tests/testthat/test-5getCrossAssociation.R
In FelixErnst/mia: Microbiome analysis
context("getCrossAssociation")
test_that("getCrossAssociation", {
# Try 5 times to fetch the data
for(i in seq_len(5) ){
mae <- tryCatch(
{
# Try to fetch the data
microbiomeDataSets::peerj32()
},
error = function(cond) {
# If it was not possible to fetch the data, give FALSE
return(NULL)
}
)
# Break if mae has the data
if( !is.null(mae) ){
break
}
}
# Run tests if the data fetch was successful
if( !is.null(mae) ){
############################### Test input ###############################
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
altexp1 = 1,
altexp2 = NULL,
method = "spearman", mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
altexp1 = FALSE,
altexp2 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
altexp2 = "test",
altexp1 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = TRUE,
assay.type1 = "counts",
assay.type2 = "counts",
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "test",
assay.type2 = "counts",
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = 1,
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = FALSE,
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = TRUE,
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = 1,
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 2,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = TRUE,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = 2,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = TRUE,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = 1,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = 1,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = TRUE,
verbose = 1))
expect_error(getCrossAssociation(mae[[1]],
assay(mae1[[2]]),
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = TRUE,
verbose = 1))
expect_error(getCrossAssociation(mae[[1]],
NULL,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = TRUE,
verbose = 1))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
col.var1 = FALSE,
col.var2 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
col.var1 = NULL,
col.var2 = 1,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
col.var1 = "test",
col.var2 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
############################# Test input end #############################
# Test that association is calculated correctly with numeric data
# Result from
# d1 <- t(assay(mae[[1]]))
# d2 <- t(assay(mae[[2]]))
# cc <- microbiome::associate(d1, d2, method='pearson', mode= "table")
cor_compare <- c(-0.67473156, 0.19980484, -0.19942102, -0.17346641, -0.16954081,
-0.15477367, -0.15279005, 0.08792788, 0.08443186)
p_adj_compare <- c(0.001247967, 0.784472862, 0.785332288, 0.830362548,
0.836148425, 0.856762552, 0.859203260, 0.938444366, 0.942610008)
# Calculate correlation
cor <- getCrossAssociation(mae,
method = "pearson",
p.adj.threshold = NULL,
show.warnings = FALSE,
test.signif = TRUE)
# Take only specific taxa and lipids
df <- cor[cor$Var1 %in% c("Fusobacteria", "Campylobacter", "Actinomycetaceae") &
cor$Var2 %in% c("PE(48:7)", "TG(50:0)", "SM(d18:1/18:0)"), ]
# Sort the data, so that lowest p-values are first
df <- df[order(df$p_adj), ]
# Correlation values and p-values should be the same
expect_equal(round(df$cor, 7), round(cor_compare, 7))
expect_equal(round(df$p_adj, 7), round(p_adj_compare, 7))
# Test that association is calculated correctly with factor data
# Create a dummy data
assay1 <- matrix(rep(c("A", "B", "B"), 20*30/3),
nrow = 20, ncol = 30)
assay2 <- matrix(rep(c("A", "B", "A", "A", "B", "B"), 20*30/6),
nrow = 20, ncol = 30)
# Reference
# ref <- c()
# for(i in 1:20){
# ref <- c(ref, GoodmanKruskal::GKtau(assay1[i, ], assay2[i, ])$tauxy)
# }
ref <- c(0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25,
1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00)
# Calculate values for 20 feature-pairs
result <- c()
for(i in 1:20){
result <- c(result, .calculate_gktau(assay1[i, ], assay2[i, ])$estimate)
}
# Values should be the same
expect_equal(round(result, 4), round(ref, 4))
mae_sub <- mae[1:10, 1:10]
# Test that output is in correct type
expect_true( is.data.frame(
getCrossAssociation(mae_sub, p.adj.threshold = NULL,
show.warnings = FALSE, test.signif = TRUE)) )
expect_true( is.data.frame(getCrossAssociation(mae_sub,
show.warnings = FALSE)) )
# There should not be any p-values that are under 0
expect_true( is.null(
getCrossAssociation(mae_sub, p.adj.threshold = 0,
show.warnings = FALSE,
test.signif = TRUE)) )
# Test that output is in correct type
expect_true( is.list(
getCrossAssociation(mae_sub, mode = "matrix",
p.adj.threshold = NULL,
show.warnings = FALSE,
test.signif = TRUE)) )
expect_true( is.matrix(getCrossAssociation(mae_sub,
mode = "matrix",
show.warnings = FALSE)) )
# There should not be any p-values that are under 0
expect_true( is.null(
getCrossAssociation(mae_sub,
p.adj.threshold = 0,
mode = "matrix",
show.warnings = FALSE,
test.signif = TRUE)) )
# When correlation between same assay is calculated, calculation is made faster
# by not calculating duplicates
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = "TRUE",
test.signif = TRUE))
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = 1,
test.signif = TRUE))
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = NULL,
test.signif = TRUE))
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = c(TRUE, TRUE),
test.signif = TRUE))
time <- system.time(
cor <- getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = TRUE,
test.signif = TRUE)
)
time2 <- system.time(
cor2 <- getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
test.signif = TRUE)
)
# Get random variables and test that their duplicates are equal
for(i in 1:10 ){
random_var1 <- sample(cor$Var1, 1)
random_var2 <- sample(cor$Var1, 1)
expect_equal(as.numeric(cor[cor$Var1 == random_var1 & cor$Var2 == random_var2, c("cor", "pval", "p_adj")]),
as.numeric(cor[cor$Var1 == random_var2 & cor$Var2 == random_var1, c("cor", "pval", "p_adj")]))
}
expect_equal(cor, cor2)
# Test that symmetric = TRUE was faster
expect_true(time[3] < time2[3])
# Test that paired samples work correctly
tse1 <- mae[[1]]
tse2 <- mae[[1]]
# Convert assay to have random values
mat <- matrix(sample(0:100, nrow(tse2)*ncol(tse2), replace = TRUE),
nrow = nrow(tse2), ncol = ncol(tse2))
colnames(mat) <- colnames(tse2)
rownames(mat) <- rownames(tse2)
assay(tse2) <- mat
# Calculate with paired samples
cor_paired <- getCrossAssociation(tse1,
experiment2 = tse2,
paired = TRUE,
by = 2,
show.warnings = FALSE,
test.signif = TRUE)
# Calculate all pairs
cor <- getCrossAssociation(tse1,
experiment2 = tse2,
by = 2,
show.warnings = FALSE,
test.signif = TRUE)
# Take only pairs that are paired
cor <- cor[cor$Var1 == cor$Var2, ]
rownames(cor) <- NULL
# Should be equal
expect_equal(cor[, c("cor", "pval")],
cor_paired[, c("cor", "pval")])
# Test that result does not depend on names (if there are equal names)
tse <- mae[[1]]
rownames(tse)[1:10] <- rep("Unknown", 10)
cor_table <- getCrossAssociation(tse, show.warnings = FALSE,
test.signif = TRUE)
cor_table_ref <- getCrossAssociation(mae[[1]], show.warnings = FALSE,
test.signif = TRUE)
expect_equal(cor_table[ , 3:5], cor_table_ref[ , 3:5])
mat <- getCrossAssociation(tse, mode = "matrix", show.warnings = FALSE)
expect_true( is.matrix(mat) )
expect_true(nrow(mat) == nrow(tse) && ncol(mat) == nrow(tse))
mat <- getCrossAssociation(tse, mode = "matrix", show.warnings = FALSE,
cor.threshold = 0.8,
filter.self.cor = TRUE)
expect_true(nrow(mat) < nrow(tse) && ncol(mat) < nrow(tse))
# Test user's own function
expect_true( is.data.frame(getCrossAssociation(tse, method = "canberra",
mode = "table",
show.warnings = T,
association.fun = stats::dist) ) )
expect_true( is.matrix( getCrossAssociation(tse, method = "bray",
show.warnings = FALSE,
mode = "matrix",
association.fun = vegan::vegdist,
test.signif = TRUE) ) )
expect_error( getCrossAssociation(tse, method = "bray",
show.warnings = FALSE,
mode = "matrix",
association.fun = DelayedMatrixStats::rowSums2,
test.signif = TRUE) )
# Test that output has right columns
expect_equal(colnames(getCrossAssociation(tse,
show.warnings = FALSE)),
c("Var1", "Var2", "cor"))
expect_equal(colnames(getCrossAssociation(tse, show.warnings = FALSE,
test.signif = TRUE)),
c("Var1", "Var2", "cor", "pval", "p_adj"))
# Test that the table have same information with different levels
tab1 <- getCrossAssociation(tse, show.warnings = FALSE)
tab1_levels1 <- levels(tab1$Var1)
tab1_levels2 <- levels(tab1$Var2)
tab1$Var1 <- as.character(tab1$Var1)
tab1$Var2 <- as.character(tab1$Var2)
tab2 <- getCrossAssociation(tse, show.warnings = FALSE, sort = TRUE)
tab2_levels1 <- levels(tab2$Var1)
tab2_levels2 <- levels(tab2$Var2)
tab2$Var1 <- as.character(tab2$Var1)
tab2$Var2 <- as.character(tab2$Var2)
expect_equal(tab1, tab2)
expect_true( !all(tab1_levels1 == tab2_levels1) )
expect_true( !all(tab1_levels2 == tab2_levels2) )
# Test altexps
altExps(tse) <- splitByRanks(tse)
# Test that output has right columns
expect_equal(getCrossAssociation(tse, tse, show.warnings = FALSE,
altexp1 = 1, altexp2 = "Phylum"),
getCrossAssociation(altExps(tse)[[1]], altExp(tse, "Phylum"),
show.warnings = FALSE))
expect_equal(getCrossAssociation(tse, tse, show.warnings = FALSE,
altexp1 = "Family",
altexp2 = NULL),
getCrossAssociation(altExp(tse, "Family"), tse,
show.warnings = FALSE))
# Test colData_variable
# Check that all the correct names are included
indices <- c("shannon", "gini_simpson")
tse <- estimateDiversity(tse, index = indices)
res <- getCrossAssociation(tse, tse,
assay.type1 = "counts",
col.var2 = indices)
unique_var1 <- unfactor(unique(res$Var1))
unique_var2 <- unfactor(unique(res$Var2))
rownames <- rownames(tse)
expect_true( all(rownames %in% unique_var1) && all(unique_var1 %in% rownames) &&
all(indices %in% unique_var2) && all(unique_var2 %in% indices) )
# Check that assay.type is disabled
res2 <- getCrossAssociation(tse, assay.type1 = "counts",
assay.type2 = "counts",
col.var2 = indices)
expect_equal(res, res2)
colData(tse)[, "test"] <- rep("a")
expect_error(
getCrossAssociation(tse, col.var2 = c("shannon", "test")))
}
})
FelixErnst/mia documentation built on July 15, 2024, 9:21 p.m.
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context("getCrossAssociation")
test_that("getCrossAssociation", {
# Try 5 times to fetch the data
for(i in seq_len(5) ){
mae <- tryCatch(
{
# Try to fetch the data
microbiomeDataSets::peerj32()
},
error = function(cond) {
# If it was not possible to fetch the data, give FALSE
return(NULL)
}
)
# Break if mae has the data
if( !is.null(mae) ){
break
}
}
# Run tests if the data fetch was successful
if( !is.null(mae) ){
############################### Test input ###############################
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
altexp1 = 1,
altexp2 = NULL,
method = "spearman", mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
altexp1 = FALSE,
altexp2 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
altexp2 = "test",
altexp1 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = TRUE,
assay.type1 = "counts",
assay.type2 = "counts",
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "test",
assay.type2 = "counts",
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = 1,
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = FALSE,
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = TRUE,
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = 1,
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 2,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = TRUE,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = 2,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = TRUE,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = 1,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = 1,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = TRUE,
verbose = 1))
expect_error(getCrossAssociation(mae[[1]],
assay(mae1[[2]]),
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = TRUE,
verbose = 1))
expect_error(getCrossAssociation(mae[[1]],
NULL,
experiment1 = 1,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
method = "pearson",
mode = "matrix",
p.adj.method = "fdr",
p.adj.threshold = 0.1,
cor.threshold = NULL,
sort = TRUE,
filter.self.cor = TRUE,
verbose = 1))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
col.var1 = FALSE,
col.var2 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
col.var1 = NULL,
col.var2 = 1,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
expect_error(getCrossAssociation(mae,
experiment1 = 3,
experiment2 = 2,
assay.type1 = "counts",
assay.type2 = "counts",
col.var1 = "test",
col.var2 = NULL,
method = "spearman",
mode = "table",
p.adj.method = "fdr",
p.adj.threshold = 0.05,
cor.threshold = NULL,
sort = FALSE,
filter.self.cor = FALSE,
verbose = TRUE))
############################# Test input end #############################
# Test that association is calculated correctly with numeric data
# Result from
# d1 <- t(assay(mae[[1]]))
# d2 <- t(assay(mae[[2]]))
# cc <- microbiome::associate(d1, d2, method='pearson', mode= "table")
cor_compare <- c(-0.67473156, 0.19980484, -0.19942102, -0.17346641, -0.16954081,
-0.15477367, -0.15279005, 0.08792788, 0.08443186)
p_adj_compare <- c(0.001247967, 0.784472862, 0.785332288, 0.830362548,
0.836148425, 0.856762552, 0.859203260, 0.938444366, 0.942610008)
# Calculate correlation
cor <- getCrossAssociation(mae,
method = "pearson",
p.adj.threshold = NULL,
show.warnings = FALSE,
test.signif = TRUE)
# Take only specific taxa and lipids
df <- cor[cor$Var1 %in% c("Fusobacteria", "Campylobacter", "Actinomycetaceae") &
cor$Var2 %in% c("PE(48:7)", "TG(50:0)", "SM(d18:1/18:0)"), ]
# Sort the data, so that lowest p-values are first
df <- df[order(df$p_adj), ]
# Correlation values and p-values should be the same
expect_equal(round(df$cor, 7), round(cor_compare, 7))
expect_equal(round(df$p_adj, 7), round(p_adj_compare, 7))
# Test that association is calculated correctly with factor data
# Create a dummy data
assay1 <- matrix(rep(c("A", "B", "B"), 20*30/3),
nrow = 20, ncol = 30)
assay2 <- matrix(rep(c("A", "B", "A", "A", "B", "B"), 20*30/6),
nrow = 20, ncol = 30)
# Reference
# ref <- c()
# for(i in 1:20){
# ref <- c(ref, GoodmanKruskal::GKtau(assay1[i, ], assay2[i, ])$tauxy)
# }
ref <- c(0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25,
1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00, 0.25, 1.00)
# Calculate values for 20 feature-pairs
result <- c()
for(i in 1:20){
result <- c(result, .calculate_gktau(assay1[i, ], assay2[i, ])$estimate)
}
# Values should be the same
expect_equal(round(result, 4), round(ref, 4))
mae_sub <- mae[1:10, 1:10]
# Test that output is in correct type
expect_true( is.data.frame(
getCrossAssociation(mae_sub, p.adj.threshold = NULL,
show.warnings = FALSE, test.signif = TRUE)) )
expect_true( is.data.frame(getCrossAssociation(mae_sub,
show.warnings = FALSE)) )
# There should not be any p-values that are under 0
expect_true( is.null(
getCrossAssociation(mae_sub, p.adj.threshold = 0,
show.warnings = FALSE,
test.signif = TRUE)) )
# Test that output is in correct type
expect_true( is.list(
getCrossAssociation(mae_sub, mode = "matrix",
p.adj.threshold = NULL,
show.warnings = FALSE,
test.signif = TRUE)) )
expect_true( is.matrix(getCrossAssociation(mae_sub,
mode = "matrix",
show.warnings = FALSE)) )
# There should not be any p-values that are under 0
expect_true( is.null(
getCrossAssociation(mae_sub,
p.adj.threshold = 0,
mode = "matrix",
show.warnings = FALSE,
test.signif = TRUE)) )
# When correlation between same assay is calculated, calculation is made faster
# by not calculating duplicates
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = "TRUE",
test.signif = TRUE))
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = 1,
test.signif = TRUE))
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = NULL,
test.signif = TRUE))
expect_error(getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = c(TRUE, TRUE),
test.signif = TRUE))
time <- system.time(
cor <- getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
symmetric = TRUE,
test.signif = TRUE)
)
time2 <- system.time(
cor2 <- getCrossAssociation(mae, experiment1 = 1, experiment2 = 1,
show.warnings = FALSE,
test.signif = TRUE)
)
# Get random variables and test that their duplicates are equal
for(i in 1:10 ){
random_var1 <- sample(cor$Var1, 1)
random_var2 <- sample(cor$Var1, 1)
expect_equal(as.numeric(cor[cor$Var1 == random_var1 & cor$Var2 == random_var2, c("cor", "pval", "p_adj")]),
as.numeric(cor[cor$Var1 == random_var2 & cor$Var2 == random_var1, c("cor", "pval", "p_adj")]))
}
expect_equal(cor, cor2)
# Test that symmetric = TRUE was faster
expect_true(time[3] < time2[3])
# Test that paired samples work correctly
tse1 <- mae[[1]]
tse2 <- mae[[1]]
# Convert assay to have random values
mat <- matrix(sample(0:100, nrow(tse2)*ncol(tse2), replace = TRUE),
nrow = nrow(tse2), ncol = ncol(tse2))
colnames(mat) <- colnames(tse2)
rownames(mat) <- rownames(tse2)
assay(tse2) <- mat
# Calculate with paired samples
cor_paired <- getCrossAssociation(tse1,
experiment2 = tse2,
paired = TRUE,
by = 2,
show.warnings = FALSE,
test.signif = TRUE)
# Calculate all pairs
cor <- getCrossAssociation(tse1,
experiment2 = tse2,
by = 2,
show.warnings = FALSE,
test.signif = TRUE)
# Take only pairs that are paired
cor <- cor[cor$Var1 == cor$Var2, ]
rownames(cor) <- NULL
# Should be equal
expect_equal(cor[, c("cor", "pval")],
cor_paired[, c("cor", "pval")])
# Test that result does not depend on names (if there are equal names)
tse <- mae[[1]]
rownames(tse)[1:10] <- rep("Unknown", 10)
cor_table <- getCrossAssociation(tse, show.warnings = FALSE,
test.signif = TRUE)
cor_table_ref <- getCrossAssociation(mae[[1]], show.warnings = FALSE,
test.signif = TRUE)
expect_equal(cor_table[ , 3:5], cor_table_ref[ , 3:5])
mat <- getCrossAssociation(tse, mode = "matrix", show.warnings = FALSE)
expect_true( is.matrix(mat) )
expect_true(nrow(mat) == nrow(tse) && ncol(mat) == nrow(tse))
mat <- getCrossAssociation(tse, mode = "matrix", show.warnings = FALSE,
cor.threshold = 0.8,
filter.self.cor = TRUE)
expect_true(nrow(mat) < nrow(tse) && ncol(mat) < nrow(tse))
# Test user's own function
expect_true( is.data.frame(getCrossAssociation(tse, method = "canberra",
mode = "table",
show.warnings = T,
association.fun = stats::dist) ) )
expect_true( is.matrix( getCrossAssociation(tse, method = "bray",
show.warnings = FALSE,
mode = "matrix",
association.fun = vegan::vegdist,
test.signif = TRUE) ) )
expect_error( getCrossAssociation(tse, method = "bray",
show.warnings = FALSE,
mode = "matrix",
association.fun = DelayedMatrixStats::rowSums2,
test.signif = TRUE) )
# Test that output has right columns
expect_equal(colnames(getCrossAssociation(tse,
show.warnings = FALSE)),
c("Var1", "Var2", "cor"))
expect_equal(colnames(getCrossAssociation(tse, show.warnings = FALSE,
test.signif = TRUE)),
c("Var1", "Var2", "cor", "pval", "p_adj"))
# Test that the table have same information with different levels
tab1 <- getCrossAssociation(tse, show.warnings = FALSE)
tab1_levels1 <- levels(tab1$Var1)
tab1_levels2 <- levels(tab1$Var2)
tab1$Var1 <- as.character(tab1$Var1)
tab1$Var2 <- as.character(tab1$Var2)
tab2 <- getCrossAssociation(tse, show.warnings = FALSE, sort = TRUE)
tab2_levels1 <- levels(tab2$Var1)
tab2_levels2 <- levels(tab2$Var2)
tab2$Var1 <- as.character(tab2$Var1)
tab2$Var2 <- as.character(tab2$Var2)
expect_equal(tab1, tab2)
expect_true( !all(tab1_levels1 == tab2_levels1) )
expect_true( !all(tab1_levels2 == tab2_levels2) )
# Test altexps
altExps(tse) <- splitByRanks(tse)
# Test that output has right columns
expect_equal(getCrossAssociation(tse, tse, show.warnings = FALSE,
altexp1 = 1, altexp2 = "Phylum"),
getCrossAssociation(altExps(tse)[[1]], altExp(tse, "Phylum"),
show.warnings = FALSE))
expect_equal(getCrossAssociation(tse, tse, show.warnings = FALSE,
altexp1 = "Family",
altexp2 = NULL),
getCrossAssociation(altExp(tse, "Family"), tse,
show.warnings = FALSE))
# Test colData_variable
# Check that all the correct names are included
indices <- c("shannon", "gini_simpson")
tse <- estimateDiversity(tse, index = indices)
res <- getCrossAssociation(tse, tse,
assay.type1 = "counts",
col.var2 = indices)
unique_var1 <- unfactor(unique(res$Var1))
unique_var2 <- unfactor(unique(res$Var2))
rownames <- rownames(tse)
expect_true( all(rownames %in% unique_var1) && all(unique_var1 %in% rownames) &&
all(indices %in% unique_var2) && all(unique_var2 %in% indices) )
# Check that assay.type is disabled
res2 <- getCrossAssociation(tse, assay.type1 = "counts",
assay.type2 = "counts",
col.var2 = indices)
expect_equal(res, res2)
colData(tse)[, "test"] <- rep("a")
expect_error(
getCrossAssociation(tse, col.var2 = c("shannon", "test")))
}
})
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