DRAFT: Test/simplify transformation tests

tests/testthat/test-5transformCounts.R

@@ -19,12 +19,8 @@ test_that("transformAssay", {
                      check.attributes = FALSE)
 
         ############################# RELATIVE ABUNDANCE #######################
-        # Calculates relative abundances. Should be equal.
-        expect_equal(as.matrix(assays(mia::transformAssay(tse, method = "relabundance"))$relabundance),
-                     apply(as.matrix(assay(tse,"counts")), 2, FUN=function(x){
-                         x/sum(x)
-                     }), check.attributes = FALSE)
-
+        # Calculation tests against vegan at later section
+        # Tests validity of assay name
         mat <- matrix(1:60, nrow = 6)
         df <- DataFrame(n = c(1:6))
         expect_error(transformAssay(
@@ -119,73 +115,32 @@ test_that("transformAssay", {
         expect_true(all.equal(as.matrix(ass[19:20, 17:18]), normalized_counts, check.attributes = FALSE))
 
         ########################## PA ##########################################
-        # Calculates pa transformation. Should be equal.
+        # Calculates pa transformation. Validate format.
         actual <- assay(mia::transformAssay(tse, method = "pa"),"pa")
-        expect_equal(as.vector(actual),
-                     as.integer(as.matrix(assay(tse, "counts")) > 0),
-                     check.attributes = FALSE)
         expect_equal(typeof(actual),"double")
         expect_true(all(actual == 1 | actual == 0))
 
-        # Tests transformAssay(MARGIN = "features"), calculates pa transformation. Should be equal.
+        # .. feature wise
         actual <- assay(mia::transformAssay(tse, MARGIN = "features", method = "pa"),"pa")
-        expect_equal(as.vector(actual),
-                     as.integer(t(as.matrix(t(assay(tse, "counts"))) > 0)))
         expect_equal(typeof(actual),"double")
         expect_true(all(actual == 1 | actual == 0))
 
-        ######################## HELLINGER #####################################
-        # Calculates Hellinger transformation. Should be equal.
-        # Calculates relative abundance table
-        relative <- assays(mia::transformAssay(tse, method = "relabundance", name = "relative"))$relative
-
-        expect_equal(as.matrix(assays(mia::transformAssay(tse, method = "hellinger", name = "test_123"))$test_123),
-                     apply(as.matrix(relative), 2, FUN=function(x){
-                         sqrt(x)
-                     }), check.attributes = FALSE)
-
         ############################### CLR ####################################
-        # Calculates clr-transformation. Should be equal.
-
         # Random pseudocount
         pseudonumber <- runif(1, 1, 100)
 
         tse <- transformAssay(tse, method = "relabundance")
-        # Calculates relative abundance table
-        relative <- assay(tse, "relabundance")
-        relative <- relative + pseudonumber
-        # Tests clr
-        # Calc CLRs
-        mat <- assays(mia::transformAssay(tse, assay.type = "relabundance",
-                                           method = "clr", pseudocount = pseudonumber))$clr
-        mat_comp <- apply(as.matrix(relative), 2, FUN=function(x){
-            log(x) - mean(log(x))
-        })
-        # Remove attributes since vegan adds additional ones
-        attributes(mat) <- NULL
-        attributes(mat_comp) <- NULL
-        # Compare
-        expect_equal(mat, mat_comp)
-
-        # Expect that error occurs
-        expect_error(mia::transformAssay(tse, method = "clr"))
 
         # Expect that error does not occur
         tse <- mia::transformAssay(tse, method = "rclr")
 
         # Tests transformAssay, tries to calculate clr. Should be an error, because of zeros.
         expect_error(mia::transformAssay(tse, method = "clr"))
 
-        # Tests that clr robust gives values that are approximately same if only
-        # one value per sample are changed to zero
         tse <- transformAssay(tse, method = "relabundance")
-        # Adds pseudocount
-        assay(tse, "test") <- assay(tse, "relabundance") + 1
-        assay(tse, "test2") <- assay(tse, "test")
+        # Adds pos/neg pseudocount
         assay(tse, "neg_values") <- assay(tse, "counts") - 2
         assay(tse, "na_values") <- assay(tse, "counts") + 2
-        # First row is zeroes
-        assay(tse, "test2")[1, ] <- 0
         # One missing value
         assay(tse, "na_values")[4, 5] <- NA
 
@@ -204,11 +159,11 @@ test_that("transformAssay", {
                                     assay.type = "neg_values", pseudocount = TRUE))
 
         # Expect pseudocount to be half of min value when NA values present
-        test3 <- tmp
-        assay(test3, "na_values") <- assay(test3, "counts")
-        assay(test3, "na_values")[4, 5] <- NA
+        test2 <- tmp
+        assay(test2, "na_values") <- assay(test2, "counts")
+        assay(test2, "na_values")[4, 5] <- NA
         expect_warning(
-        actual <- transformAssay(test3, method = "relabundance",
+        actual <- transformAssay(test2, method = "relabundance",
                                 assay.type = "na_values", pseudocount = TRUE)
         )
         value <- attr(assay(actual, "relabundance"), "parameters")[["pseudocount"]]
@@ -272,18 +227,6 @@ test_that("transformAssay", {
         # Expect that assay contains count and rank table
         expect_true(all(c("counts", "rank") %in% assayNames(tse_rank)))
 
-        for(i in c(1:10)){
-            # Gets columns from 'rank' table
-            ranks <- assay(tse_rank, "rank")[,i]
-            # Gets columns from 'counts' table, and calculates ranks
-            counts_compare <- assay(tse_rank, "counts")[,i]
-            counts_compare[counts_compare == 0] <- NA
-            ranks_compare <- rank(counts_compare, na.last = "keep")
-            ranks_compare[is.na(ranks_compare)] <- 0
-            # Expect that they are equal
-            expect_equal(ranks, ranks_compare, check.attributes = FALSE)
-        }
-
         # Calculates rank with pseudocount
         tse_rank_pseudo <- transformAssay(tse, method = "rank", pseudocount = runif(1, 0, 1000))
         # Pseudocount should not change the rank
@@ -295,15 +238,13 @@ test_that("transformAssay", {
 
         ############################## Z TRANSFORMATION ########################
         # Calculates Z-transformation for features
-        xx <- t(scale(t(as.matrix(assay(tse, "counts")))))
         expect_warning(z_assay <- assay(
             mia::transformAssay(
                 tse, method = "standardize", MARGIN = "features",
                 pseudocount = 1),
             "standardize"))
-        expect_equal(max(abs(z_assay - xx), na.rm=TRUE), 0,
-                     tolerance = 1e-14, check.attributes = FALSE)
 
+        ####################### Test equality to vegan #########################
         # Test that transformations are equal to ones directly from vegan
         # clr
         tse <- transformAssay(tse, method = "relabundance")
@@ -313,9 +254,9 @@ test_that("transformAssay", {
         compare <- vegan::decostand(assay(tse, "relabundance"), method = "clr",
                                     pseudocount = 4, MARGIN = 2)
         expect_equal(actual, compare)
+
         # rclr
         tse <- transformAssay(tse, assay.type = "relabundance", method = "rclr")
-
         actual <- assay(tse, "rclr")
         # mia has additional pseudocount parameter for all methods
         attr(actual, "parameters")$pseudocount <- NULL
@@ -351,6 +292,35 @@ test_that("transformAssay", {
                                     MARGIN = 2)
         compare <- t(compare)
         expect_equal(na.omit(actual), na.omit(compare))
+
+        # rank
+        tse <- transformAssay(tse, assay.type = "counts", method = "rank")
+        actual <- assay(tse, "rank")
+        attr(actual, "parameters")$pseudocount <- NULL
+        compare <- vegan::decostand(assay(tse, "counts"), method = "rank",
+                                    MARGIN = 2)
+        expect_equal(actual, compare)
+
+        # pa
+        tse <- transformAssay(tse, assay.type = "counts", method = "pa")
+        actual <- assay(tse, "pa")
+        compare <- vegan::decostand(assay(tse, "counts"), method = "pa",
+                                    MARGIN = 2)
+        expect_equal(actual, compare, check.attributes = FALSE)
+        # .. feature wise
+        tse <- transformAssay(tse, MARGIN = "features", method = "pa")
+        actual <- assay(tse, "pa")
+        compare <- vegan::decostand(assay(tse, "counts"), method = "pa",
+                                    MARGIN = 1)
+        expect_equal(actual, compare, check.attributes = FALSE) 
+
+        # Standardize
+        tse <- transformAssay(tse, method = "standardize")
+        actual <- assay(tse, "standardize")
+        compare <- vegan::decostand(assay(tse, "counts"), method = "standardize",
+                                    MARGIN = 2)
+        expect_equal(actual, compare,
+                     check.attributes = FALSE)
 
         # Check that transformation is applied to altExps
         expect_error(transformAssay(tse, altexp = "Phylum"))
@@ -372,7 +342,7 @@ test_that("transformAssay", {
         test <- altExp(test, "Genus")
         expect_equal(assay(test, "relabundance"), assay(ref, "relabundance"))
 
-        # Check that philt transformation works
+        # Check that philr transformation works
         skip_if_not_installed("philr")
         data("GlobalPatterns")
         tse <- GlobalPatterns