Make data import from MGnify smoother

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: miaDash
-Version: 1.1.3
+Version: 1.1.4
 Authors@R: 
     c(person(given = "Giulio", family = "Benedetti", role = c("aut", "cre"),
            email = "giulio.benedetti@utu.fi",
@@ -33,6 +33,7 @@ Imports:
     scuttle,
     shinydashboard,
     shinyjs,
+    shinyWidgets,
     S4Vectors,
     SingleCellExperiment,
     SummarizedExperiment,

NAMESPACE

@@ -5,6 +5,7 @@ importFrom(S4Vectors,DataFrame)
 importFrom(SummarizedExperiment,assay)
 importFrom(SummarizedExperiment,assayNames)
 importFrom(SummarizedExperiment,colData)
+importFrom(SummarizedExperiment,rowData)
 importFrom(TreeSummarizedExperiment,TreeSummarizedExperiment)
 importFrom(TreeSummarizedExperiment,rowTree)
 importFrom(ape,read.tree)
@@ -31,6 +32,7 @@ importFrom(mia,addPrevalence)
 importFrom(mia,addPrevalentAbundance)
 importFrom(mia,agglomerateByRank)
 importFrom(mia,getDissimilarity)
+importFrom(mia,getTaxonomyLabels)
 importFrom(mia,importBIOM)
 importFrom(mia,importHUMAnN)
 importFrom(mia,importMetaPhlAn)
@@ -46,6 +48,7 @@ importFrom(rintrojs,introjs)
 importFrom(scater,runMDS)
 importFrom(scater,runPCA)
 importFrom(scuttle,addPerCellQC)
+importFrom(shinyWidgets,addSpinner)
 importFrom(shinydashboard,box)
 importFrom(shinydashboard,dashboardBody)
 importFrom(shinydashboard,dashboardHeader)
@@ -56,6 +59,5 @@ importFrom(shinyjs,enable)
 importFrom(stats,as.formula)
 importFrom(utils,data)
 importFrom(utils,packageVersion)
-importFrom(utils,read.csv)
 importFrom(utils,read.table)
 importFrom(vegan,vegdist)

NEWS

@@ -1,3 +1,7 @@
+Changes in version 1.1.4
+* Switch from csv to tsv support
+* Improve interoperability with MGnify datasets
+
 Changes in version 1.1.3
 * Introduced tab for Quality Control
 

R/constants.R

@@ -10,14 +10,15 @@
 #' \item{\code{.alphaMetrics}}{List of alpha diversity metrics.}
 #' \item{\code{.betaMetrics}}{List of beta diversity metrics.}
 #' \item{\code{.betaMethods}}{List of beta diversity methods.}
+#' \item{\code{.qualityMetrics}}{List of metrics for quality control.}
 #' }
 #'
 #' @author Giulio Benedetti
 #' 
 #' @keywords internal
 #' @name constants
 #' @aliases .miaDashDefaultPanels .miaDashOtherPanels .transformMethods
-#' .alphaMetrics .betaMetrics .betaMethods
+#' .alphaMetrics .betaMetrics .betaMethods .qualityMetrics
 NULL
 
 #' @rdname constants

R/landing_page.R

@@ -14,6 +14,7 @@
 #'   dashboardBody box
 #' @importFrom htmltools HTML br tags div tagList
 #' @importFrom shinyjs disable
+#' @importFrom shinyWidgets addSpinner
 #' @importFrom utils data
 .landing_page <- function(FUN, input, output, session) {
 
@@ -49,16 +50,16 @@
                         tabPanel(title = "Raw Data", value = "raw", br(),
 
                             fileInput(inputId = "assay", label = "Assays:",
-                                accept = ".csv", multiple = TRUE,
-                                placeholder = "assay.csv"),
+                                accept = ".tsv", multiple = TRUE,
+                                placeholder = "assay.tsv"),
                             div(style = "margin-top: -20px"),
 
                             fileInput(inputId = "coldata", label = "colData:",
-                                accept = ".csv", placeholder = "coldata.csv"),
+                                accept = ".tsv", placeholder = "coldata.tsv"),
                             div(style = "margin-top: -20px"),
 
                             fileInput(inputId = "rowdata", label = "rowData:",
-                                accept = ".csv", placeholder = "rowdata.csv"),
+                                accept = ".tsv", placeholder = "rowdata.tsv"),
                             div(style = "margin-top: -20px"),
 
                             fileInput(inputId = "row.tree",
@@ -69,7 +70,10 @@
                             fileInput(inputId = "col.tree",
                                   label = "colTree:", placeholder = "col.tree",
                                   accept = c(".tree", ".tre")),
-                            div(style = "margin-top: -20px")),
+                            div(style = "margin-top: -20px"),
+
+                            checkboxInput(inputId = "taxa.from.rownames",
+                                label = "Derive rowData from assay rownames")),
 
                         tabPanel(title = "Foreign", value = "foreign", br(),
 
@@ -237,7 +241,8 @@
                 box(id = "output.panel", title = "Output", width = 8,
                     status = "primary", solidHeader = TRUE, collapsible = TRUE,
 
-                    verbatimTextOutput(outputId = "object"),
+                    addSpinner(verbatimTextOutput(outputId = "object"),
+                        color = "#007bff"),
 
                     downloadButton(outputId = "download", label = "Download",
                         class = "btn-primary")))))})

R/observers.R

@@ -14,7 +14,7 @@
 #' @keywords internal
 
 #' @rdname create_observers
-#' @importFrom utils read.csv
+#' @importFrom utils read.table
 #' @importFrom ape read.tree
 #' @importFrom S4Vectors DataFrame
 #' @importFrom mia importHUMAnN importMetaPhlAn importQIIME2 importMothur
@@ -40,18 +40,20 @@
 
             isolate({
                 req(input$assay)
-
+                
                 assay_list <- lapply(input$assay$datapath,
-                    function(x) as.matrix(read.csv(x, row.names = 1)))
+                    function(x) as.matrix(read.table(x, row.names = 1,
+                        header = TRUE, sep = "\t")))
 
-                names(assay_list) <- gsub(".csv", "", input$assay$name)
+                names(assay_list) <- gsub(".tsv", "", input$assay$name)
 
                 coldata <- .set_optarg(input$coldata$datapath,
                     alternative = DataFrame(row.names = colnames(assay_list[[1]])),
-                    loader = read.csv, row.names = 1)
+                    loader = read.table, row.names = 1, header = TRUE, sep = "\t")
 
                 rowdata <- .set_optarg(input$rowdata$datapath,
-                    loader = read.csv, row.names = 1)
+                    loader = read.table, row.names = 1,
+                    header = TRUE, sep = "\t")
 
                 row.tree <- .set_optarg(input$row.tree$datapath,
                     loader = read.tree)
@@ -65,6 +67,14 @@
                 rObjects$tse <- .update_tse(
                      rObjects$tse, TreeSummarizedExperiment, fun_args
                 )
+                print(input$taxa.from.rownames)
+                if( input$taxa.from.rownames ){
+
+                    rObjects$tse <- .update_tse(
+                        rObjects$tse, .rownames2taxa, list(x = rObjects$tse)
+                    )
+
+                }
 
             })
 

R/utils.R

@@ -118,6 +118,7 @@ NULL
     return(cond)
 }
 
+#' @rdname utils
 #' @importFrom mia importBIOM
 #' @importFrom SummarizedExperiment colData
 #' @importFrom TreeSummarizedExperiment rowTree
@@ -139,4 +140,24 @@ NULL
     }
 
     return(tse)
+}
+
+#' @rdname utils
+#' @importFrom SummarizedExperiment rowData
+#' @importFrom S4Vectors DataFrame
+#' @importFrom mia getTaxonomyLabels
+.rownames2taxa <- function(x){
+
+    tax_df <- data.frame(Taxonomy = rownames(x))
+
+    rowdata <- mia:::.parse_taxonomy(
+        tax_df,
+        col.name = "Taxonomy",
+        removeTaxaPrefixes = TRUE
+    )
+
+    rowData(x) <- DataFrame(rowdata)
+    rownames(x) <- getTaxonomyLabels(x, make.unique = TRUE)
+
+    return(x)
 }

README.md

@@ -13,7 +13,7 @@ manipulate, analyse and visualise TreeSummarizedExperiment objects.
 
 miaDash is available online at [this address](https://miadash-microbiome.2.rahtiapp.fi/).
 While suitable for small and medium datasets, the online version may slow down
-when larger datasets are analysed (> 1000 features). In this case, the app can
+when larger datasets are analysed (> 500 samples). In this case, the app can
 be installed and run locally. Either way, functionality to subset and
 agglomerate the data is also provided in the app.