SEvisual provides an interactive visualization shiny-based App for exploring data stored in SummarizedExperiment object, currently supporting heatmap and MDS/PCA plot.

• Interactive visualization

  • Use ComplexHeatmap and plotly to create heatmap and MDS/PCA plot respectively

• Modern UI

  • The UI is built with bslib, which is a Bootstrap 5 theme for shiny. It is a modern, clean and highly customizable theme for shiny applications

• Handling of missing values (NA)

  • deletion or K-nearest neighbors (KNN) imputation using impute

• Mandatory clustering

  • When encountering the error "NA/NaN/Inf in foreign function call...", clustering is mandatory.

• Support SummarizedExperiment object as input

  • SummarizedExperiment is used to store rectangular matrices of experimental results, which is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc.) and columns represent samples

• Support data.frame as input

  • If the input data is a data.frame, it is automatically converted to a SummarizedExperiment object, assuming that all values are numeric

• Support qs file as input

  • This .qs file contains SummarizedExperiment object, which can be loaded directly into the app

• Online

  • The SEvisual shiny app is also hosted on the website: SEvisual. (N.B. This may be slow and interrupted due to overlapping memory usage.)

• Install Dependency Packages

    if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager")
    packages <- c('tidyverse', "cli", "shiny", "bslib", "plotly", "qs", "SummarizedExperiment", 
                "htmltools", "colourpicker", "RColorBrewer", "shinyWidgets", "waiter", "shinyalert",
                "DT", "shinycustomloader", "GetoptLong", "kableExtra", "circlize", "impute",
                "ComplexHeatmap", "InteractiveComplexHeatmap", "pheatmap", "bsicons")
    for(i in 1:length(packages)){
        if (!requireNamespace(packages[i], quietly=TRUE)){
            BiocManager::install(packages[i], update=FALSE)
        }
    }

• Install SEvisual Package

    if (!requireNamespace("SEvisual", quietly=TRUE)) devtools::install_github("SEvisual/SEvisual")

#SummarizedExperiment 
    if (!require("BiocManager", quietly=TRUE)) install.packages("BiocManager")
    BiocManager::install("airway")
    library(airway)
    data(airway)            #SummarizedExperiment with annotation
    SEvisual(airway)

#data.frame
    SEvisual(mtcars)        #automatically convert to SummarizedExperiment but without annotation
    SEvisual(iris)          #non-numeric values will be forcibly converted to numeric(eg. Species)

#data.frame convert to SummarizedExperiment with annotation
    colData <- data.frame(type=sample(c("A", "B"), ncol(mtcars), replace=TRUE)) #columns annotations
    rowData <- data.frame(car=rownames(mtcars))                                 #rows annotations 
    mtcars.SE <- SummarizedExperiment(assay=mtcars, colData=colData, rowData=rowData)
    SEvisual(mtcars.SE)

Since SEvisual covers a wide packages of uses, it has numerous getting started articles, each focusing on a particular interest:

There are two main places to get help with SEvisual:

• The github is a friendly place to ask any questions and report issues/bugs

• send email to [email protected]