context("Check to Update")
test_that("Check the functions which update the values on the datasets.", {
data("midsamp")
data("minsamp")
data("new_counts")
primerg<- midsamp%>%
priming_graph(competing_count = Gene_expression, miRNA_count = miRNA_expression)
seconderg<- primerg%>%
update_how(sample_n(midsamp, 1)$Genes, 2)
minsamp%>%
priming_graph(competing_count = Competing_expression, miRNA_count = miRNA_expression)%>%
update_variables(new_counts)%>%
as_tibble()%>%
select(count_current)%>%
pull()-> min_res
new_counts%>%
mutate(Competing= ifelse(Competing== "Gene2", "Gene16", Competing))->new_counts2
new_counts%>%
mutate(miRNA= ifelse(miRNA== "Mir2", "Mir3", miRNA))-> new_counts3
expect_equal(ncol(as_tibble(primerg%>%activate(nodes))), 7)
expect_equal(ncol(as_tibble(seconderg%>%activate(nodes))), 7)
expect_equal(ncol(as_tibble(seconderg%>%activate(edges))), ncol(as_tibble(primerg%>%activate(edges))))
expect_equal(min_res, c(10000, 20000, 5000, 10000, 5000, 10000, 1000, 2000))
expect_error(primerg%>%update_how("Gene30", 2), "Given node name Gene30 was not found! Please check it.")
expect_error(primerg%>%update_how("Gene3", -2), "Fold change should not be less than zero.\n Please use decimal values for decrease. e.g. 0.5 for 2-fold decrease")
expect_error(minsamp%>%
priming_graph(competing_count = Competing_expression, miRNA_count = miRNA_expression)%>%
update_variables(current_counts=new_counts2),
"Current values include one or more different node name!")
expect_error(minsamp%>%
priming_graph(competing_count = Competing_expression, miRNA_count = miRNA_expression)%>%
update_variables(current_counts=new_counts3),
"Current values include one or more different node name!")
})
test_that("Check the functions which update the values on the datasets with interaction factors.", {
data("midsamp")
primergf<- midsamp%>%
priming_graph(competing_count = Gene_expression, miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)
secondergf<- primergf%>%
update_how(sample_n(midsamp, 1)$Genes, 2)
midsamp%>%
priming_graph(competing_count = Gene_expression, miRNA_count = miRNA_expression)%>%
update_how("Gene15", 0.3)%>%
simulate(10)%>%
as_tibble()%>%
dplyr::mutate(count_current= round(count_current, 2))%>%
dplyr::select(count_current)%>%
pull() -> outputall
expect_equal(ncol(as_tibble(secondergf%>%activate(edges))), ncol(as_tibble(primergf%>%activate(edges))))
expect_equal(outputall, c(10000, 10000, 5000, 9974, 5000, 9976, 6981, 2992, 5999, 2000, 7999, 1500, 500, 6983, 1021, 1995, 5984, 1000, 6500, 4800, 1000, 2000, 3000, 5000))
})
test_that("Check zero values", {
data("midsamp")
midsamp%>%
priming_graph(competing_count = Gene_expression, miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)%>%
update_how("Gene6", 0, knockdown = FALSE)%>%
simulate(10, knockdown = FALSE)%>%
as_tibble()%>%
dplyr::mutate(count_current= round(count_current, 2))%>%
dplyr::select(count_current)%>%
pull() -> gene6_zero
expect_equal(gene6_zero, c(10000, 10000,5000, 9952,5000, 708,6960, 3000,5970, 1995,7936, 1492, 498, 6752,2989, 1999,5949, 1000,6486, 4798,1000,2000, 3000,5000))
})
test_that("Check gene knock down", {
data("midsamp")
midsamp %>%
priming_graph(competing_count = Gene_expression,
miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)%>%
update_how("Gene2", how=0, knockdown = TRUE)%>%
as_tibble()%>%
filter( name == "Gene2")%>%
select(count_current)%>%
pull()-> res1
midsamp %>%
priming_graph(competing_count = Gene_expression,
miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)%>%
update_how("Gene2", how=0, knockdown = TRUE)%>%
simulate(cycle = 5, knockdown = TRUE)%>%
as_tibble()%>%
filter( name == "Gene2")%>%
select(count_current)%>%
pull()-> res2
midsamp %>%
priming_graph(competing_count = Gene_expression,
miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)%>%
update_how("Gene2", how=0, knockdown = TRUE)%>%
simulate(cycle = 5, knockdown = TRUE)%>%
activate(edges)%>%
as_tibble()%>%
filter( Competing_name == "Gene2")%>%
select(comp_count_pre)%>%
pull()-> res3
midsamp %>%
priming_graph(competing_count = Gene_expression,
miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)%>%
update_how("Gene2", how=0, knockdown = TRUE)%>%
simulate(cycle = 5, knockdown = TRUE)%>%
activate(edges)%>%
as_tibble()%>%
filter(Competing_name == "Gene2")%>%
select(comp_count_current)%>%
pull()-> res4
expect_equal(res1, res2, 0)
expect_equal(res3, res4, 0)
midsamp %>%
priming_graph(competing_count = Gene_expression,
miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)%>%
update_how("Gene2", how=0, knockdown = TRUE)%>%
simulate(cycle = 5, knockdown = TRUE)%>%
activate(edges)%>%
as_tibble()%>%
filter( Competing_name == "Gene2")%>%
select(comp_count_list)%>%
unnest(comp_count_list)%>%
pull()-> sim_res
expect_equal(sim_res, c(10000, 0, 0, 0, 0, 0))
midsamp %>%
priming_graph(competing_count = Gene_expression,
miRNA_count = miRNA_expression, aff_factor = c(seeds, Energy), deg_factor = targeting_region)%>%
update_how("Gene2", how=0, knockdown = FALSE)%>%
simulate(cycle = 5, knockdown = FALSE)%>%
as_tibble()%>%
filter(name== "Gene2")%>%
select(count_current)%>%
round(digits = 2)%>%
pull()->res5
expect_equal(res5, 7)
})
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