This question I had asked here which I guess more R question than bioinfo
This the code
library(Seurat)
library(BiocParallel)
library(reticulate)
library(Matrix)
library(shiny)
library(dplyr)
# Set the maximum file upload size to 3 GB
options(shiny.maxRequestSize = 3e+9)
# Define the UI
ui <- fluidPage(
titlePanel("Single-cell RNA-seq Analysis"),
sidebarLayout(
sidebarPanel(
fileInput("h5ad_file", "Upload H5AD File", accept = ".h5ad"),
actionButton("run_button", "Run Analysis"),
selectInput("gene_input", "Select Gene for Feature Plot:", choices = NULL),
downloadButton("export_button", "Export Cluster")
),
mainPanel(
plotOutput("feature_plot")
)
)
)
# Define the server
server <- function(input, output, session) {
# Function to perform the analysis
performAnalysis <- function(h5ad_file) {
sc <- import("scanpy")
adata <- sc$read_h5ad(h5ad_file)
counts <- t(adata$layers["counts"])
colnames(counts) <- adata$obs_names$to_list()
rownames(counts) <- adata$var_names$to_list()
counts <- Matrix::Matrix(as.matrix(counts), sparse = TRUE)
data <- t(adata$layers["winsorized"])
colnames(data) <- adata$obs_names$to_list()
rownames(data) <- adata$var_names$to_list()
data <- Matrix::Matrix(as.matrix(data), sparse = TRUE)
seurat <- CreateSeuratObject(counts)
seurat <- SetAssayData(seurat, "data", data)
seurat <- AddMetaData(seurat, adata$obs)
seurat[["percent.mt"]] <- PercentageFeatureSet(seurat, pattern = "^MT[-\\.]")
seurat <- NormalizeData(seurat)
seurat <- FindVariableFeatures(seurat, nfeatures = 500)
seurat <- ScaleData(seurat)
seurat <- ScaleData(seurat, vars.to.regress = c("nFeature_RNA", "percent.mt"))
seurat <- RunPCA(seurat, npcs = 50)
seurat <- RunUMAP(seurat, dims = 1:20)
seurat <- FindNeighbors(seurat, dims = 1:20)
seurat <- FindClusters(seurat, resolution = 1)
cl_markers <- FindAllMarkers(seurat, only.pos = TRUE, min.pct = 0.25, logfc.threshold = log(1.2))
top_gene <- cl_markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_log2FC)
return(seurat)
}
# Reactive value to store the Seurat object
seurat_object <- reactiveVal(NULL)
# Perform analysis when the Run Analysis button is clicked
observeEvent(input$run_button, {
req(input$h5ad_file)
h5ad_file <- input$h5ad_file$datapath
seurat <- performAnalysis(h5ad_file)
seurat_object(seurat)
# Update gene choices for feature plot
gene_choices <- rownames(seurat@assays$RNA@counts)
updateSelectInput(session, "gene_input", choices = gene_choices)
})
# Render the feature plot
output$feature_plot <- renderPlot({
seurat <- seurat_object()
if (!is.null(seurat)) {
gene <- input$gene_input
FeaturePlot(seurat, features = gene, label = TRUE)
}
})
# Export cluster when the Export Cluster button is clicked
observeEvent(input$export_button, {
seurat <- seurat_object()
if (!is.null(seurat)) {
cluster_data <- cbind(Cluster = seurat@meta.data$cluster, seurat@assays$RNA@counts)
write.csv(cluster_data, "cluster.csv", row.names = TRUE)
updateActionButton(session, "export_button", label = "Download Cluster", href = "cluster.csv")
}
})
# Function to end the session
endSession <- function() {
stopApp() # This will end the Shiny session and shut down the server
}
# Call the endSession function when the session ends (browser window closed)
session$onSessionEnded(endSession)
}
# Run the Shiny app
shinyApp(ui = ui, server = server)
The seurat object which is generated its structure is like this
str(seurat)
Formal class 'Seurat' [package "SeuratObject"] with 13 slots
..@ assays :List of 1
.. ..$ RNA:Formal class 'Assay' [package "SeuratObject"] with 8 slots
.. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. .. .. ..@ i : int [1:2238732] 29 73 80 148 163 184 186 227 229 230 ...
.. .. .. .. .. ..@ p : int [1:2639] 0 779 2131 3260 4220 4741 5522 6304 7094 7626 ...
.. .. .. .. .. ..@ Dim : int [1:2] 13714 2638
.. .. .. .. .. ..@ Dimnames:List of 2
.. .. .. .. .. .. ..$ : chr [1:13714] "AL627309.1" "AP006222.2" "RP11-206L10.2" "RP11-206L10.9" ...
.. .. .. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. .. .. ..@ x : num [1:2238732] 1 1 2 1 1 1 1 41 1 1 ...
.. .. .. .. .. ..@ factors : list()
.. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. .. .. ..@ i : int [1:2238732] 29 73 80 148 163 184 186 227 229 230 ...
.. .. .. .. .. ..@ p : int [1:2639] 0 779 2131 3260 4220 4741 5522 6304 7094 7626 ...
.. .. .. .. .. ..@ Dim : int [1:2] 13714 2638
.. .. .. .. .. ..@ Dimnames:List of 2
.. .. .. .. .. .. ..$ : chr [1:13714] "AL627309.1" "AP006222.2" "RP11-206L10.2" "RP11-206L10.9" ...
.. .. .. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. .. .. ..@ x : num [1:2238732] 1.64 1.64 2.23 1.64 1.64 ...
.. .. .. .. .. ..@ factors : list()
.. .. .. ..@ scale.data : num [1:500, 1:2638] 1.5265 -0.4982 -0.0324 -0.3129 -0.4631 ...
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..$ : chr [1:500] "MRPL20" "RER1" "TNFRSF9" "SRM" ...
.. .. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. ..@ key : chr "rna_"
.. .. .. ..@ assay.orig : NULL
.. .. .. ..@ var.features : chr [1:500] "PPBP" "LYZ" "S100A9" "IGLL5" ...
.. .. .. ..@ meta.features:'data.frame': 13714 obs. of 5 variables:
.. .. .. .. ..$ vst.mean : num [1:13714] 0.00341 0.00114 0.0019 0.00114 0.00682 ...
.. .. .. .. ..$ vst.variance : num [1:13714] 0.0034 0.00114 0.00189 0.00114 0.00678 ...
.. .. .. .. ..$ vst.variance.expected : num [1:13714] 0.00365 0.00114 0.00197 0.00114 0.00748 ...
.. .. .. .. ..$ vst.variance.standardized: num [1:13714] 0.933 0.992 0.963 0.992 0.906 ...
.. .. .. .. ..$ vst.variable : logi [1:13714] FALSE FALSE FALSE FALSE FALSE FALSE ...
.. .. .. ..@ misc : list()
..@ meta.data :'data.frame': 2638 obs. of 10 variables:
.. ..$ orig.ident : Factor w/ 1 level "SeuratProject": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ nCount_RNA : num [1:2638] 2419 4903 3147 2639 980 ...
.. ..$ nFeature_RNA : int [1:2638] 779 1352 1129 960 521 781 782 790 532 550 ...
.. ..$ n_genes : num [1:2638] 781 1352 1131 960 522 ...
.. ..$ percent_mito : num [1:2638] 0.0302 0.0379 0.0089 0.0174 0.0122 ...
.. ..$ n_counts : num [1:2638] 2419 4903 3147 2639 980 ...
.. ..$ louvain : Factor w/ 8 levels "CD4 T cells",..: 1 3 1 2 5 4 4 4 1 6 ...
.. ..$ percent.mt : num [1:2638] 3.02 3.79 0.89 1.74 1.22 ...
.. ..$ RNA_snn_res.1 : Factor w/ 8 levels "0","1","2","3",..: 1 3 1 6 4 1 5 5 5 6 ...
.. ..$ seurat_clusters: Factor w/ 8 levels "0","1","2","3",..: 1 3 1 6 4 1 5 5 5 6 ...
..@ active.assay: chr "RNA"
..@ active.ident: Factor w/ 8 levels "0","1","2","3",..: 1 3 1 6 4 1 5 5 5 6 ...
.. ..- attr(*, "names")= chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
..@ graphs :List of 2
.. ..$ RNA_nn :Formal class 'Graph' [package "SeuratObject"] with 7 slots
.. .. .. ..@ assay.used: chr "RNA"
.. .. .. ..@ i : int [1:52760] 0 102 187 274 457 833 1147 1503 1506 1550 ...
.. .. .. ..@ p : int [1:2639] 0 17 24 30 33 40 61 83 85 96 ...
.. .. .. ..@ Dim : int [1:2] 2638 2638
.. .. .. ..@ Dimnames :List of 2
.. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. ..@ x : num [1:52760] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. ..@ factors : list()
.. ..$ RNA_snn:Formal class 'Graph' [package "SeuratObject"] with 7 slots
.. .. .. ..@ assay.used: chr "RNA"
.. .. .. ..@ i : int [1:245992] 0 69 102 172 187 189 248 274 276 315 ...
.. .. .. ..@ p : int [1:2639] 0 79 118 179 253 323 424 541 574 650 ...
.. .. .. ..@ Dim : int [1:2] 2638 2638
.. .. .. ..@ Dimnames :List of 2
.. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. ..@ x : num [1:245992] 1 0.0811 0.2903 0.1765 0.1765 ...
.. .. .. ..@ factors : list()
..@ neighbors : list()
..@ reductions :List of 2
.. ..$ pca :Formal class 'DimReduc' [package "SeuratObject"] with 9 slots
.. .. .. ..@ cell.embeddings : num [1:2638, 1:50] -3.544 -5.892 -3.544 10.139 0.932 ...
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. .. .. ..$ : chr [1:50] "PC_1" "PC_2" "PC_3" "PC_4" ...
.. .. .. ..@ feature.loadings : num [1:500, 1:50] 0.02701 0.16566 0.17077 -0.00786 -0.03674 ...
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..$ : chr [1:500] "PPBP" "LYZ" "S100A9" "IGLL5" ...
.. .. .. .. .. ..$ : chr [1:50] "PC_1" "PC_2" "PC_3" "PC_4" ...
.. .. .. ..@ feature.loadings.projected: num[0 , 0 ]
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ global : logi FALSE
.. .. .. ..@ stdev : num [1:50] 5 3.39 3.2 2.78 1.94 ...
.. .. .. ..@ key : chr "PC_"
.. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "SeuratObject"] with 4 slots
.. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ]
.. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ]
.. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ]
.. .. .. .. .. ..@ overall.p.values : num[0 , 0 ]
.. .. .. ..@ misc :List of 1
.. .. .. .. ..$ total.variance: num 457
.. ..$ umap:Formal class 'DimReduc' [package "SeuratObject"] with 9 slots
.. .. .. ..@ cell.embeddings : num [1:2638, 1:2] -3.44 -7.94 -1.77 8.79 -1.51 ...
.. .. .. .. ..- attr(*, "scaled:center")= num [1:2] 1.64 -1.3
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC-1" "AAACATTGAGCTAC-1" "AAACATTGATCAGC-1" "AAACCGTGCTTCCG-1" ...
.. .. .. .. .. ..$ : chr [1:2] "UMAP_1" "UMAP_2"
.. .. .. ..@ feature.loadings : num[0 , 0 ]
.. .. .. ..@ feature.loadings.projected: num[0 , 0 ]
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ global : logi TRUE
.. .. .. ..@ stdev : num(0)
.. .. .. ..@ key : chr "UMAP_"
.. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "SeuratObject"] with 4 slots
.. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ]
.. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ]
.. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ]
.. .. .. .. .. ..@ overall.p.values : num[0 , 0 ]
.. .. .. ..@ misc : list()
..@ images : list()
..@ project.name: chr "SeuratProject"
..@ misc : list()
..@ version :Classes 'package_version', 'numeric_version' hidden list of 1
.. ..$ : int [1:3] 4 1 3
..@ commands :List of 7
.. ..$ NormalizeData.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
.. .. .. ..@ name : chr "NormalizeData.RNA"
.. .. .. ..@ time.stamp : POSIXct[1:1], format: "2023-05-21 03:58:21"
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ call.string: chr "NormalizeData(seurat)"
.. .. .. ..@ params :List of 5
.. .. .. .. ..$ assay : chr "RNA"
.. .. .. .. ..$ normalization.method: chr "LogNormalize"
.. .. .. .. ..$ scale.factor : num 10000
.. .. .. .. ..$ margin : num 1
.. .. .. .. ..$ verbose : logi TRUE
.. ..$ FindVariableFeatures.RNA:Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
.. .. .. ..@ name : chr "FindVariableFeatures.RNA"
.. .. .. ..@ time.stamp : POSIXct[1:1], format: "2023-05-21 03:58:22"
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ call.string: chr "FindVariableFeatures(seurat, nfeatures = 500)"
.. .. .. ..@ params :List of 12
.. .. .. .. ..$ assay : chr "RNA"
.. .. .. .. ..$ selection.method : chr "vst"
.. .. .. .. ..$ loess.span : num 0.3
.. .. .. .. ..$ clip.max : chr "auto"
.. .. .. .. ..$ mean.function :function (mat, display_progress)
.. .. .. .. ..$ dispersion.function:function (mat, display_progress)
.. .. .. .. ..$ num.bin : num 20
.. .. .. .. ..$ binning.method : chr "equal_width"
.. .. .. .. ..$ nfeatures : num 500
.. .. .. .. ..$ mean.cutoff : num [1:2] 0.1 8
.. .. .. .. ..$ dispersion.cutoff : num [1:2] 1 Inf
.. .. .. .. ..$ verbose : logi TRUE
.. ..$ ScaleData.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
.. .. .. ..@ name : chr "ScaleData.RNA"
.. .. .. ..@ time.stamp : POSIXct[1:1], format: "2023-05-21 03:58:23"
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ call.string: chr [1:2] "ScaleData(seurat, vars.to.regress = c(\"nFeature_RNA\", " " \"percent.mt\"))"
.. .. .. ..@ params :List of 11
.. .. .. .. ..$ features : chr [1:500] "PPBP" "LYZ" "S100A9" "IGLL5" ...
.. .. .. .. ..$ assay : chr "RNA"
.. .. .. .. ..$ vars.to.regress : chr [1:2] "nFeature_RNA" "percent.mt"
.. .. .. .. ..$ model.use : chr "linear"
.. .. .. .. ..$ use.umi : logi FALSE
.. .. .. .. ..$ do.scale : logi TRUE
.. .. .. .. ..$ do.center : logi TRUE
.. .. .. .. ..$ scale.max : num 10
.. .. .. .. ..$ block.size : num 1000
.. .. .. .. ..$ min.cells.to.block: num 2638
.. .. .. .. ..$ verbose : logi TRUE
.. ..$ RunPCA.RNA :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
.. .. .. ..@ name : chr "RunPCA.RNA"
.. .. .. ..@ time.stamp : POSIXct[1:1], format: "2023-05-21 03:58:25"
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ call.string: chr "RunPCA(seurat, npcs = 50)"
.. .. .. ..@ params :List of 10
.. .. .. .. ..$ assay : chr "RNA"
.. .. .. .. ..$ npcs : num 50
.. .. .. .. ..$ rev.pca : logi FALSE
.. .. .. .. ..$ weight.by.var : logi TRUE
.. .. .. .. ..$ verbose : logi TRUE
.. .. .. .. ..$ ndims.print : int [1:5] 1 2 3 4 5
.. .. .. .. ..$ nfeatures.print: num 30
.. .. .. .. ..$ reduction.name : chr "pca"
.. .. .. .. ..$ reduction.key : chr "PC_"
.. .. .. .. ..$ seed.use : num 42
.. ..$ RunUMAP.RNA.pca :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
.. .. .. ..@ name : chr "RunUMAP.RNA.pca"
.. .. .. ..@ time.stamp : POSIXct[1:1], format: "2023-05-21 03:58:35"
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ call.string: chr "RunUMAP(seurat, dims = 1:20)"
.. .. .. ..@ params :List of 26
.. .. .. .. ..$ dims : int [1:20] 1 2 3 4 5 6 7 8 9 10 ...
.. .. .. .. ..$ reduction : chr "pca"
.. .. .. .. ..$ assay : chr "RNA"
.. .. .. .. ..$ slot : chr "data"
.. .. .. .. ..$ umap.method : chr "uwot"
.. .. .. .. ..$ return.model : logi FALSE
.. .. .. .. ..$ n.neighbors : int 30
.. .. .. .. ..$ n.components : int 2
.. .. .. .. ..$ metric : chr "cosine"
.. .. .. .. ..$ learning.rate : num 1
.. .. .. .. ..$ min.dist : num 0.3
.. .. .. .. ..$ spread : num 1
.. .. .. .. ..$ set.op.mix.ratio : num 1
.. .. .. .. ..$ local.connectivity : int 1
.. .. .. .. ..$ repulsion.strength : num 1
.. .. .. .. ..$ negative.sample.rate: int 5
.. .. .. .. ..$ uwot.sgd : logi FALSE
.. .. .. .. ..$ seed.use : int 42
.. .. .. .. ..$ angular.rp.forest : logi FALSE
.. .. .. .. ..$ densmap : logi FALSE
.. .. .. .. ..$ dens.lambda : num 2
.. .. .. .. ..$ dens.frac : num 0.3
.. .. .. .. ..$ dens.var.shift : num 0.1
.. .. .. .. ..$ verbose : logi TRUE
.. .. .. .. ..$ reduction.name : chr "umap"
.. .. .. .. ..$ reduction.key : chr "UMAP_"
.. ..$ FindNeighbors.RNA.pca :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
.. .. .. ..@ name : chr "FindNeighbors.RNA.pca"
.. .. .. ..@ time.stamp : POSIXct[1:1], format: "2023-05-21 03:58:35"
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ call.string: chr "FindNeighbors(seurat, dims = 1:20)"
.. .. .. ..@ params :List of 17
.. .. .. .. ..$ reduction : chr "pca"
.. .. .. .. ..$ dims : int [1:20] 1 2 3 4 5 6 7 8 9 10 ...
.. .. .. .. ..$ assay : chr "RNA"
.. .. .. .. ..$ k.param : num 20
.. .. .. .. ..$ return.neighbor: logi FALSE
.. .. .. .. ..$ compute.SNN : logi TRUE
.. .. .. .. ..$ prune.SNN : num 0.0667
.. .. .. .. ..$ nn.method : chr "annoy"
.. .. .. .. ..$ n.trees : num 50
.. .. .. .. ..$ annoy.metric : chr "euclidean"
.. .. .. .. ..$ nn.eps : num 0
.. .. .. .. ..$ verbose : logi TRUE
.. .. .. .. ..$ force.recalc : logi FALSE
.. .. .. .. ..$ do.plot : logi FALSE
.. .. .. .. ..$ graph.name : chr [1:2] "RNA_nn" "RNA_snn"
.. .. .. .. ..$ l2.norm : logi FALSE
.. .. .. .. ..$ cache.index : logi FALSE
.. ..$ FindClusters :Formal class 'SeuratCommand' [package "SeuratObject"] with 5 slots
.. .. .. ..@ name : chr "FindClusters"
.. .. .. ..@ time.stamp : POSIXct[1:1], format: "2023-05-21 03:58:36"
.. .. .. ..@ assay.used : chr "RNA"
.. .. .. ..@ call.string: chr "FindClusters(seurat, resolution = 1)"
.. .. .. ..@ params :List of 10
.. .. .. .. ..$ graph.name : chr "RNA_snn"
.. .. .. .. ..$ modularity.fxn : num 1
.. .. .. .. ..$ resolution : num 1
.. .. .. .. ..$ method : chr "matrix"
.. .. .. .. ..$ algorithm : num 1
.. .. .. .. ..$ n.start : num 10
.. .. .. .. ..$ n.iter : num 10
.. .. .. .. ..$ random.seed : num 0
.. .. .. .. ..$ group.singletons: logi TRUE
.. .. .. .. ..$ verbose : logi TRUE
..@ tools : list()
My objective is
- Generate featurePlot which is I'm able to generate.
- From the drop down menu I'm able to select genes which are labeling the cells in the featurePlot
- Third one which Im not able to do is export the gene labelled cluster, I tried using this
cluster_data <- cbind(Cluster = seurat@meta.data$cluster, seurat@assays$RNA@counts) write.csv(cluster_data, "cluster.csv", row.names = TRUE)
This creates a data-frame but what I require is gene specific which i dont know how to do, I found the function to use is this FetchData()which can be used as this
data <- FetchData(object = object, vars = features, slot = slot)
Now how do I use this FetchData() where it can use the gene as input which is used to labell cells in the plot and can be exported as clusters?
Any help or suggestion is really helpful
