/usr/share/doc/r-bioc-deseq2/tests/testthat/test_unmix_samples.R is in r-bioc-deseq2 1.18.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | context("unmix samples")
test_that("unmixing samples works", {
set.seed(1)
n <- 2000
a <- runif(n)
b <- runif(n)
c <- runif(n)
counts <- matrix(nrow=n, ncol=8)
disp <- 0.01
counts[,1] <- rnbinom(n, mu=1e4 * a, size=1/disp)
counts[,2] <- rnbinom(n, mu=1e4 * b, size=1/disp)
counts[,3] <- rnbinom(n, mu=1e4 * c, size=1/disp)
counts[,4] <- rnbinom(n, mu=1e4 * (.75*a + .25*b), size=1/disp)
counts[,5] <- rnbinom(n, mu=1e4 * (.5*a + .5*b), size=1/disp)
counts[,6] <- rnbinom(n, mu=1e4 * (.25*a + .75*b), size=1/disp)
counts[,7] <- rnbinom(n, mu=1e4 * (.33*a + .33*b + .33*c), size=1/disp)
counts[,8] <- rnbinom(n, mu=1e4 * (.25*a + .25*b + .5*c), size=1/disp)
coldata <- data.frame(a=c(1,0,0,.75,.5,.25,.33,.25),
b=c(0,1,0,.25,.5,.75,.33,.25),
c=c(0,0,1, 0, 0, 0,.33,.5))
dds <- DESeqDataSetFromMatrix(counts, coldata, ~1)
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds, fitType="mean")
vsd <- varianceStabilizingTransformation(dds, blind=FALSE)
#library(ggplot2)
#plotPCA(vsd, intgroup=c("a","b","c")) + geom_point(size=5)
pure <- matrix(rnbinom(3*n,mu=1e4*c(a,b,c),size=1/disp),ncol=3)
colnames(pure) <- c("a","b","c")
x <- counts
alpha <- attr(dispersionFunction(dds),"mean")
mix <- unmix(counts, pure=pure, alpha=alpha, quiet=TRUE)
max(abs(dds$a - mix[,1]))
max(abs(dds$b - mix[,2]))
max(abs(dds$c - mix[,3]))
expect_lt(max(abs(dds$a - mix[,1])), .01)
expect_lt(max(abs(dds$b - mix[,2])), .01)
expect_lt(max(abs(dds$c - mix[,3])), .01)
# test the shifted log (designed for TPMs)
mix2 <- unmix(counts, pure=pure, shift=0.5, quiet=TRUE)
})
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