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RiboRingsRiboRings
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Merge pull request #27 from microbiome/cluster
Add Cluster tab
2 parents 22dcfd3 + 9644d5e commit 6b11cd5

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‎DESCRIPTION‎

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Package: miaDash
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Version: 1.1.4
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Version: 1.1.5
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Authors@R:
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c(person(given = "Giulio", family = "Benedetti", role = c("aut", "cre"),
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email = "giulio.benedetti@utu.fi",
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shiny
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Imports:
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ape,
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bluster,
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htmltools,
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iSEEtree (>= 1.1.4),
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mia,
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URL: https://github.com/microbiome/miaDash
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BugReports: https://github.com/microbiome/miaDash/issues
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Roxygen: list(markdown = TRUE)
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RoxygenNote: 7.3.2
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RoxygenNote: 7.3.3
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VignetteBuilder: knitr
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Config/testthat/edition: 3

‎NAMESPACE‎

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@@ -9,6 +9,10 @@ importFrom(SummarizedExperiment,rowData)
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importFrom(TreeSummarizedExperiment,TreeSummarizedExperiment)
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importFrom(TreeSummarizedExperiment,rowTree)
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importFrom(ape,read.tree)
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importFrom(bluster,DmmParam)
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importFrom(bluster,HclustParam)
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importFrom(bluster,KmeansParam)
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importFrom(bluster,NNGraphParam)
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importFrom(htmltools,HTML)
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importFrom(htmltools,br)
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importFrom(htmltools,div)
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importFrom(iSEEtree,RowTreePlot)
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importFrom(iSEEtree,ScreePlot)
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importFrom(mia,addAlpha)
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importFrom(mia,addCluster)
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importFrom(mia,addHierarchyTree)
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importFrom(mia,addPrevalence)
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importFrom(mia,addPrevalentAbundance)

‎NEWS‎

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Changes in version 1.1.5
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* Added Cluster tab
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Changes in version 1.1.4
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* Switch from csv to tsv support
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* Improve interoperability with MGnify datasets

‎R/constants.R‎

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#' @keywords internal
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#' @name constants
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#' @aliases .miaDashDefaultPanels .miaDashOtherPanels .transformMethods
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#' .alphaMetrics .betaMetrics .betaMethods .qualityMetrics
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#' .qualityMetrics .alphaMetrics .betaMetrics .betaMethods .clustMethods
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#' .DmmCriteria
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NULL
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#' @rdname constants
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"hellinger", "log", "log10", "log2", "max", "normalize", "pa",# "philr",
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"range", "rank", "rclr", "relabundance", "rrank", "standardize", "total")
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#' @rdname constants
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.qualityMetrics <- list("Library size" = "PerCellQC",
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"Prevalence" = "Prevalence", "Prevalent abundance" = "PrevalentAbundance",
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"Hierarchy tree" = "HierarchyTree")
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#' @rdname constants
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.alphaMetrics <- c("coverage_diversity", "fisher_diversity", "faith_diversity",
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"gini_simpson_diversity", "inverse_simpson_diversity",
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#"TSNE", "UMAP")
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#' @rdname constants
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.qualityMetrics <- list("Library size" = "PerCellQC",
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"Prevalence" = "Prevalence", "Prevalent abundance" = "PrevalentAbundance",
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"Hierarchy tree" = "HierarchyTree")
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.clustMethods <- c("Dmm", "Hclust", "Kmeans", "NNGraph")
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#' @rdname constants
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.DmmCriteria <- c("laplace", "AIC", "BIC")

‎R/landing_page.R‎

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div(style = "margin-top: -20px"),
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conditionalPanel(
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condition = "input.ftype == 'Mothur' | input.ftype == 'QIIME2'",
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condition = paste0("input.ftype == 'Mothur' | ",
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"input.ftype == 'QIIME2'"),
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fileInput(inputId = "f.rowdata",
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label = "rowData:", accept = c(".taxonomy",
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".qza"), placeholder = "taxonomy or qza"),
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div(style = "margin-top: -20px")),
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conditionalPanel(
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condition = "input.ftype == 'biom' | input.ftype == 'MetaPhlAn'",
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condition = paste0("input.ftype == 'biom' | ",
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"input.ftype == 'MetaPhlAn'"),
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fileInput(inputId = "tree.file",
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label = "rowTree:", placeholder = "tree.tree",
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accept = c(".tree", ".tre", ".qza")),
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div(style = "margin-top: -20px")),
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conditionalPanel(
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condition = "input.ftype == 'biom' | input.ftype == 'HUMAnN'",
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condition = paste0("input.ftype == 'biom' | ",
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"input.ftype == 'HUMAnN'"),
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checkboxInput(inputId = "rm.tax.pref",
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label = "Remove taxa prefixes")),
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inline = TRUE),
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conditionalPanel(
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condition = paste("input.bmethod == 'MDS' || ",
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condition = paste0("input.bmethod == 'MDS' || ",
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"input.bmethod == 'NMDS' || ",
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"input.bmethod == 'RDA'"),
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label = "Number of components:", min = 1,
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step = 1)),
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tabPanel(title = "Cluster", value = "cluster",
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radioButtons(inputId = "cmethod",
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label = "Method:", choices = .clustMethods,
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inline = TRUE),
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conditionalPanel(
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condition = paste0("input.cmethod == 'Dmm' | ",
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"input.cmethod == 'Kmeans'"),
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numericInput(inputId = "kclusters", value = 3,
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label = "Number of clusters:", min = 1,
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step = 1)),
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conditionalPanel(
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condition = "input.cmethod == 'Dmm'",
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selectInput(inputId = "dmm.type",
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label = "Criterion:",
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choices = .DmmCriteria,
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selected = .DmmCriteria[1]),
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numericInput(inputId = "dmm.seed", value = 1L,
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label = "Seed:", min = 0, step = 1)),
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conditionalPanel(
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condition = "input.cmethod == 'NNGraph'",
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numericInput(inputId = "kneighbours",
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value = 10, label = "Number of neighbours:",
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min = 1, step = 1),
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checkboxInput(inputId = "nn.shared",
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label = "Construct shared graph")),
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checkboxInput(inputId = "clust.full",
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label = "Add to metadata"),
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radioButtons(inputId = "clust.margin",
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label = "Margin:", inline = TRUE,
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choices = c("samples", "features"),
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selected = "features")),
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footer = textInput(inputId = "estimate.name",
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label = "Name:")),
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‎R/observers.R‎

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rObjects$tse <- .update_tse(
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rObjects$tse, TreeSummarizedExperiment, fun_args
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)
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print(input$taxa.from.rownames)
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if( input$taxa.from.rownames ){
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rObjects$tse <- .update_tse(
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#' @rdname create_observers
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#' @importFrom stats as.formula
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#' @importFrom mia addAlpha runNMDS runRDA getDissimilarity addHierarchyTree
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#' addPrevalence addPrevalentAbundance
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#' addPrevalence addPrevalentAbundance addCluster
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#' @importFrom TreeSummarizedExperiment rowTree
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#' @importFrom scater runMDS runPCA
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#' @importFrom scuttle addPerCellQC
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#' @importFrom vegan vegdist
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#' @importFrom bluster KmeansParam DmmParam HclustParam NNGraphParam
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.create_estimate_observers <- function(input, rObjects) {
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# nocov start
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name <- input$bmethod
296297
}
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298-
beta_args <- list(x = rObjects$tse, assay.type = input$assay.type,
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beta_args <- list(x = rObjects$tse,
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assay.type = input$estimate.assay,
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ncomponents = input$ncomponents, name = name)
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if( input$beta.index == "unifrac" ){
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beta_args <- c(beta_args,
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formula = as.formula(input$rda.formula))
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}
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beta_fun <- eval(parse(text = paste0("run", input$bmethod)))
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rObjects$tse <- .update_tse(rObjects$tse, beta_fun, beta_args)
338339
})
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341+
}else if( input$estimate == "cluster" ){
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isolate({
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req(input$estimate.assay)
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if( input$estimate.name != "" ){
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name <- input$estimate.name
348+
}else{
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name <- "clusters"
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}
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if( input$cmethod == "Dmm" ){
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blus_params <- list(k = input$kclusters,
355+
type = deparse(input$dmm.type))#, seed = input$dmm.seed)
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}else if( input$cmethod == "Hclust" ){
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blus_params <- list()
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361+
}else if( input$cmethod == "Kmeans" ){
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363+
blus_params <- list(centers = input$kclusters)
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365+
}else if( input$cmethod == "NNGraph" ){
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blus_params <- list(shared = input$nn.shared,
368+
k = input$kneighbours)
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}
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372+
blus_params <- sprintf("%s=%s", names(blus_params), blus_params)
373+
blus_params <- paste(blus_params, collapse = ", ")
374+
blus_fun <- sprintf("%sParam(%s)", input$cmethod, blus_params)
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376+
clust_args <- list(x = rObjects$tse,
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assay.type = input$estimate.assay,
378+
by = input$clust.margin, full = input$clust.full,
379+
BLUSPARAM = eval(parse(text = blus_fun)),
380+
name = name, clust.col = name)
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382+
rObjects$tse <- .update_tse(
383+
rObjects$tse, addCluster, clust_args)
384+
})
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}
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}, ignoreInit = TRUE, ignoreNULL = TRUE)

‎man/constants.Rd‎

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