Solve conflicts

.Rbuildignore

@@ -5,3 +5,5 @@
 .RData
 .pdf
 inst/extras/
+^doc$
+^Meta$

.github/workflows/check-bioc-devel.yml

@@ -167,7 +167,7 @@ jobs:
           BiocManager::install("HDF5Array")
           BiocManager::install("fionarhuang/TreeSummarizedExperiment")
           BiocManager::install("microbiome/mia")
-          devtools::install_github("microbiome/miaTime")	  
+          devtools::install_github("microbiome/miaTime")
           
           # rspm is out of date
           install.packages("matrixStats", repos = "https://cran.rstudio.com/")

.gitignore

@@ -3,4 +3,9 @@ inst/doc
 docs
 .Rhistory
 *.RData
+<<<<<<< HEAD
 .DS_Store
+=======
+/doc/
+/Meta/
+>>>>>>> origin/plotRDA

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: miaViz
 Title: Microbiome Analysis Plotting and Visualization
-Version: 1.9.1
+Version: 1.9.2
 Authors@R: 
     c(person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
              email = "[email protected]",
@@ -10,7 +10,8 @@ Authors@R:
              comment = c(ORCID = "0000-0001-5064-0928")),
       person(given = "Leo", family = "Lahti", role = c("aut"),
              email = "[email protected]",
-             comment = c(ORCID = "0000-0001-5537-637X"))
+             comment = c(ORCID = "0000-0001-5537-637X")),
+      person(given = "Basil", family = "Courbayre", role = c("ctb"))
 	     )
 Description: The miaViz package implements functions to visualize
     TreeSummarizedExperiment objects especially in the context of microbiome
@@ -30,7 +31,6 @@ Imports:
     methods,
     stats,
     S4Vectors,
-    miaTime,    
     BiocGenerics,
     BiocParallel,
     DelayedArray,
@@ -55,7 +55,9 @@ Suggests:
     testthat,
     patchwork,
     vegan,
-    microbiomeDataSets
+    microbiomeDataSets,
+    bluster,
+    miaTime
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
 VignetteBuilder: knitr

NAMESPACE

@@ -35,7 +35,6 @@ import(ggplot2)
 import(ggraph)
 import(methods)
 import(mia)
-import(miaTime)
 importFrom(BiocGenerics,ncol)
 importFrom(BiocGenerics,nrow)
 importFrom(BiocParallel,SerialParam)

NEWS

@@ -17,3 +17,6 @@ Changes in version 1.5.x
 
 Changes in version 1.7.x
 + Fixed plotGraph* that was broked due changes in dependencies
+
+Changes in version 1.9.x
++ Updated plotDMN to work with newest mia version

R/miaViz.R

@@ -13,7 +13,6 @@ NULL
 #' @import methods
 #' @import TreeSummarizedExperiment
 #' @import mia
-#' @import miaTime
 #' @import ggplot2
 #' @import ggraph
 #' @importFrom rlang sym !! :=

R/plotAbundance.R

@@ -110,7 +110,7 @@
 #' ## Compositional barplot with top 5 taxa and samples sorted by "Bacteroidetes"
 #' 
 #' # Getting top taxa on a Phylum level
-#' se <- transformCounts(se, method="relabundance")
+#' se <- transformAssay(se, method="relabundance")
 #' se_phylum <- agglomerateByRank(se, rank ="Phylum", onRankOnly=TRUE)
 #' top_taxa <- getTopTaxa(se_phylum,top = 5, assay.type = "relabundance")
 #' 

R/plotAbundanceDensity.R

@@ -94,7 +94,7 @@
 #' plotAbundanceDensity(tse, assay.type = "counts")
 #' 
 #' # Counts relative abundances
-#' tse <- transformCounts(tse, method = "relabundance")
+#' tse <- transformAssay(tse, method = "relabundance")
 #' 
 #' # Plots the relative abundance of 10 most abundant taxa. 
 #' # "nationality" information is used to color the points. X-axis is log-scaled.

R/plotCCA.R

@@ -222,7 +222,7 @@ setMethod("plotRDA", signature = c(object = "SingleCellExperiment"),
             vector_label <- rownames(vector_data)
             # Make labels more tidy
             if( all_var_found ){
-                vector_label <- .tidy_vector_labels(vector_label, coldata)
+                vector_label <- .tidy_vector_labels(vector_label, coldata, ...)
             }
             # Add to df
             vector_data$vector_label <- vector_label
@@ -252,7 +252,7 @@ setMethod("plotRDA", signature = c(object = "SingleCellExperiment"),
             # Get vector labels
             vector_label <- vector_data[["vector_label"]]
             # Add significance to vector labels
-            vector_label <- .add_signif_to_vector_labels(vector_label, variable_names, signif_data)
+            vector_label <- .add_signif_to_vector_labels(vector_label, variable_names, signif_data, ...)
             vector_data[["vector_label"]] <- vector_label
         } else{
             # If it cannot be found, give warning
@@ -297,7 +297,7 @@ setMethod("plotRDA", signature = c(object = "SingleCellExperiment"),
 # Make vector labels more tidy, i.e, separate variable and group names.
 # Replace also underscores with space
 .tidy_vector_labels <- function(
-        vector_label, coldata, sep_group = "\U2012", sep_underscore = " "){
+        vector_label, coldata, sep_group = "\U2012", sep_underscore = " ", ...){
     # Get variable names from sample metadata
     var_names <- colnames(coldata)
     # Loop through vector labels
@@ -322,7 +322,7 @@ setMethod("plotRDA", signature = c(object = "SingleCellExperiment"),
 }
 
 # This function adds significance info to vector labels
-.add_signif_to_vector_labels <- function(vector_label, var_names, signif_data, sep_underscore = " "){
+.add_signif_to_vector_labels <- function(vector_label, var_names, signif_data, sep_underscore = " ", ...){
     # Replace underscores from significance data and variable names to match labels
     rownames(signif_data) <- sapply(rownames(signif_data), function(x) gsub("_", sep_underscore, x))
     var_names <- sapply(var_names, function(x) gsub("_", sep_underscore, x))

R/plotDMN.R

@@ -23,11 +23,19 @@
 #' @name plotDMN
 #'
 #' @examples
-#' data(dmn_se, package = "mia")
-#' names(metadata(dmn_se))
-#'
-#' # plot the fit
-#' plotDMNFit(dmn_se, type = "laplace")
+#' library(mia)
+#' library(bluster)
+#' 
+#' # Get dataset
+#' data("peerj13075", package = "mia")
+#' tse <- peerj13075
+#' 
+#' # Cluster the samples
+#' tse <- cluster(tse, DmmParam(k = 1:4), name = "DMM", full = TRUE)
+#' 
+#' # Plot the fit
+#' plotDMNFit(tse, name = "DMM", type = "laplace")
+#' 
 NULL
 
 #' @rdname plotDMN
@@ -43,7 +51,13 @@ setGeneric("plotDMNFit", signature = "x",
 setMethod("plotDMNFit", signature = c(x = "SummarizedExperiment"),
     function(x, name = "DMN", type = c("laplace","AIC","BIC")){
         #
-        dmn <- getDMN(x, name)
+        if (!is.null(metadata(x)[[name]]$dmm)) {
+            dmn <- metadata(x)[[name]]$dmm
+        } else {
+            .Deprecated(old="getDMN", new="cluster", 
+                    "Now runDMN and calculateDMN are deprecated. Use cluster with DMMParam parameter and full parameter set as true instead.")
+            dmn <- metadata(x)[[name]]
+        }
         fit_FUN <- mia:::.get_dmn_fit_FUN(type)
         #
         k <- vapply(dmn, function(d){ncol(mixture(d))}, numeric(1))

R/plotSeries.R

@@ -67,7 +67,7 @@
 #'            colour_by = "Family")
 #' 
 #' # Counts relative abundances
-#' object <- transformCounts(object, method = "relabundance")
+#' object <- transformAssay(object, method = "relabundance")
 #' 
 #' # Selects taxa
 #' taxa <- c("seq_1", "seq_2", "seq_3", "seq_4", "seq_5")

vignettes/miaViz.Rmd

@@ -334,7 +334,7 @@ if(!requireNamespace("devtools", quietly = TRUE)){
     BiocManager::install("devtools")
 }
 if(!requireNamespace("miaTime", quietly = TRUE)){
-    devtools::install_github("microbiome/miaTime")
+    devtools::install_github("microbiome/miaTime", upgrade = "never")
 }
 ```