Rename agglomerateByRank/Prevalence

DESCRIPTION

@@ -14,7 +14,8 @@ Authors@R:
       person(given = "Basil", family = "Courbayre", role = c("ctb")),
       person(given = "Giulio", family = "Benedetti", role = c("ctb"),
              email = "[email protected]",
-             comment = c(ORCID = "0000-0002-8732-7692"))
+             comment = c(ORCID = "0000-0002-8732-7692")),
+      person(given = "Muluh", family = "Muluh", role=c("ctb"))
 	     )
 Description: The miaViz package implements functions to visualize
     TreeSummarizedExperiment objects especially in the context of microbiome

NEWS

@@ -21,6 +21,7 @@ Changes in version 1.7.x
 Changes in version 1.9.x
 + Updated plotDMN to work with newest mia version
 + Added plotCCA and plotRDA functions
++ deprecate agglomerateByRank and agglomerateByPrevalence
 
 Changes in version 1.11.x
 + replace addTaxonomyTree with addHierarchyTree after renaming in mia package

R/plotGraph.R

@@ -88,9 +88,9 @@
 #' data(row_graph_order)
 #' metadata(GlobalPatterns)$col_graph <- col_graph
 #' 
-#' genus <- agglomerateByRank(GlobalPatterns,"Genus",na.rm=TRUE)
+#' genus <- mergeFeaturesByRank(GlobalPatterns,"Genus",na.rm=TRUE)
 #' metadata(genus)$row_graph <- row_graph
-#' order <- agglomerateByRank(genus,"Order",na.rm=TRUE)
+#' order <- mergeFeaturesByRank(genus,"Order",na.rm=TRUE)
 #' metadata(order)$row_graph <- row_graph_order
 #' 
 #' # plot a graph independently

R/plotPrevalence.R

@@ -13,8 +13,8 @@
 #' @param rank,... additional arguments
 #' \itemize{
 #'   \item{If \code{!is.null(rank)} matching arguments are passed on to
-#'     \code{\link[=agglomerate-methods]{agglomerateByRank}}. See
-#'     \code{\link[=agglomerate-methods]{?agglomerateByRank}} for more details.
+#'     \code{\link[=agglomerate-methods]{mergeFeaturesByRank}}. See
+#'     \code{\link[=agglomerate-methods]{?mergeFeaturesByRank}} for more details.
 #'   }
 #'   \item{additional arguments for plotting. See 
 #'   \code{\link{mia-plot-args}} for more details i.e. call \code{help("mia-plot-args")}}
@@ -79,16 +79,16 @@
 #' 
 #' To exclude certain taxa, preprocess \code{x} to your liking, for example 
 #' with subsetting via \code{getPrevalentTaxa} or 
-#' \code{agglomerateByPrevalence}.
+#' \code{mergeFeaturesByPrevalence}.
 #' 
 #' @return 
 #' A \code{ggplot2} object or \code{plotly} object, if more than one 
 #' \code{prevalences} was defined.
 #' 
 #' @seealso 
 #' \code{\link[mia:getPrevalence]{getPrevalence}},
-#' \code{\link[mia:getPrevalence]{agglomerateByPrevalence}},
-#' \code{\link[mia:agglomerate-methods]{agglomerateByRank}}
+#' \code{\link[mia:getPrevalence]{mergeFeaturesByPrevalence}},
+#' \code{\link[mia:agglomerate-methods]{mergeFeaturesByRank}}
 #' 
 #' @name plotPrevalence
 #' 

R/plotSeries.R

@@ -134,7 +134,7 @@ setMethod("plotSeries", signature = c(object = "SummarizedExperiment"),
             .check_taxonomic_rank(rank, object)
 
             # Agglomerates the object
-            object <- agglomerateByRank(object, rank = rank)
+            object <- mergeFeaturesByRank(object, rank = rank)
         }
 
         # Checks Y

tests/testthat/test-2plotGraph.R

@@ -50,7 +50,7 @@ test_that("plot graph", {
     expect_equal(miaViz:::.colnames_tbl_graph(actual,"edges"),
                  c("from","to","weight"))
     #
-    genus <- agglomerateByRank(GlobalPatterns,"Genus",na.rm=TRUE)
+    genus <- mergeFeaturesByRank(GlobalPatterns,"Genus",na.rm=TRUE)
     plot <- plotColGraph(col_graph,
                          genus,
                          colour_by = "SampleType",

tests/testthat/test-2plotTree.R

@@ -58,7 +58,7 @@ test_that("plot tree", {
     library(scater)
     library(mia)
     data(GlobalPatterns)
-    altExp(GlobalPatterns,"genus") <- agglomerateByRank(GlobalPatterns,"Genus", make_unique = FALSE)
+    altExp(GlobalPatterns,"genus") <- mergeFeaturesByRank(GlobalPatterns,"Genus", make_unique = FALSE)
     altExp(GlobalPatterns,"genus") <- addPerFeatureQC(altExp(GlobalPatterns,"genus"))
     rowData(altExp(GlobalPatterns,"genus"))$log_mean <- log(rowData(altExp(GlobalPatterns,"genus"))$mean)
     top_taxa <- getTopFeatures(altExp(GlobalPatterns,"genus"),

vignettes/miaViz.Rmd

@@ -149,7 +149,7 @@ library(scater)
 library(mia)
 ```
 ```{r}
-altExp(GlobalPatterns,"Genus") <- agglomerateByRank(GlobalPatterns,"Genus")
+altExp(GlobalPatterns,"Genus") <- mergeFeaturesByRank(GlobalPatterns,"Genus")
 altExp(GlobalPatterns,"Genus") <- addPerFeatureQC(altExp(GlobalPatterns,"Genus"))
 rowData(altExp(GlobalPatterns,"Genus"))$log_mean <-
     log(rowData(altExp(GlobalPatterns,"Genus"))$mean)