change transformCounts to transformAssay (#396)

Co-authored-by: Leo Lahti <[email protected]>

NAMESPACE

@@ -87,7 +87,7 @@ export(subsetSamples)
 export(subsetTaxa)
 export(testExperimentCrossAssociation)
 export(testExperimentCrossCorrelation)
-export(transformCounts)
+export(transformAssay)
 export(transformFeatures)
 export(transformSamples)
 export(unsplitByRanks)
@@ -176,7 +176,7 @@ exportMethods(taxonomyRanks)
 exportMethods(taxonomyTree)
 exportMethods(testExperimentCrossAssociation)
 exportMethods(testExperimentCrossCorrelation)
-exportMethods(transformCounts)
+exportMethods(transformAssay)
 exportMethods(transformFeatures)
 exportMethods(transformSamples)
 exportMethods(unsplitByRanks)

NEWS

@@ -2,7 +2,7 @@ Changes in version 0.99.0
 + Submitted to Bioconductor
 
 Changes in version 1.1.x
-+ split transformCounts into transformSamples and transformFeatures
++ split transformAssay into transformSamples and transformFeatures
 + added log_modulo_skewness as a diversity index
 + added functions for summarizing dominant taxa information
 + added wrapper for adding dominant taxa information to colData
@@ -67,7 +67,7 @@ Changes in version 1.7.x
 + mergeSEs: fix bug related to equally named variables that are different class
 + mergeSEs: option for merging multiple assays
 + calculateUnifrac: option for specifying the tree from TreeSE
-+ transformCounts: utilize vegan package
++ transformAssay: utilize vegan package
 + calculateUnifrac: subset tree based on data
 + agglomerateByRank: take into account multiple trees
 + loadFromBiom: name columns of rowData based on prefixes

R/agglomerate.R

@@ -90,9 +90,9 @@
 #'  # If assay contains binary or negative values, summing might lead to meaningless
 #'  # values, and you will get a warning. In these cases, you might want to do 
 #'  # agglomeration again at chosen taxonomic level.
-#'  tse <- transformCounts(GlobalPatterns, method = "pa")
+#'  tse <- transformAssay(GlobalPatterns, method = "pa")
 #'  tse <- agglomerateByRank(tse, rank = "Genus")
-#'  tse <- transformCounts(tse, method = "pa")
+#'  tse <- transformAssay(tse, method = "pa")
 #'
 #' # removing empty labels by setting na.rm = TRUE
 #' sum(is.na(rowData(GlobalPatterns)$Family))

R/calculateOverlap.R

@@ -48,7 +48,7 @@
 #' @examples
 #' data(esophagus)
 #' tse <- esophagus
-#' tse <- transformCounts(tse, method = "relabundance")
+#' tse <- transformAssay(tse, method = "relabundance")
 #' overlap <- calculateOverlap(tse, assay_name = "relabundance")
 #' overlap
 #' 

R/cluster.R

@@ -13,7 +13,7 @@
 #' @param ... Additional parameters to use altExps for example
 #' @inheritParams bluster::clusterRows
 #' @inheritParams runDMN
-#' @inheritParams transformCounts
+#' @inheritParams transformAssay
 #'   
 #'   
 #' @details
@@ -160,4 +160,4 @@ setMethod("cluster", signature = c(x = "SummarizedExperiment"),
                  call. = FALSE)
         }
     }
-}
+}

R/getExperimentCrossAssociation.R

@@ -141,7 +141,7 @@
 #' mae[[1]] <- mae[[1]][1:20, 1:10]
 #' mae[[2]] <- mae[[2]][1:20, 1:10]
 #' # Transform data
-#' mae[[1]] <- transformCounts(mae[[1]], method = "rclr")
+#' mae[[1]] <- transformAssay(mae[[1]], method = "rclr")
 #' 
 #' # Calculate cross-correlations
 #' result <- getExperimentCrossAssociation(mae, method = "pearson", assay.type2 = "nmr")
@@ -151,7 +151,7 @@
 #' # Use altExp option to specify alternative experiment from the experiment
 #' altExp(mae[[1]], "Phylum") <- agglomerateByRank(mae[[1]], rank = "Phylum")
 #' # Transform data
-#' altExp(mae[[1]], "Phylum") <- transformCounts(altExp(mae[[1]], "Phylum"), method = "relabundance")
+#' altExp(mae[[1]], "Phylum") <- transformAssay(altExp(mae[[1]], "Phylum"), method = "relabundance")
 #' # When mode = matrix, matrix is returned
 #' result <- getExperimentCrossAssociation(mae, experiment2 = 2, 
 #'                                         assay.type1 = "relabundance", assay.type2 = "nmr",

R/makephyloseqFromTreeSummarizedExperiment.R

@@ -47,7 +47,7 @@
 #' # they can be chosen with assay.type.
 #'
 #' # Counts relative abundances table
-#' tse <- transformCounts(tse, method = "relabundance")
+#' tse <- transformAssay(tse, method = "relabundance")
 #' phy2 <- makePhyloseqFromTreeSE(tse, assay.type = "relabundance")
 #' phy2
 NULL

R/merge.R

@@ -151,7 +151,7 @@ setGeneric("mergeCols",
     if(length(levels(f)) == nrow(x)){
         return(x)
     }
-
+    
     archetype <- .norm_archetype(f, archetype)
     # merge assays
     assays <- assays(x)
@@ -177,9 +177,9 @@ setGeneric("mergeCols",
     # Check if assays include binary or negative values
     if( all(assay == 0 | assay == 1) ){
         warning(paste0("'",assay.type,"'", " includes binary values."),
-        "\nAgglomeration of it might lead to meaningless values.", 
-        "\nCheck the assay, and consider doing transformation again manually", 
-        " with agglomerated data.",
+                "\nAgglomeration of it might lead to meaningless values.", 
+                "\nCheck the assay, and consider doing transformation again manually", 
+                " with agglomerated data.",
                 call. = FALSE)
     }
     if( !all( assay >= 0 | is.na(assay) ) ){
@@ -232,17 +232,17 @@ setGeneric("mergeCols",
 #' @rdname merge-methods
 #' @export
 setMethod("mergeRows", signature = c(x = "SummarizedExperiment"),
-    function(x, f, archetype = 1L, ...){
-        .merge_rows(x, f, archetype = archetype, ...)
-    }
+          function(x, f, archetype = 1L, ...){
+              .merge_rows(x, f, archetype = archetype, ...)
+          }
 )
 
 #' @rdname merge-methods
 #' @export
 setMethod("mergeCols", signature = c(x = "SummarizedExperiment"),
-    function(x, f, archetype = 1L, ...){
-        .merge_cols(x, f, archetype = archetype, ...)
-    }
+          function(x, f, archetype = 1L, ...){
+              .merge_cols(x, f, archetype = archetype, ...)
+          }
 )
 
 .merge_tree <- function(tree, links){
@@ -330,44 +330,44 @@ setMethod("mergeCols", signature = c(x = "SummarizedExperiment"),
 #' @importFrom ape keep.tip
 #' @export
 setMethod("mergeRows", signature = c(x = "TreeSummarizedExperiment"),
-    function(x, f, archetype = 1L, mergeTree = FALSE, mergeRefSeq = FALSE, ...){
-        # input check
-        if(!.is_a_bool(mergeTree)){
-            stop("'mergeTree' must be TRUE or FALSE.", call. = FALSE)
-        }
-        if(!.is_a_bool(mergeRefSeq)){
-            stop("'mergeRefSeq' must be TRUE or FALSE.", call. = FALSE)
-        }
-        # for optionally merging referenceSeq
-        refSeq <- NULL
-        if(mergeRefSeq){
-            refSeq <- referenceSeq(x)
-        }
-        #
-        x <- callNextMethod(x, f, archetype = 1L, ...)
-        # optionally merge rowTree
-        x <- .merge_trees(x, mergeTree, 1)
-        # optionally merge referenceSeq
-        if(!is.null(refSeq)){
-            referenceSeq(x) <- .merge_refseq_list(refSeq, f, rownames(x), ...)
-        }
-        x
-    }
+          function(x, f, archetype = 1L, mergeTree = FALSE, mergeRefSeq = FALSE, ...){
+              # input check
+              if(!.is_a_bool(mergeTree)){
+                  stop("'mergeTree' must be TRUE or FALSE.", call. = FALSE)
+              }
+              if(!.is_a_bool(mergeRefSeq)){
+                  stop("'mergeRefSeq' must be TRUE or FALSE.", call. = FALSE)
+              }
+              # for optionally merging referenceSeq
+              refSeq <- NULL
+              if(mergeRefSeq){
+                  refSeq <- referenceSeq(x)
+              }
+              #
+              x <- callNextMethod(x, f, archetype = 1L, ...)
+              # optionally merge rowTree
+              x <- .merge_trees(x, mergeTree, 1)
+              # optionally merge referenceSeq
+              if(!is.null(refSeq)){
+                  referenceSeq(x) <- .merge_refseq_list(refSeq, f, rownames(x), ...)
+              }
+              x
+          }
 )
 
 #' @rdname merge-methods
 #' @importFrom ape keep.tip
 #' @export
 setMethod("mergeCols", signature = c(x = "TreeSummarizedExperiment"),
-    function(x, f, archetype = 1L, mergeTree = FALSE, ...){
-        # input check
-        if(!.is_a_bool(mergeTree)){
-            stop("'mergeTree' must be TRUE or FALSE.", call. = FALSE)
-        }
-        #
-        x <- callNextMethod(x, f, archetype = 1L, ...)
-        # optionally merge colTree
-        x <- .merge_trees(x, mergeTree, 2)
-        return(x)
-    }
+          function(x, f, archetype = 1L, mergeTree = FALSE, ...){
+              # input check
+              if(!.is_a_bool(mergeTree)){
+                  stop("'mergeTree' must be TRUE or FALSE.", call. = FALSE)
+              }
+              #
+              x <- callNextMethod(x, f, archetype = 1L, ...)
+              # optionally merge colTree
+              x <- .merge_trees(x, mergeTree, 2)
+              return(x)
+          }
 )

R/mergeSEs.R

@@ -131,8 +131,8 @@
 #' tse_temp
 #' 
 #' # Merge all available assays
-#' tse <- transformCounts(tse, method="relabundance")
-#' ts1 <- transformCounts(tse1, method="relabundance")
+#' tse <- transformAssay(tse, method="relabundance")
+#' ts1 <- transformAssay(tse1, method="relabundance")
 #' tse_temp <- mergeSEs(tse, tse1, assay.type = assayNames(tse))
 #' 
 NULL

R/relabundance.R

@@ -19,7 +19,7 @@
 #' @examples
 #' data(GlobalPatterns)
 #' # Calculates relative abundances
-#' GlobalPatterns <- transformCounts(GlobalPatterns, method="relabundance")
+#' GlobalPatterns <- transformAssay(GlobalPatterns, method="relabundance")
 #' # Fetches calculated relative abundances
 #' # head(assay(GlobalPatterns, "relabundance"))
 NULL

R/runCCA.R

@@ -103,9 +103,9 @@
 #' GlobalPatterns <- runRDA(GlobalPatterns, data ~ SampleType)
 #' plotReducedDim(GlobalPatterns,"CCA", colour_by = "SampleType")
 #' 
-#' # To scale values when using *RDA functions, use transformCounts(MARGIN = "features", 
+#' # To scale values when using *RDA functions, use transformAssay(MARGIN = "features", 
 #' tse <- GlobalPatterns
-#' tse <- transformCounts(tse, MARGIN = "features", method = "z")
+#' tse <- transformAssay(tse, MARGIN = "features", method = "z")
 #' # Data might include taxa that do not vary. Remove those because after z-transform
 #' # their value is NA
 #' tse <- tse[ rowSums( is.na( assay(tse, "z") ) ) == 0, ]