replace getPrevalent* and getTop* functions

NEWS

@@ -27,4 +27,4 @@ Changes in version 1.11.x
 
 Changes in version 1.13.x
 + plot*Tree: bugfix, ununique nodes
-+ Added confidence.level parameter to plotCCA 
++ Added confidence.level parameter to plotCCA

R/plotAbundance.R

@@ -112,7 +112,7 @@
 #' # Getting top taxa on a Phylum level
 #' se <- transformAssay(se, method="relabundance")
 #' se_phylum <- agglomerateByRank(se, rank ="Phylum", onRankOnly=TRUE)
-#' top_taxa <- getTopFeatures(se_phylum,top = 5, assay.type = "relabundance")
+#' top_taxa <- getTop(se_phylum,top = 5, assay.type = "relabundance")
 #' 
 #' # Renaming the "Phylum" rank to keep only top taxa and the rest to "Other"
 #' phylum_renamed <- lapply(rowData(se)$Phylum,

R/plotAbundanceDensity.R

@@ -214,7 +214,7 @@ setMethod("plotAbundanceDensity", signature = c(object = "SummarizedExperiment")
     # Gets the assay
     mat <- assay(object, assay.type, withDimnames = TRUE)
     # Gets the most abundant taxa
-    top_taxa <- getTopFeatures(object, top = n, assay.type = assay.type)
+    top_taxa <- getTop(object, top = n, assay.type = assay.type)
     # Subsets abundance table  by taking taxa of highest abundance
     mat <- mat[top_taxa, , drop=FALSE]
     # enable conversion to data.frame for non-matrix assays, e.g. sparseMatrices

R/plotPrevalence.R

@@ -78,7 +78,7 @@
 #' \code{rank} argument. 
 #' 
 #' To exclude certain taxa, preprocess \code{x} to your liking, for example 
-#' with subsetting via \code{getPrevalentTaxa} or 
+#' with subsetting via \code{getPrevalent} or 
 #' \code{agglomerateByPrevalence}.
 #' 
 #' @return 

R/plotSeries.R

@@ -63,7 +63,7 @@
 #' # Plots 2 most abundant taxa, which are colored by their family
 #' plotSeries(object,
 #'            x = "DAY_ORDER",
-#'            y = getTopFeatures(object, 2),
+#'            y = getTop(object, 2),
 #'            colour_by = "Family")
 #' 
 #' # Counts relative abundances
@@ -82,7 +82,7 @@
 #' # In addition to 'colour_by' and 'linetype_by', 'size_by' can also be used to group taxa.
 #' plotSeries(object,
 #'            x = "DAY_ORDER", 
-#'            y = getTopFeatures(object, 5), 
+#'            y = getTop(object, 5), 
 #'            colour_by = "Family",
 #'            size_by = "Phylum",
 #'            assay.type = "counts")

R/plotTree.R

@@ -112,7 +112,7 @@
 #'   log(rowData(altExp(GlobalPatterns,"Genus"))$mean)
 #' rowData(altExp(GlobalPatterns,"Genus"))$detected <-
 #'    rowData(altExp(GlobalPatterns,"Genus"))$detected / 100
-#' top_genus <- getTopFeatures(altExp(GlobalPatterns,"Genus"),
+#' top_genus <- getTop(altExp(GlobalPatterns,"Genus"),
 #'                         method="mean",
 #'                         top=100L,
 #'                         assay.type="counts")
@@ -148,8 +148,8 @@
 #' # aggregating data over the taxonomic levels for plotting a taxonomic tree
 #' # please note that the original tree of GlobalPatterns is dropped by
 #' # unsplitByRanks
-#' GlobalPatterns <- agglomerateByRanks(GlobalPatterns)
-#' top_phyla <- getTopFeatures(altExp(GlobalPatterns,"Phylum"),
+#' altExps(GlobalPatterns) <- splitByRanks(GlobalPatterns)
+#' top_phyla <- getTop(altExp(GlobalPatterns,"Phylum"),
 #'                         method="mean",
 #'                         top=10L,
 #'                         assay.type="counts")

tests/testthat/test-2plotTree.R

@@ -61,7 +61,7 @@ test_that("plot tree", {
     altExp(GlobalPatterns,"genus") <- agglomerateByRank(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"),
+    top_taxa <- getTop(altExp(GlobalPatterns,"genus"),
                            method="mean",
                            top=100L,
                            assay.type="counts")

vignettes/miaViz.Rmd

@@ -73,7 +73,7 @@ plotAbundance(GlobalPatterns, rank = "Kingdom", assay.type = "relabundance")
 With subsetting to selected features the plot can be fine tuned.
 
 ```{r}
-prev_phylum <- getPrevalentTaxa(GlobalPatterns, rank = "Phylum",
+prev_phylum <- getPrevalent(GlobalPatterns, rank = "Phylum",
                                 detection = 0.01, onRankOnly = TRUE)
 ```
 
@@ -155,7 +155,7 @@ rowData(altExp(GlobalPatterns,"Genus"))$log_mean <-
     log(rowData(altExp(GlobalPatterns,"Genus"))$mean)
 rowData(altExp(GlobalPatterns,"Genus"))$detected <- 
     rowData(altExp(GlobalPatterns,"Genus"))$detected / 100
-top_taxa <- getTopFeatures(altExp(GlobalPatterns,"Genus"),
+top_taxa <- getTop(altExp(GlobalPatterns,"Genus"),
                        method="mean",
                        top=100L,
                        assay.type="counts")
@@ -268,7 +268,7 @@ library("miaTime")
 data(SilvermanAGutData, package="miaTime")
 tse <- SilvermanAGutData
 tse <- transformAssay(tse, method = "relabundance")
-taxa <- getTopFeatures(tse, 2)
+taxa <- getTop(tse, 2)
 ```
 
 Data from samples collected along time can be visualized using `plotSeries`.
@@ -300,7 +300,7 @@ Additional variables can be used to modify line type aesthetics.
 ```{r, eval=FALSE}
 plotSeries(tse,
            x = "DAY_ORDER",
-           y = getTopFeatures(tse, 5),
+           y = getTop(tse, 5),
            colour_by = "Family",
            linetype_by = "Phylum",
            assay.type = "counts")