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# This tests the various trendCV2() options.
# require(scran); require(testthat); source("test-trend-cv2.R")
set.seed(20002)
ncells <- 200
ngenes <- 1000
means <- 2^runif(ngenes, -1, 5)
dummy <- matrix(rnbinom(ngenes*ncells, mu=means, size=5), ncol=ncells, nrow=ngenes)
library(DelayedMatrixStats)
means <- rowMeans(dummy)
cv2s <- rowVars(dummy)/means^2
test_that("trendCV2 works on a basic scenario", {
out <- fitTrendCV2(means, cv2s, ncells)
expect_true(out$std.dev > 0)
expect_is(out$trend, "function")
# Hard to test it without copying the code, so I'll just check the limits.
expect_equal(out$trend(0), Inf)
expect_equal(out$trend(1:10), sapply(1:10, out$trend)) # Checking we get consistent results with returned function.
expect_equal(out$trend(100:1/20), sapply(100:1/20, out$trend)) # More checking, reversed order.
})
test_that("trendCV2 prunes out capped CV2 values", {
dummy[1:10,] <- 0
dummy[1:10,1] <- 1:10*100
means <- rowMeans(dummy)
cv2s <- rowVars(dummy)/means^2
out <- fitTrendCV2(means, cv2s, ncells)
ref <- fitTrendCV2(means[-(1:10)], cv2s[-(1:10)], ncells)
expect_identical(out$std.dev, ref$std.dev)
expect_identical(out$trend(1:100), ref$trend(1:100))
})
test_that("trendCV2 handles nls errors gracefully", {
cv2.err <- jitter(10/1:10)
m.err <- 1:10
expect_error(fitTrendCV2(m.err, cv2.err, 10, simplified=FALSE), "singular gradient")
expect_error(fit <- fitTrendCV2(m.err, cv2.err, 10), NA)
})
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