tests/testthat/test_main.R
In lwaldron/doppelgangR: Identify likely duplicate samples from genomic or meta-data
library(Biobase)
library(doppelgangR)
ncor <- 0
npheno <- 0
nsmoking <- 0
options(stringsAsFactors = FALSE)
set.seed(1)
m1 <- matrix(rnorm(1100), ncol = 11)
colnames(m1) <- paste("m", 1:11, sep = "")
rownames(m1) <- make.names(1:nrow(m1))
n1 <- matrix(rnorm(1000), ncol = 10)
colnames(n1) <- paste("n", 1:10, sep = "")
rownames(n1) <- make.names(1:nrow(n1))
##m:1 & n:1 are expression doppelgangers:
m1[, 1] <- n1[, 1] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##m:2 & m:3 are expression doppelgangers:
m1[, 2] <- m1[, 3] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##n:2 & n:3 are expression doppelgangers:
n1[, 2] <- n1[, 3] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##n:8 & n:9 are expression doppelgangers:
n1[, 8] <- n1[, 9] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
#n:4 & m:6 are expression doppelgangers:
n1[, 4] <- m1[, 6] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
#n:5 & m:4 are expression doppelgangers:
n1[, 5] <- m1[, 4] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##
##m:10 and n:10 are phenotype doppelgangers:
m.pdata <-
matrix(letters[sample(1:26, size = 110, replace = TRUE)], ncol = 10)
rownames(m.pdata) <- colnames(m1)
n.pdata <-
matrix(letters[sample(1:26, size = 100, replace = TRUE)], ncol = 10)
n.pdata[10,] <- m.pdata[10,]
npheno <- npheno + 1
rownames(n.pdata) <- colnames(n1)
##Create ExpressionSets
m.eset <-
ExpressionSet(assayData = m1, phenoData = AnnotatedDataFrame(data.frame(m.pdata)))
m.eset$id <- toupper(colnames(m1))
##
n.eset <-
ExpressionSet(assayData = n1, phenoData = AnnotatedDataFrame(data.frame(n.pdata)))
n.eset$id <- toupper(colnames(n1))
##m5 and n4 are "smoking gun" doppelgangers:
n.eset$id[4] <- "gotcha"
m.eset$id[5] <- "gotcha"
nsmoking <- nsmoking + 1
##
esets <- list(m = m.eset, n = n.eset)
##------------------------------------------
##Check of all three types of doppelgangers:
##------------------------------------------
res1 <-
doppelgangR(
esets,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df1 <- summary(res1)
expect_true(df1[df1$sample1 == "m:m1" &
df1$sample2 == "n:n1", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m2" &
df1$sample2 == "m:m3", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m6" &
df1$sample2 == "n:n4", "expr.doppel"])
expect_true(df1[df1$sample1 == "n:n2" &
df1$sample2 == "n:n3", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m6" &
df1$sample2 == "n:n4", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m10" &
df1$sample2 == "n:n10", "pheno.doppel"])
expect_true(df1[df1$sample1 == "m:m5" &
df1$sample2 == "n:n4", "smokinggun.doppel"])
expect_equal(nrow(df1), ncor + npheno + nsmoking)
expect_equal(sum(df1$expr.doppel), ncor)
expect_equal(sum(df1$pheno.doppel), npheno)
expect_equal(sum(df1$smokinggun.doppel), nsmoking)
expect_equal(sum(is.na(df1$expr.similarity)), 0)
expect_equal(sum(is.na(df1$pheno.similarity)), 0)
expect_equal(sum(is.na(df1$smokinggun.similarity)), 0)
expect_equal(
df1$id,
c(
"M2:M3",
"N2:N3",
"N8:N9",
"M1:N1",
"gotcha:gotcha",
"M6:gotcha",
"M4:N5",
"M10:N10"
)
)
for (i in match(paste("X", 1:10, sep = ""), colnames(df1))) {
lab <- paste("Checking column", i, "\n")
expect_equal(all(grepl("[a-z]:[a-z]", df1[[i]])), TRUE, label = lab)
}
##------------------------------------------
## Check without smoking guns
##------------------------------------------
res2 <-
doppelgangR(esets,
smokingGunFinder.args = NULL,
cache.dir = NULL)
df2 <- summary(res2)
for (i in grep("pheno.similarity|smokinggun.similarity",
colnames(df1),
invert = TRUE)) {
lab <- paste("Check without smoking guns, column", i, "\n")
expect_equal(df2[, i], df1[!df1$smokinggun.doppel, i], label = lab)
}
##------------------------------------------
## Check without phenotype
##------------------------------------------
res3 <-
doppelgangR(
esets,
phenoFinder.args = NULL,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df3 <- summary(res3)
for (i in grep("pheno.similarity", colnames(df1), invert = TRUE)) {
lab <- paste("Check without phenotype column", i, "\n")
expect_equal(df3[, i], df1[!df1$pheno.doppel, i], label = lab)
}
##------------------------------------------
## Check without expression
##------------------------------------------
res4 <-
doppelgangR(
esets,
corFinder.args = NULL,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df4 <- summary(res4)
for (i in grep("expr.similarity", colnames(df1), invert = TRUE)) {
lab <- paste("Check without expression column", i, "\n")
expect_equal(df4[, i], df1[!df1$expr.doppel, i], label = lab)
}
##------------------------------------------
## Check smoking guns only
##------------------------------------------
res4b <-
doppelgangR(
esets,
corFinder.args = NULL,
phenoFinder.args = NULL,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df4b <- summary(res4b)
rownames(df4b) <- NULL
df4b.compare <-
df1[df1$smokinggun.doppel,]
rownames(df4b.compare) <- NULL
##don't check expr and pheno columns
expect_identical(df4b.compare[,-3:-6], df4b[,-3:-6], label = "Check smoking guns only")
##------------------------------------------
## Check pruning
##------------------------------------------
res5 <-
doppelgangR(
esets,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
intermediate.pruning = TRUE,
cache.dir = NULL
)
df5 <- summary(res5)
expect_equal(df1, df5, label = "Check pruning")
##------------------------------------------
## Check caching, with a third ExpressionSet that is almost identical to the first
##------------------------------------------
esets2 <- c(esets, esets[[1]])
names(esets2)[3] <- "o"
exprs(esets2[[3]]) <-
exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd =
0.1)
esets2[[3]]$X10 <- "a"
sampleNames(esets2[[3]]) <-
paste("X", sampleNames(esets2[[3]]), sep = "")
tmpcachedir <- tempdir()
##Do this twice, so the second time the cache will be used:
for (i in 1:2) {
res6 <-
doppelgangR(
esets2,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = tmpcachedir
)
##Make sure comparison of m to n is the same as res1:
df6a <- summary(res6)
df6a <-
df6a[grepl("^[mn]", df6a$sample1) & grepl("^[mn]", df6a$sample2),]
rownames(df6a) <- 1:nrow(df6a)
expect_equal(df1, df6a, label = "Check caching, with a third ExpressionSet that is almost identical to the first")
}
df6b <- summary(res6)
df6b <-
df6b[grepl("^[mo]", df6b$sample1) & grepl("^[mo]", df6b$sample2),]
expect_identical(df6b[df6b$sample1 == "o:Xm2" &
df6b$sample2 == "o:Xm3", "expr.doppel"], TRUE)
df6b <- df6b[-1:-2,]
##expect_true(all(df6b$expr.doppel)) ## not a bug, but a shortcoming in the outlier detection that these are not all identified as expression doppelgangers.
expect_true(all(df6b$pheno.doppel))
expect_true(all(df6b$smokinggun.doppel))
res7 <-
doppelgangR(
esets2,
phenoFinder.args = NULL,
smokingGunFinder.args = NULL,
outlierFinder.expr.args = list(
bonf.prob = 1.0,
transFun = atanh,
tail = "upper"
),
cache.dir = NULL
)
df7 <- summary(res7)
has.o <- grepl("^o", df7$sample1) | grepl("^o", df7$sample2)
df7a <- df7[has.o,]
df7a$sample1 <- sub("o", "m", df7a$sample1)
df7a$sample1 <- sub("X", "", df7a$sample1)
df7a$sample2 <- sub("o", "m", df7a$sample2)
df7a$sample2 <- sub("X", "", df7a$sample2)
df7b <- df7[!has.o,]
df7b <-
df7b[(!grepl("^n", df7b$sample1) | !grepl("^n", df7b$sample2)),]
for (i in 1:nrow(df7a)) {
df7a[i, 1:2] <- sort(as.character(df7a[i, 1:2]))
df7b[i, 1:2] <- sort(as.character(df7b[i, 1:2]))
}
df7a <- df7a[order(df7a$sample1, df7a$sample2),]
df7b <- df7b[order(df7b$sample1, df7b$sample2),]
expect_true(all(df7a$expr.doppel == df7b$expr.doppel))
expect_true(all(df7a$pheno.doppel == df7b$pheno.doppel))
expect_true(all(df7a$smokinggun.doppel == df7b$smokinggun.doppel))
##------------------------------------------
## Check corFinder function
##------------------------------------------
cor1 <- corFinder(eset.pair = esets)
cor2 <- corFinder(eset.pair = esets[c(2, 1)])
expect_equal(cor1, t(cor2), label = "Check corFinder function")
cor1 <- corFinder(eset.pair = esets, use.ComBat = FALSE)
cor2 <- corFinder(eset.pair = esets[c(2, 1)], use.ComBat = FALSE)
expect_equal(cor1, t(cor2), label = "Check corFinder function 2")
cor1 <- corFinder(eset.pair = esets[c(1, 1)])
cor2 <- corFinder(eset.pair = esets[c(1, 1)], use.ComBat = FALSE)
expect_equal(cor1, cor2, label = "Check corFinder function 3")
##Check missing values:
exprs(esets[[1]])[1:10, 1:5] <- NA
expect_message(doppelgangR(esets[1:2]), regexp = "Finalizing", label = "check missing values 1")
## More missing values:
exprs(esets[[1]])[1:10, 1:8] <- NA
expect_warning(doppelgangR(esets[1:2]), regexp = "10 rows with more than 50 % entries missing", label = "check missing values 2")
## More missing values:
exprs(esets[[1]])[1:10, 1:11] <- NA
expect_warning(doppelgangR(esets[1:2]), regexp = "mean imputation used for these rows", label = "check missing values 3")
## infinite values:
exprs(esets[[1]])[14, 1] <- -Inf
exprs(esets[[1]])[15, 2] <- Inf
expect_warning(doppelgangR(esets[1:2]), regexp = "Inf with min/max expression values for dataset m", label = "check missing values 4")
##------------------------------------------
## Smoking guns only with cache=TRUE
##------------------------------------------
expect_warning(
dop <-
doppelgangR(
esets,
corFinder.args = NULL,
phenoFinder.args = NULL,
manual.smokingguns = "id"
),
label = "Smoking guns only with cache=TRUE"
)
expect_equal(summary(dop)[, 1], "m:m5", label = "Smoking guns only with cache=TRUE 2")
expect_equal(summary(dop)[, 2], "n:n4", label = "Smoking guns only with cache=TRUE 3")
##------------------------------------------
## Identical ExpressionSets
##------------------------------------------
expect_warning(df1 <-
summary(doppelgangR(esets[[1]], cache.dir = NULL)), label = "Identical ExpressionSets 1")
expect_true(df1$sample1 == "m2", label = "Identical ExpressionSets 2")
expect_true(df1$sample2 == "m3", label = "Identical ExpressionSets 3")
##
df2 <- summary(doppelgangR(esets[[2]], cache.dir = NULL))
expect_true(all(df2$sample1 == "n2"), label = "Identical ExpressionSets 4")
expect_true(all(df2$sample2 == "n3"), label = "Identical ExpressionSets 4b")
##
expect_warning(df5 <-
summary(doppelgangR(
list(eset1 = esets[[1]], eset2 = esets[[2]]), cache.dir = NULL
)), label = "Identical ExpressionSets 5")
expect_warning(df6 <-
summary(doppelgangR(esets, cache.dir = NULL)), label = "Identical ExpressionSets 6")
expect_identical(df5[,-1:-2], df6[,-1:-2], label = "Identical ExpressionSets 7")
expect_identical(sub("eset2", "n", sub("eset1", "m", df5$sample1)), df6$sample1, label = "Identical ExpressionSets 8")
expect_identical(sub("eset2", "n", sub("eset1", "m", df5$sample2)), df6$sample2, label = "Identical ExpressionSets 9")
## with zero-column pData:
expect_warning(withpheno <-
summary(doppelgangR(esets)), label = "with zero-column pData")
esets3 <- esets
pData(esets3[[1]]) <- pData(esets3[[1]])[, 0]
expect_warning(withoutpheno <-
summary(doppelgangR(esets3)),
regexp = "expression values for dataset m",
label = "with zero-column pData 2")
pData(esets3[[2]]) <- pData(esets3[[2]])[, 0]
expect_warning(withoutpheno2 <-
summary(doppelgangR(esets3)),
regexp = "with min/max expression values for dataset m",
label = "with zero-column pData 3")
withoutpheno3 <- withoutpheno[!withoutpheno$pheno.doppel,]
withoutpheno4 <- withoutpheno2[!withoutpheno2$pheno.doppel,]
expect_identical(withoutpheno[, 1:4], withoutpheno3[, 1:4], label = "with zero-column pData 4")
expect_identical(withoutpheno2[, 1:4], withoutpheno4[, 1:4], label = "with zero-column pData 5")
esets4 <- esets
for (i in 1:length(esets4))
pData(esets4[[i]]) <- pData(esets4[[i]])[1]
expect_warning(doppelgangR(esets4[[1]]), label = "with zero-column pData 6")
expect_s4_class(doppelgangR(esets4[[2]]), "DoppelGang")
lwaldron/doppelgangR documentation built on Nov. 2, 2024, 9:28 a.m.
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library(Biobase)
library(doppelgangR)
ncor <- 0
npheno <- 0
nsmoking <- 0
options(stringsAsFactors = FALSE)
set.seed(1)
m1 <- matrix(rnorm(1100), ncol = 11)
colnames(m1) <- paste("m", 1:11, sep = "")
rownames(m1) <- make.names(1:nrow(m1))
n1 <- matrix(rnorm(1000), ncol = 10)
colnames(n1) <- paste("n", 1:10, sep = "")
rownames(n1) <- make.names(1:nrow(n1))
##m:1 & n:1 are expression doppelgangers:
m1[, 1] <- n1[, 1] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##m:2 & m:3 are expression doppelgangers:
m1[, 2] <- m1[, 3] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##n:2 & n:3 are expression doppelgangers:
n1[, 2] <- n1[, 3] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##n:8 & n:9 are expression doppelgangers:
n1[, 8] <- n1[, 9] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
#n:4 & m:6 are expression doppelgangers:
n1[, 4] <- m1[, 6] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
#n:5 & m:4 are expression doppelgangers:
n1[, 5] <- m1[, 4] + rnorm(100, sd = 0.25)
ncor <- ncor + 1
##
##m:10 and n:10 are phenotype doppelgangers:
m.pdata <-
matrix(letters[sample(1:26, size = 110, replace = TRUE)], ncol = 10)
rownames(m.pdata) <- colnames(m1)
n.pdata <-
matrix(letters[sample(1:26, size = 100, replace = TRUE)], ncol = 10)
n.pdata[10,] <- m.pdata[10,]
npheno <- npheno + 1
rownames(n.pdata) <- colnames(n1)
##Create ExpressionSets
m.eset <-
ExpressionSet(assayData = m1, phenoData = AnnotatedDataFrame(data.frame(m.pdata)))
m.eset$id <- toupper(colnames(m1))
##
n.eset <-
ExpressionSet(assayData = n1, phenoData = AnnotatedDataFrame(data.frame(n.pdata)))
n.eset$id <- toupper(colnames(n1))
##m5 and n4 are "smoking gun" doppelgangers:
n.eset$id[4] <- "gotcha"
m.eset$id[5] <- "gotcha"
nsmoking <- nsmoking + 1
##
esets <- list(m = m.eset, n = n.eset)
##------------------------------------------
##Check of all three types of doppelgangers:
##------------------------------------------
res1 <-
doppelgangR(
esets,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df1 <- summary(res1)
expect_true(df1[df1$sample1 == "m:m1" &
df1$sample2 == "n:n1", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m2" &
df1$sample2 == "m:m3", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m6" &
df1$sample2 == "n:n4", "expr.doppel"])
expect_true(df1[df1$sample1 == "n:n2" &
df1$sample2 == "n:n3", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m6" &
df1$sample2 == "n:n4", "expr.doppel"])
expect_true(df1[df1$sample1 == "m:m10" &
df1$sample2 == "n:n10", "pheno.doppel"])
expect_true(df1[df1$sample1 == "m:m5" &
df1$sample2 == "n:n4", "smokinggun.doppel"])
expect_equal(nrow(df1), ncor + npheno + nsmoking)
expect_equal(sum(df1$expr.doppel), ncor)
expect_equal(sum(df1$pheno.doppel), npheno)
expect_equal(sum(df1$smokinggun.doppel), nsmoking)
expect_equal(sum(is.na(df1$expr.similarity)), 0)
expect_equal(sum(is.na(df1$pheno.similarity)), 0)
expect_equal(sum(is.na(df1$smokinggun.similarity)), 0)
expect_equal(
df1$id,
c(
"M2:M3",
"N2:N3",
"N8:N9",
"M1:N1",
"gotcha:gotcha",
"M6:gotcha",
"M4:N5",
"M10:N10"
)
)
for (i in match(paste("X", 1:10, sep = ""), colnames(df1))) {
lab <- paste("Checking column", i, "\n")
expect_equal(all(grepl("[a-z]:[a-z]", df1[[i]])), TRUE, label = lab)
}
##------------------------------------------
## Check without smoking guns
##------------------------------------------
res2 <-
doppelgangR(esets,
smokingGunFinder.args = NULL,
cache.dir = NULL)
df2 <- summary(res2)
for (i in grep("pheno.similarity|smokinggun.similarity",
colnames(df1),
invert = TRUE)) {
lab <- paste("Check without smoking guns, column", i, "\n")
expect_equal(df2[, i], df1[!df1$smokinggun.doppel, i], label = lab)
}
##------------------------------------------
## Check without phenotype
##------------------------------------------
res3 <-
doppelgangR(
esets,
phenoFinder.args = NULL,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df3 <- summary(res3)
for (i in grep("pheno.similarity", colnames(df1), invert = TRUE)) {
lab <- paste("Check without phenotype column", i, "\n")
expect_equal(df3[, i], df1[!df1$pheno.doppel, i], label = lab)
}
##------------------------------------------
## Check without expression
##------------------------------------------
res4 <-
doppelgangR(
esets,
corFinder.args = NULL,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df4 <- summary(res4)
for (i in grep("expr.similarity", colnames(df1), invert = TRUE)) {
lab <- paste("Check without expression column", i, "\n")
expect_equal(df4[, i], df1[!df1$expr.doppel, i], label = lab)
}
##------------------------------------------
## Check smoking guns only
##------------------------------------------
res4b <-
doppelgangR(
esets,
corFinder.args = NULL,
phenoFinder.args = NULL,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = NULL
)
df4b <- summary(res4b)
rownames(df4b) <- NULL
df4b.compare <-
df1[df1$smokinggun.doppel,]
rownames(df4b.compare) <- NULL
##don't check expr and pheno columns
expect_identical(df4b.compare[,-3:-6], df4b[,-3:-6], label = "Check smoking guns only")
##------------------------------------------
## Check pruning
##------------------------------------------
res5 <-
doppelgangR(
esets,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
intermediate.pruning = TRUE,
cache.dir = NULL
)
df5 <- summary(res5)
expect_equal(df1, df5, label = "Check pruning")
##------------------------------------------
## Check caching, with a third ExpressionSet that is almost identical to the first
##------------------------------------------
esets2 <- c(esets, esets[[1]])
names(esets2)[3] <- "o"
exprs(esets2[[3]]) <-
exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd =
0.1)
esets2[[3]]$X10 <- "a"
sampleNames(esets2[[3]]) <-
paste("X", sampleNames(esets2[[3]]), sep = "")
tmpcachedir <- tempdir()
##Do this twice, so the second time the cache will be used:
for (i in 1:2) {
res6 <-
doppelgangR(
esets2,
manual.smokingguns = "id",
automatic.smokingguns = FALSE,
cache.dir = tmpcachedir
)
##Make sure comparison of m to n is the same as res1:
df6a <- summary(res6)
df6a <-
df6a[grepl("^[mn]", df6a$sample1) & grepl("^[mn]", df6a$sample2),]
rownames(df6a) <- 1:nrow(df6a)
expect_equal(df1, df6a, label = "Check caching, with a third ExpressionSet that is almost identical to the first")
}
df6b <- summary(res6)
df6b <-
df6b[grepl("^[mo]", df6b$sample1) & grepl("^[mo]", df6b$sample2),]
expect_identical(df6b[df6b$sample1 == "o:Xm2" &
df6b$sample2 == "o:Xm3", "expr.doppel"], TRUE)
df6b <- df6b[-1:-2,]
##expect_true(all(df6b$expr.doppel)) ## not a bug, but a shortcoming in the outlier detection that these are not all identified as expression doppelgangers.
expect_true(all(df6b$pheno.doppel))
expect_true(all(df6b$smokinggun.doppel))
res7 <-
doppelgangR(
esets2,
phenoFinder.args = NULL,
smokingGunFinder.args = NULL,
outlierFinder.expr.args = list(
bonf.prob = 1.0,
transFun = atanh,
tail = "upper"
),
cache.dir = NULL
)
df7 <- summary(res7)
has.o <- grepl("^o", df7$sample1) | grepl("^o", df7$sample2)
df7a <- df7[has.o,]
df7a$sample1 <- sub("o", "m", df7a$sample1)
df7a$sample1 <- sub("X", "", df7a$sample1)
df7a$sample2 <- sub("o", "m", df7a$sample2)
df7a$sample2 <- sub("X", "", df7a$sample2)
df7b <- df7[!has.o,]
df7b <-
df7b[(!grepl("^n", df7b$sample1) | !grepl("^n", df7b$sample2)),]
for (i in 1:nrow(df7a)) {
df7a[i, 1:2] <- sort(as.character(df7a[i, 1:2]))
df7b[i, 1:2] <- sort(as.character(df7b[i, 1:2]))
}
df7a <- df7a[order(df7a$sample1, df7a$sample2),]
df7b <- df7b[order(df7b$sample1, df7b$sample2),]
expect_true(all(df7a$expr.doppel == df7b$expr.doppel))
expect_true(all(df7a$pheno.doppel == df7b$pheno.doppel))
expect_true(all(df7a$smokinggun.doppel == df7b$smokinggun.doppel))
##------------------------------------------
## Check corFinder function
##------------------------------------------
cor1 <- corFinder(eset.pair = esets)
cor2 <- corFinder(eset.pair = esets[c(2, 1)])
expect_equal(cor1, t(cor2), label = "Check corFinder function")
cor1 <- corFinder(eset.pair = esets, use.ComBat = FALSE)
cor2 <- corFinder(eset.pair = esets[c(2, 1)], use.ComBat = FALSE)
expect_equal(cor1, t(cor2), label = "Check corFinder function 2")
cor1 <- corFinder(eset.pair = esets[c(1, 1)])
cor2 <- corFinder(eset.pair = esets[c(1, 1)], use.ComBat = FALSE)
expect_equal(cor1, cor2, label = "Check corFinder function 3")
##Check missing values:
exprs(esets[[1]])[1:10, 1:5] <- NA
expect_message(doppelgangR(esets[1:2]), regexp = "Finalizing", label = "check missing values 1")
## More missing values:
exprs(esets[[1]])[1:10, 1:8] <- NA
expect_warning(doppelgangR(esets[1:2]), regexp = "10 rows with more than 50 % entries missing", label = "check missing values 2")
## More missing values:
exprs(esets[[1]])[1:10, 1:11] <- NA
expect_warning(doppelgangR(esets[1:2]), regexp = "mean imputation used for these rows", label = "check missing values 3")
## infinite values:
exprs(esets[[1]])[14, 1] <- -Inf
exprs(esets[[1]])[15, 2] <- Inf
expect_warning(doppelgangR(esets[1:2]), regexp = "Inf with min/max expression values for dataset m", label = "check missing values 4")
##------------------------------------------
## Smoking guns only with cache=TRUE
##------------------------------------------
expect_warning(
dop <-
doppelgangR(
esets,
corFinder.args = NULL,
phenoFinder.args = NULL,
manual.smokingguns = "id"
),
label = "Smoking guns only with cache=TRUE"
)
expect_equal(summary(dop)[, 1], "m:m5", label = "Smoking guns only with cache=TRUE 2")
expect_equal(summary(dop)[, 2], "n:n4", label = "Smoking guns only with cache=TRUE 3")
##------------------------------------------
## Identical ExpressionSets
##------------------------------------------
expect_warning(df1 <-
summary(doppelgangR(esets[[1]], cache.dir = NULL)), label = "Identical ExpressionSets 1")
expect_true(df1$sample1 == "m2", label = "Identical ExpressionSets 2")
expect_true(df1$sample2 == "m3", label = "Identical ExpressionSets 3")
##
df2 <- summary(doppelgangR(esets[[2]], cache.dir = NULL))
expect_true(all(df2$sample1 == "n2"), label = "Identical ExpressionSets 4")
expect_true(all(df2$sample2 == "n3"), label = "Identical ExpressionSets 4b")
##
expect_warning(df5 <-
summary(doppelgangR(
list(eset1 = esets[[1]], eset2 = esets[[2]]), cache.dir = NULL
)), label = "Identical ExpressionSets 5")
expect_warning(df6 <-
summary(doppelgangR(esets, cache.dir = NULL)), label = "Identical ExpressionSets 6")
expect_identical(df5[,-1:-2], df6[,-1:-2], label = "Identical ExpressionSets 7")
expect_identical(sub("eset2", "n", sub("eset1", "m", df5$sample1)), df6$sample1, label = "Identical ExpressionSets 8")
expect_identical(sub("eset2", "n", sub("eset1", "m", df5$sample2)), df6$sample2, label = "Identical ExpressionSets 9")
## with zero-column pData:
expect_warning(withpheno <-
summary(doppelgangR(esets)), label = "with zero-column pData")
esets3 <- esets
pData(esets3[[1]]) <- pData(esets3[[1]])[, 0]
expect_warning(withoutpheno <-
summary(doppelgangR(esets3)),
regexp = "expression values for dataset m",
label = "with zero-column pData 2")
pData(esets3[[2]]) <- pData(esets3[[2]])[, 0]
expect_warning(withoutpheno2 <-
summary(doppelgangR(esets3)),
regexp = "with min/max expression values for dataset m",
label = "with zero-column pData 3")
withoutpheno3 <- withoutpheno[!withoutpheno$pheno.doppel,]
withoutpheno4 <- withoutpheno2[!withoutpheno2$pheno.doppel,]
expect_identical(withoutpheno[, 1:4], withoutpheno3[, 1:4], label = "with zero-column pData 4")
expect_identical(withoutpheno2[, 1:4], withoutpheno4[, 1:4], label = "with zero-column pData 5")
esets4 <- esets
for (i in 1:length(esets4))
pData(esets4[[i]]) <- pData(esets4[[i]])[1]
expect_warning(doppelgangR(esets4[[1]]), label = "with zero-column pData 6")
expect_s4_class(doppelgangR(esets4[[2]]), "DoppelGang")
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