip,
c(3, 3))
## Check that function works as expected with valid input - data + tree
## -------------------------------------------------------------------------
dat <- parEstimate(obj = toyTable)
pr <- dat$pi
## No restrictions
out <- selNode(pr = NULL, obj = NULL, assay = 1, data = dat,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0,
maxPr = 1, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 9)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, seq(11, 19))
expect_equal(out$proportion,
c(sum(pr), sum(pr[names(pr) != "t3"]),
sum(pr[c("t2", "t6", "t7")]), sum(pr[c("t6", "t7")]),
sum(pr[c("t1", "t4", "t5", "t8", "t9", "t10")]),
sum(pr[c("t1", "t4", "t8", "t9", "t10")]),
sum(pr[c("t4", "t8", "t9")]), sum(pr[c("t4", "t9")]),
sum(pr[c("t1", "t10")])))
expect_equal(out$numTip,
c(10, 9, 3, 2, 6, 5, 3, 2, 2))
## Restrict proportion
out <- selNode(pr = NULL, obj = NULL, assay = 1, data = dat,
tree = tinyTree, minTip = 0, maxTip = Inf, minPr = 0.2,
maxPr = 0.5, skip = NULL, all = TRUE)
expect_s3_class(out, "data.frame")
expect_equal(nrow(out), 2)
expect_named(out, c("nodeNum", "nodeLab", "proportion", "numTip"))
expect_equal(out$nodeNum, c(13, 17))
expect_equal(out$proportion,
c(sum(pr[c("t2", "t6", "t7")]),
sum(pr[c("t4", "t8", "t9")])))
expect_equal(out$numTip,
c(3, 3))
})
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