@@ -16,8 +16,8 @@

 ##'   label of each sample in `colData(x)`.

 ##' @param class1,class2 A string specifying the class label of samples to be

 ##'   compared. Must be one of `group` levels. No need to be specified if

-##'   `group` has only two levels. This function evaluates the contrast: class2

-##'   - class1.

+##'   `group` has only two levels. This function evaluates the contrast:

+##'   `class2 - class1`.

 ##' @param covariates A vector indicating the names of variables to be included

 ##'   in the model as covariates. The covariates must be found in `colData(x)`.

 ##' @param confint A logical specifying whether 95% confidence intervals of

@@ -40,9 +40,9 @@

 ##' @param fc A numeric value specifying a minimum fold-change to be required.

 ##'   If specified, the function output only includes metabolic features with

 ##'   absolute fold-change greater than `fc`.

-##' @param lfc A numeric value specifying a minimum log2-fold-change required,

-##'   equal to log2(fc). `fc` and `lfc` are alternative ways to specify a

-##'   fold-change cut-off and, if both are specified, then `fc` take precedence.

+##' @param lfc A numeric value specifying a minimum log-fold-change required.

+##'   `fc` and `lfc` are alternative ways to specify a fold-change cut-off and,

+##'   if both are specified, then `fc` take precedence.

 ##' @param ... Additional arguments passed to [limma::eBayes].

 ##' @return A data.frame with a row for the metabolic features and the following

 ##'   columns:

@@ -113,7 +113,6 @@ compareSamples <- function(x, i, group,

     stop(setdiff(c(class1, class2), levels(cdat[[group]])),

          " is not the levels of '", group, "'")

   }

-  message(paste0("Contrast: ", class2, " - ", class1))

   ## Check covariates are found in cdat

   if (!all(covariates %in% colnames(cdat))) {

     stop("`colData(x)` does not have a column(s): ",

@@ -131,6 +130,7 @@ compareSamples <- function(x, i, group,

   fit <- contrasts.fit(fit, contrasts = contrast)

   fit <- eBayes(fit, ...)

   ## Output

+  message(paste0("Contrast: ", class2, " - ", class1))

   topTable(fit, number = number, adjust.method = adjust.method,

            sort.by = sort.by, resort.by = resort.by, p.value = p.value,

            fc = fc, lfc = lfc, confint = confint)

@@ -33,10 +33,8 @@ label of each sample in \code{colData(x)}.}

 \item{class1, class2}{A string specifying the class label of samples to be

 compared. Must be one of \code{group} levels. No need to be specified if

-\code{group} has only two levels. This function evaluates the contrast: class2

-\itemize{

-\item class1.

-}}

+\code{group} has only two levels. This function evaluates the contrast:

+\code{class2 - class1}.}

 \item{covariates}{A vector indicating the names of variables to be included

 in the model as covariates. The covariates must be found in \code{colData(x)}.}

@@ -68,9 +66,9 @@ Only metabolic features with lower p-values are listed.}

 If specified, the function output only includes metabolic features with

 absolute fold-change greater than \code{fc}.}

-\item{lfc}{A numeric value specifying a minimum log2-fold-change required,

-equal to log2(fc). \code{fc} and \code{lfc} are alternative ways to specify a

-fold-change cut-off and, if both are specified, then \code{fc} take precedence.}

+\item{lfc}{A numeric value specifying a minimum log-fold-change required.

+\code{fc} and \code{lfc} are alternative ways to specify a fold-change cut-off and,

+if both are specified, then \code{fc} take precedence.}

 \item{...}{Additional arguments passed to \link[limma:ebayes]{limma::eBayes}.}

 }

@@ -4,6 +4,10 @@ data(faahko_se)

 faahko_sub <- faahko_se[1:50, ]

 expect_true(anyNA(assay(faahko_sub, 1)))

+## Add some covariates

+faahko_sub$covar1 <- rep(c("A", "A", "A", "B", "B", "B"), 2)

+faahko_sub$covar2 <- rep(c("C", "D"), 6)

+

 imputation_methods <- c("knn", "rf", "bpca", "QRILC", "MLE",

                         "MinDet", "MinProb", "min", "zero",

                         "mixed", "nbavg", "with", "none")

@@ -2,7 +2,7 @@ test_that("clusterFeatures works.", {

     ## Incorrect rtime varname

     expect_error(

-        clusterFeatures(fahhko_se, i = "knn_vsn", rtime_var = "zzz")

+        clusterFeatures(faahko_sub, i = "knn_vsn", rtime_var = "zzz")

     )

     ## Function generates "rtime_group" and "feature_group" in rowData

new file mode 100644

@@ -0,0 +1,43 @@

+test_that("compareSamples works.", {

+

+  ## Incorrect group varname

+  expect_error(

+    compareSamples(faahko_sub, i = "knn_vsn", group = "sample_grp")

+  )

+

+  ## Incorrect group level specification

+  expect_error(

+    compareSamples(faahko_sub, i = "knn_vsn", group = "sample_group",

+                   class1 = "WW", class2 = "KK")

+  )

+  expect_error(

+    compareSamples(faahko_sub, i = "knn_vsn", group = "sample_group",

+                   class1 = "WT", class2 = "KK")

+  )

+  expect_error(

+    compareSamples(faahko_sub, i = "knn_vsn", group = "sample_group",

+                   class1 = "WW", class2 = "KK")

+  )

+

+  ## Incorrect covariates varnames

+  expect_error(

+    compareSamples(faahko_sub, i = "knn_vsn", group = "sample_group",

+                   covariates = c("covar3", "covar4")) ## not covar3 and covar4

+  )

+  expect_error(

+    compareSamples(faahko_sub, i = "knn_vsn", group = "sample_group",

+                   covariates = c("covar1", "covar3")) ## not covar3

+  )

+

+  ## Confidence interval when confint is enabled

+  mod1 <- compareSamples(faahko_sub, i = "knn_vsn", group = "sample_group",

+                        confint = TRUE)

+  expect_s3_class(mod1, "data.frame")

+  expect_true(all(c("CI.L", "CI.R") %in% names(mod1)))

+

+  mod2 <- compareSamples(faahko_sub, i = "knn_vsn", group = "sample_group",

+                         confint = FALSE)

+  expect_s3_class(mod2, "data.frame")

+  expect_false(all(c("CI.L", "CI.R") %in% names(mod2)))

+

+})