improve diff abund api

NAMESPACE

@@ -32,7 +32,7 @@ importFrom(microbiomeComputations,Comparator)
 importFrom(microbiomeComputations,DifferentialAbundanceResult)
 importFrom(microbiomeComputations,alphaDiv)
 importFrom(microbiomeComputations,betaDiv)
-importFrom(microbiomeComputations,differentialAbundance)
+importFrom(microbiomeComputations,internalDiffAbund)
 importFrom(microbiomeComputations,rankedAbundance)
 importFrom(microbiomeComputations,selfCorrelation)
 importFrom(phyloseq,phyloseq)

R/method-differentialAbundance.R

@@ -94,6 +94,16 @@ buildBinaryComparator <- function(covariate, groupAValue, groupBValue) {
 #'        verbose=FALSE
 #' )
 #' 
+#' ## this case of categorical variables also has a 'short cut'
+#' diffAbundOutput <- MicrobiomeDB::differentialAbundance(
+#'        getCollection(microbiomeData::DiabImmune, '16S (V4) Genus'), 
+#'        "country", 
+#'        groupA = "Russia",
+#'        groupB = c("Finland","Estonia"),
+#'        method='Maaslin2', 
+#'        verbose=FALSE
+#' )
+#' 
 #' ## a categorical variable with 2 values
 #' diffAbundOutput <- MicrobiomeDB::differentialAbundance(
 #'        getCollection(microbiomeData::DiabImmune, '16S (V4) Genus'),
@@ -105,15 +115,17 @@ buildBinaryComparator <- function(covariate, groupAValue, groupBValue) {
 #' @param covariate character vector giving the name of a metadata variable of interest. If this 
 #' variable has only two values, you do not need to provide functions for arguments `groupA` and `groupB`.
 #' @param groupA A function that takes a vector of values and returns TRUE or FALSE for each value. This will 
-#' be used to assign samples to groupA.
+#' be used to assign samples to groupA. In the specific case of `covariate` being a categorical variable,
+#' this can be a character vector of values to be included in groupA.
 #' @param groupB A function that takes a vector of values and returns TRUE or FALSE for each value. This will 
-#' be used to assign samples to groupB. If not provided, groupB will be the complement of groupA.
+#' be used to assign samples to groupB. If not provided, groupB will be the complement of groupA. In the specific
+#' case of `covariate` being a categorical variable, this can be a character vector of values to be included in groupB.
 #' @param method string defining the the differential abundance method. Accepted values are 'DESeq2' and 'Maaslin2'. 
 #' Default is 'Maaslin2', as 'DESeq2' only supports counts.
 #' @param verbose boolean indicating if timed logging is desired
 #' @return ComputeResult object
 #' @rdname differentialAbundance-methods
-#' @importFrom microbiomeComputations differentialAbundance Comparator
+#' @importFrom microbiomeComputations internalDiffAbund Comparator
 #' @export
 setGeneric("differentialAbundance", 
 function(data, covariate, groupA, groupB, method = c("Maaslin2", "DESeq2"), verbose = c(TRUE, FALSE)) {
@@ -137,7 +149,7 @@ function(data, covariate, groupA, groupB, method = c("Maaslin2", "DESeq2"), verb
     groupBLabel <- unique(data@sampleMetadata@data[[covariate]])[2]
     comparator <- buildBinaryComparator(covariate, groupALabel, groupBLabel)
 
-    return(microbiomeComputations::differentialAbundance(data, comparator = comparator, method = method, verbose = verbose))
+    return(microbiomeComputations::internalDiffAbund(data, comparator = comparator, method = method, verbose = verbose))
 })
 
 #' @rdname differentialAbundance-methods
@@ -154,7 +166,24 @@ function(data, covariate, groupA, groupB, method = c("Maaslin2", "DESeq2"), verb
     data@sampleMetadata@data[[covariate]] <- assignToBinaryGroups(data@sampleMetadata@data[[covariate]], groupA, NULL)
     comparator <- buildBinaryComparator(covariate, 'groupA', 'groupB')
 
-    return(microbiomeComputations::differentialAbundance(data, comparator = comparator, method = method, verbose = verbose))
+    return(microbiomeComputations::internalDiffAbund(data, comparator = comparator, method = method, verbose = verbose))
+})
+
+#' @rdname differentialAbundance-methods
+#' @aliases differentialAbundance,CollectionWithMetadata,character,character,missingOrNULL-method
+setMethod("differentialAbundance", signature("CollectionWithMetadata", "character", "character", "missingOrNULL"),
+function(data, covariate, groupA, groupB, method = c("Maaslin2", "DESeq2"), verbose = c(TRUE, FALSE)) {
+    method <- veupathUtils::matchArg(method)
+    verbose <- veupathUtils::matchArg(verbose)
+
+    covariateDataType <- class(data@sampleMetadata@data[[covariate]])
+    if (!covariateDataType %in% c("factor", "character")) {
+        stop("Argument 'groupA' must be a function when 'covariate' is not a factor or character")
+    }
+
+    groupAFxn <- function(x) {x %in% groupA}
+
+    return(differentialAbundance(data, covariate, groupAFxn, groupB, method, verbose))
 })
 
 #' @rdname differentialAbundance-methods
@@ -172,7 +201,25 @@ function(data, covariate, groupA, groupB, method = c("Maaslin2", "DESeq2"), verb
     comparator <- buildBinaryComparator(covariate, 'groupA', 'groupB')
 
     ## microbiomeComputations will remove for us rows not in either group, and provide validation
-    return(microbiomeComputations::differentialAbundance(data, comparator = comparator, method = method, verbose = verbose))
+    return(microbiomeComputations::internalDiffAbund(data, comparator = comparator, method = method, verbose = verbose))
+})
+
+#' @rdname differentialAbundance-methods
+#' @aliases differentialAbundance,CollectionWithMetadata,character,character,character-method
+setMethod("differentialAbundance", signature("CollectionWithMetadata", "character", "character", "character"),
+function(data, covariate, groupA, groupB, method = c("Maaslin2", "DESeq2"), verbose = c(TRUE, FALSE)) {
+    method <- veupathUtils::matchArg(method)
+    verbose <- veupathUtils::matchArg(verbose)
+
+    covariateDataType <- class(data@sampleMetadata@data[[covariate]])
+    if (!covariateDataType %in% c("factor", "character")) {
+        stop("Arguments 'groupA' and 'groupB' must be functions when 'covariate' is not a factor or character")
+    }
+
+    groupAFxn <- function(x) {x %in% groupA}
+    groupBFxn <- function(x) {x %in% groupB}
+
+    return(differentialAbundance(data, covariate, groupAFxn, groupBFxn, method, verbose))
 })
 
 #### NOTE: While i think its important for people to be able to recreate the computes from the site, and tried to make

tests/testthat/test-GetComputeResult.R

@@ -35,7 +35,7 @@ test_that("we can get compute results in different formats", {
                             )
                         )
     )
-    diffAbundOutput <- microbiomeComputations::differentialAbundance(getCollection(mbioDataset, "16S (V4) Genus"), comparatorVariable, method='Maaslin2', verbose=FALSE)
+    diffAbundOutput <- microbiomeComputations::internalDiffAbund(getCollection(mbioDataset, "16S (V4) Genus"), comparatorVariable, method='Maaslin2', verbose=FALSE)
     expect_equal(inherits(diffAbundOutput, "ComputeResult"), TRUE)
 
     correlationOutput <- MicrobiomeDB::selfCorrelation(getCollection(mbioDataset, "16S (V4) Genus"), method='spearman', verbose=FALSE)

tests/testthat/test-differentialAbundance.R

@@ -20,8 +20,6 @@ genusCounts <- AbsoluteAbundanceData(
     ancestorIdColumns = genus@ancestorIdColumns
 )
 
-## TODO tests for assignToBinaryGroups and buildBinaryComparator directly?
-
 test_that("differentialAbundance wrapper works", {    
     # using a variable thats already binary works
     diffAbundOutput <- MicrobiomeDB::differentialAbundance(genus, "delivery_mode", method='Maaslin2', verbose=FALSE)
@@ -51,6 +49,32 @@ test_that("differentialAbundance wrapper works", {
     expect_equal(inherits(diffAbundOutput@statistics@statistics, "data.frame"), TRUE)
     expect_equal(nrow(diffAbundOutput@statistics@statistics) > 0, TRUE)
 
+    # character vector of values works for categorical vars
+    diffAbundOutput <- MicrobiomeDB::differentialAbundance(
+        genus, 
+        "country", 
+        groupA = "Russia",
+        groupB = c("Finland", "Estonia"),
+        method='Maaslin2', 
+        verbose=FALSE
+    )
+    expect_equal(inherits(diffAbundOutput, "ComputeResult"), TRUE)
+    expect_equal(inherits(diffAbundOutput@statistics, "DifferentialAbundanceResult"), TRUE)
+    expect_equal(inherits(diffAbundOutput@statistics@statistics, "data.frame"), TRUE)
+    expect_equal(nrow(diffAbundOutput@statistics@statistics) > 0, TRUE)
+
+    # character vector of values works for continuous vars errs
+    expect_error(
+        diffAbundOutput <- MicrobiomeDB::differentialAbundance(
+            genus, 
+            "breastfed_duration_days", 
+            groupA = "100",
+            groupB = "300",
+            method='Maaslin2', 
+            verbose=FALSE
+        )
+    )
+
     # excluding some values from both predicates works
     diffAbundOutput <- MicrobiomeDB::differentialAbundance(
         genus, 
@@ -93,13 +117,13 @@ test_that("differentialAbundance wrapper works", {
     # passing overlapping groupA and groupB fails
     expect_error(
         diffAbundOutput <- MicrobiomeDB::differentialAbundance(
-        genus, 
-        "breastfed_duration_days", 
-        groupA = function(x) {x<300},
-        groupB = function(x) {x>=100},
-        method='Maaslin2', 
-        verbose=FALSE
-    )
+            genus, 
+            "breastfed_duration_days", 
+            groupA = function(x) {x<300},
+            groupB = function(x) {x>=100},
+            method='Maaslin2', 
+            verbose=FALSE
+        )
     )
 
     # passing only groupA, but where groupA is TRUE for all values fails