context("General tests for the scone main function")
set.seed(13124)
BiocParallel::register(BiocParallel::bpparam("SerialParam"))
test_that("Test with no real method (only identity)", {
e <- matrix(rpois(10000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
obj <- SconeExperiment(e)
# one combination
res <- scone(obj, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
evaluate=FALSE, run=TRUE, return_norm = "in_memory")
expect_equal(assay(res), e)
# res2 should be the same as res
res2 <- scone(res, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
evaluate=FALSE, run=TRUE, return_norm = "in_memory")
expect_equal(res2, res)
# add more imputations
res <- scone(obj, imputation=list(a=impute_null, b=impute_null), scaling=identity,
k_ruv=0, k_qc=0, evaluate=FALSE, run=TRUE, return_norm = "in_memory")
expect_equal(assay(res), e)
expect_equal(assay(res, 2), e)
# add more scaling
res <- scone(obj, imputation=list(a=impute_null, b=impute_null),
scaling=list(a=identity, b=identity, c=identity), k_ruv=0,
k_qc=0, evaluate=FALSE, run=TRUE)
# add ruv
expect_error(scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity),
k_ruv=3, k_qc=0, evaluate=FALSE, run=FALSE),
"negative controls must be specified")
obj <- SconeExperiment(e, negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)))
obj <- scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=0, evaluate=FALSE, run=FALSE)
obj2 <- scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=0, evaluate=FALSE, run=FALSE)
expect_equal(obj, obj2)
# add qc
expect_error(scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=5, evaluate=FALSE, run=FALSE),
"QC metrics must be specified")
qc_mat <- matrix(rnorm(20), nrow=10)
obj <- SconeExperiment(e, qc=qc_mat, negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)))
res <- scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3, k_qc=2,
evaluate=FALSE, run=TRUE)
# add bio
bio <- rep(1:2, each=5)
expect_error(scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=2, adjust_bio="yes", evaluate=FALSE, run=FALSE),
"if adjust_bio is 'yes' or 'force', 'bio' must be specified")
obj <- SconeExperiment(e, qc=qc_mat,
negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)),
bio = as.factor(bio))
res <- scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=2, adjust_bio="yes", evaluate=FALSE, run=TRUE)
res <- scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=2, adjust_bio="force", evaluate=FALSE, run=TRUE)
# add batch
batch <- rep(1:2, each=5)
expect_error(scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=2, adjust_bio="force", adjust_batch="yes",
evaluate=FALSE, run=FALSE),
"if adjust_batch is 'yes' or 'force', 'batch' must be specified")
obj <- SconeExperiment(e, qc=qc_mat,
negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)),
bio = as.factor(bio), batch=as.factor(batch))
expect_error(scone(obj, imputation=list(impute_null,impute_null),
scaling=list(identity, identity, identity), k_ruv=3,
k_qc=2, adjust_bio="force", adjust_batch="yes",
evaluate=FALSE, run=FALSE),
"Biological conditions and batches are confounded")
batch <- as.factor(rep(1:2, 5))
obj <- SconeExperiment(e, qc=qc_mat,
negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)),
bio = as.factor(bio), batch=as.factor(batch))
res <- scone(obj, imputation=list(a=impute_null, b=impute_null),
scaling=list(a=identity, b=identity, c=identity),
k_ruv=3, k_qc=2, adjust_bio="force", adjust_batch="yes",
evaluate=FALSE, run=TRUE)
## add evaluation
res <- scone(obj, imputation=list(a=impute_null, b=impute_null),
scaling=list(a=identity, b=identity, c=identity),
k_ruv=3, k_qc=2, adjust_bio="force", adjust_batch="yes",
evaluate=TRUE, run=TRUE, eval_kclust=5)
})
test_that("Test imputation and scaling", {
e <- matrix(rpois(1000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
qc_mat <- matrix(rnorm(20), nrow=10)
bio <- gl(2, 5)
batch <- as.factor(rep(1:2, 5))
obj <- SconeExperiment(e, qc=qc_mat,
negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)),
bio = as.factor(bio), batch=as.factor(batch))
# factorial
res <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=3, k_qc=2, adjust_bio="force", adjust_batch="yes",
evaluate=FALSE, run=TRUE)
# nested
batch <- as.factor(c(1, 2, 1, 2, 1, 3, 4, 3, 4, 3))
obj <- SconeExperiment(e, qc=qc_mat,
negcon_ruv=c(rep(TRUE, 100), rep(FALSE, NROW(e)-100)),
bio = as.factor(bio), batch=as.factor(batch))
obj <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=3, k_qc=2, adjust_bio="force", adjust_batch="yes",
evaluate=FALSE, run=FALSE)
obj@scone_params <- obj@scone_params[-(1:5),]
res <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=3, k_qc=2, adjust_bio="force", adjust_batch="yes",
evaluate=FALSE, run=TRUE)
# evaluation
ruv_negcon <- eval_negcon <- eval_poscon <- rep(FALSE, NROW(e))
ruv_negcon[1:10] <- TRUE
eval_negcon[11:20] <- TRUE
eval_poscon[21:30] <- TRUE
obj <- SconeExperiment(e, qc=qc_mat, negcon_ruv=ruv_negcon,
negcon_eval=eval_negcon, poscon=eval_poscon,
bio=as.factor(bio), batch=as.factor(batch))
system.time(res <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=3, k_qc=2, adjust_bio="yes",
adjust_batch="yes", run=TRUE, evaluate=TRUE,
eval_kclust = 2, verbose=FALSE,
return_norm = "in_memory"))
expect_equal(rownames(res@scone_metrics), rownames(res@scone_scores))
expect_equal(rownames(res@scone_metrics), rownames(res@scone_params))
expect_equal(rownames(res@scone_metrics), names(assays(res)))
res2 <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=3, k_qc=2, adjust_bio="yes", adjust_batch="yes", run=TRUE,
evaluate=FALSE, eval_kclust = 2, verbose=FALSE,
return_norm = "in_memory")
norm_ordered <- assays(res2)[names(assays(res))]
expect_equal(norm_ordered, assays(res))
expect_true(is.na(res2@scone_scores))
expect_true(is.na(res2@scone_metrics))
})
test_that("scone works with only one normalization",{
e <- matrix(rpois(1000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
obj <- SconeExperiment(e)
res <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity),
k_ruv=0, k_qc=0, run=TRUE,
evaluate=TRUE, eval_kclust = 2, return_norm = "in_memory")
expect_equal(assay(res), e)
})
test_that("conditional PAM",{
e <- matrix(rpois(1000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
qc_mat <- matrix(rnorm(20), nrow=10)
bio <- gl(2, 5)
batch <- gl(5, 2)
eval_negcon <- eval_poscon <- rep(FALSE, NROW(e))
eval_negcon[11:20] <- TRUE
eval_poscon[21:30] <- TRUE
obj <- SconeExperiment(e, qc=qc_mat, bio=bio,
negcon_eval = eval_negcon, poscon=eval_poscon)
res <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=0, k_qc=0, adjust_bio="yes", run=FALSE,
evaluate=TRUE, eval_kclust = 2, stratified_pam = TRUE)
obj <- SconeExperiment(e, qc=qc_mat, bio=bio, batch=batch,
negcon_eval = eval_negcon, poscon=eval_poscon)
expect_error(res <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=0, k_qc=0, adjust_bio="yes", adjust_batch="yes", run=FALSE,
evaluate=TRUE, eval_kclust = 6, stratified_pam = TRUE),
"For stratified_pam, max 'eval_kclust' must be smaller than bio-cross-batch stratum size")
obj <- SconeExperiment(e, qc=qc_mat, negcon_eval = eval_negcon, poscon=eval_poscon)
expect_error(res <- scone(obj, imputation=list(none=impute_null),
scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
k_ruv=0, k_qc=0, run = FALSE,
evaluate = TRUE,
eval_kclust = 6, stratified_pam = TRUE),
"For stratified_pam, bio and/or batch must be specified")
})
test_that("if bio=no bio is ignored", {
e <- matrix(rpois(10000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
bio <- gl(2, 5)
obj1 <- SconeExperiment(e)
obj2 <- SconeExperiment(e, bio=bio)
res1 <- scone(obj1, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
adjust_bio = "no", eval_kclust = 3, return_norm = "in_memory")
res2 <- scone(obj2, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
adjust_bio = "no", eval_kclust = 3, return_norm = "in_memory")
expect_equal(assays(res1), assays(res2))
})
test_that("if batch=no batch is ignored", {
e <- matrix(rpois(10000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
batch <- gl(2, 5)
obj1 <- SconeExperiment(e)
obj2 <- SconeExperiment(e, batch=batch)
res1 <- scone(obj1, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
adjust_batch = "no", eval_kclust = 3, return_norm = "in_memory")
res2 <- scone(obj2, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
adjust_batch = "no", eval_kclust = 3, return_norm = "in_memory")
expect_equal(assays(res1), assays(res2))
})
test_that("batch and bio can be confounded if at least one of adjust_bio or adjust_batch is no", {
e <- matrix(rpois(10000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
obj <- SconeExperiment(e, batch=gl(2, 5), bio=gl(2, 5))
expect_warning(scone(obj, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
adjust_batch = "yes", eval_kclust = 3),
"Biological conditions and batches are confounded.")
expect_warning(scone(obj, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0,
adjust_bio = "yes", eval_kclust = 3),
"Biological conditions and batches are confounded.")
})
test_that("batch and bio can contain NA", {
e <- matrix(rpois(10000, lambda = 5), ncol=10)
rownames(e) <- as.character(1:nrow(e))
colnames(e) <- paste0("Sample", 1:ncol(e))
batch <- gl(2, 5)
bio <- gl(5, 2)
obj <- SconeExperiment(e, batch=batch, bio=bio)
res1 <- scone(obj, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0, evaluate = TRUE,
adjust_batch = "no", eval_kclust = 3)
batch[1] <- NA
bio[2] <- NA
obj <- SconeExperiment(e, batch=batch, bio=bio)
res2 <- scone(obj, imputation=impute_null, scaling=identity, k_ruv=0, k_qc=0, evaluate = TRUE,
adjust_batch = "no", eval_kclust = 3)
expect_true(!is.na(res2@scone_metrics[,"BIO_SIL"]))
expect_true(!is.na(res2@scone_metrics[,"BATCH_SIL"]))
expect_false(all(res1@scone_metrics[,"BIO_SIL"] == res2@scone_metrics[,"BIO_SIL"]))
expect_false(all(res1@scone_metrics[,"BATCH_SIL"] == res2@scone_metrics[,"BATCH_SIL"]))
})
