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app.R
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app.R
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# Sys.setenv("plotly_username"=" your_plotly_username")
# Sys.setenv("plotly_api_key"="your_api_key")
## test repo
# wd <- dirname(rstudioapi::getActiveDocumentContext()$path) # set wd as the current folder
# print(wd == getwd())
# print(wd)
# print(getwd())
# if (!wd == getwd()) {
# setwd(wd)
# }
print("start loading")
start.load <- Sys.time() ### time
if (length(find.package(package = "shiny", quiet = T)) > 0) {
library(shiny)
} else {
print("Package shiny not installed")
install.packages("shiny")
print("Package shiny installed")
library(shiny)
}
if (length(find.package(package = "cyjShiny", quiet = T)) > 0) {
library(cyjShiny)
} else {
remotes::install_github("cytoscape/cyjShiny")
library(cyjShiny)
}
if (length(find.package(package = "shinythemes", quiet = T)) > 0) {
library(shinythemes)
} else {
print("Package shinythemes not installed")
install.packages("shinythemes")
print("Package shinythemes installed")
library(shinythemes)
}
if (length(find.package(package = "rstudioapi", quiet = T)) > 0) {
library(rstudioapi)
} else {
install.packages("rstudioapi")
library(rstudioapi)
}
if (!length(find.package(package = "rlang", quiet = T)) > 0) {
install.packages("rlang")
}
#################################
if (length(find.package(package = "RColorBrewer", quiet = T)) > 0) {
library(RColorBrewer)
} else {
install.packages("RColorBrewer")
library(RColorBrewer)
}
if (length(find.package(package = "kohonen", quiet = T)) > 0) {
library(kohonen)
} else {
install.packages("kohonen")
library(kohonen)
}
if (length(find.package(package = "sparsepca", quiet = T)) > 0) {
library(sparsepca)
} else {
install.packages("sparsepca")
library(sparsepca)
}
if (length(find.package(package = "randomForest", quiet = T)) > 0) {
library(randomForest)
} else {
install.packages("randomForest")
library(randomForest)
}
if (length(find.package(package = "cluster", quiet = T)) > 0) {
library(cluster)
} else {
install.packages("cluster")
library(cluster)
}
if (length(find.package(package = "ComplexHeatmap", quiet = T)) > 0) {
library(ComplexHeatmap)
} else {
install.packages("ComplexHeatmap")
library(ComplexHeatmap)
}
## for t-sne
# if (length(find.package(package = "reticulate", quiet = T)) > 0) {
# library(reticulate)
# } else {
# install.packages("reticulate")
# library(reticulate)
# }
if (length(find.package(package = "Rtsne", quiet = T)) > 0) {
library(Rtsne)
} else {
install.packages("Rtsne")
library(Rtsne)
}
######################################For Report Generation#################
if (length(find.package(package = "webshot2", quiet = T)) > 0) {
library(webshot2)
} else {
remotes::install_github("rstudio/webshot2")
library(webshot2)
}
library(gridExtra)
library(plotly)
if (!require("processx")) install.packages("processx")
library(png)
if (length(find.package(package = "capture", quiet = T)) > 0) {
library(capture)
} else {
remotes::install_github("dreamRs/capture")
library(capture)
}
####################### Dependencies For RAFSIL ###################################
# if (length(find.package(package = "RAFSIL", quiet = T)) > 0) {
# library(RAFSIL)
# }
if (length(find.package(package = "gridGraphics", quiet = T)) > 0) {
library(gridGraphics)
} else {
install.packages("gridGraphics")
library(gridGraphics)
}
if (length(find.package(package = "gridExtra", quiet = T)) > 0) {
library(gridExtra)
} else {
install.packages("gridExtra")
library(gridExtra)
}
if (length(find.package(package = "tidyverse", quiet = T)) > 0) {
library(tidyverse)
} else {
install.packages("tidyverse", dependencies = TRUE)
library(tidyverse)
}
if (length(find.package(package = "ggpubr", quiet = T)) > 0) {
library(ggpubr)
} else {
install.packages("ggpubr")
library(ggpubr)
}
if (length(find.package(package = "networkD3", quiet = T)) > 0) {
library(networkD3)
} else {
install.packages("https://cran.r-project.org/src/contrib/Archive/networkD3/networkD3_0.2.10.tar.gz", repo=NULL, type="source")
library(networkD3)
}
if (length(find.package(package = "data.tree", quiet = T)) > 0) {
library(data.tree)
} else {
install.packages("data.tree")
library(data.tree)
}
if (length(find.package(package = "bubbles", quiet = T)) > 0) {
library(bubbles)
} else {
devtools::install_github("jcheng5/bubbles", upgrade = FALSE)
library(bubbles)
}
###################################################################################
####################### Dependencies For Uniprot ###################################
if (length(find.package(package = "UniprotR", quiet = T)) > 0) {
library(UniprotR)
} else {
install.packages("UniprotR")
library(UniprotR)
}
if (length(find.package(package = "scales", quiet = T)) > 0) {
library(scales)
} else {
install.packages("scales")
library(scales)
}
###################################################################################
####################### Dependencies For Pathway Enrichment ###################################
if (length(find.package(package = "gprofiler2", quiet = T)) > 0) {
library(gprofiler2)
} else {
install.packages("gprofiler2")
library(gprofiler2)
}
###################################################################################
####################### Dependencies For Protein Interactions ###################################
if (length(find.package(package = "httr", quiet = T)) > 0) {
library(httr)
} else {
install.packages("httr")
library(httr)
}
if (length(find.package(package = "curl", quiet = T)) > 0) {
library(curl)
} else {
install.packages("curl")
library(curl)
}
if (length(find.package(package = "later", quiet = T)) > 0) {
library(later)
} else {
install.packages("later")
library(later)
}
if (length(find.package(package = "qdapTools", quiet = T)) > 0) {
library(qdapTools)
} else {
install.packages("qdapTools")
library(qdapTools)
}
if (length(find.package(package = "alakazam", quiet = T)) > 0) {
library(alakazam)
} else {
install.packages("https://cran.r-project.org/src/contrib/Archive/alakazam/alakazam_1.0.0.tar.gz", repo=NULL, type="source")
library(alakazam)
}
if (length(find.package(package = "msa", quiet = T)) > 0) {
library(msa)
} else {
BiocManager::install("msa", update = FALSE)
library(msa)
}
if (length(find.package(package = "ape", quiet = T)) > 0) {
library(ape)
} else {
install.packages("ape")
library(ape)
}
if (length(find.package(package = "seqinr", quiet = T)) > 0) {
library(seqinr)
} else {
install.packages("seqinr")
library(seqinr)
}
if (length(find.package(package = "qdapRegex", quiet = T)) > 0) {
library(qdapRegex)
} else {
install.packages("qdapRegex")
library(qdapRegex)
}
###################################################################################
####################### Dependencies For Co-expression ###################################
if (length(find.package(package = "shinyjs", quiet = T)) > 0) {
library(shinyjs)
} else {
install.packages("shinyjs")
library(shinyjs)
}
###################################################################################
####################### Dependencies For Microarray ###################################
if (length(find.package(package = "devtools", quiet = T)) > 0) {
library(devtools)
} else {
install.packages("devtools")
library(devtools)
}
if (length(find.package(package = "remotes", quiet = T)) > 0) {
library(remotes)
} else {
devtools::install_github("r-lib/remotes")
library(remotes)
}
if (length(find.package(package = "maEndToEnd", quiet = T)) > 0) {
suppressPackageStartupMessages({library("maEndToEnd")})
}
# else {
# remotes::install_github("b-klaus/maEndToEnd", ref="master")
# suppressPackageStartupMessages({library("maEndToEnd")})
# }
if (length(find.package(package = "oligoClasses", quiet = T)) > 0) {
library(oligoClasses)
} else {
print("Package oligoClasses not installed")
BiocManager::install("oligoClasses")
print("Package oligoClasses installed")
library(oligoClasses)
}
if (length(find.package(package = "ArrayExpress", quiet = T)) > 0) {
library(ArrayExpress)
} else {
print("Package ArrayExpress not installed")
BiocManager::install("ArrayExpress")
print("Package ArrayExpress installed")
library(ArrayExpress)
}
if (length(find.package(package = "pd.hugene.1.0.st.v1", quiet = T)) > 0) {
library(pd.hugene.1.0.st.v1)
} else {
print("Package pd.hugene.1.0.st.v1 not installed")
BiocManager::install("pd.hugene.1.0.st.v1")
print("Package pd.hugene.1.0.st.v1 installed")
library(pd.hugene.1.0.st.v1)
}
if (length(find.package(package = "hugene10sttranscriptcluster.db", quiet = T)) > 0) {
library(hugene10sttranscriptcluster.db)
} else {
print("Package hugene10sttranscriptcluster.db not installed")
BiocManager::install("hugene10sttranscriptcluster.db")
print("Package hugene10sttranscriptcluster.db installed")
library(hugene10sttranscriptcluster.db)
}
if (length(find.package(package = "arrayQualityMetrics", quiet = T)) > 0) {
library(arrayQualityMetrics)
} else {
print("Package arrayQualityMetrics not installed")
BiocManager::install("arrayQualityMetrics")
print("Package arrayQualityMetrics installed")
library(arrayQualityMetrics)
}
if (length(find.package(package = "limma", quiet = T)) > 0) {
library(limma)
} else {
print("Package limma not installed")
BiocManager::install("limma")
print("Package limma installed")
library(limma)
}
if (length(find.package(package = "topGO", quiet = T)) > 0) {
library(topGO)
} else {
print("Package topGO not installed")
BiocManager::install("topGO")
print("Package topGO installed")
library(topGO)
}
if (length(find.package(package = "ReactomePA", quiet = T)) > 0) {
library(ReactomePA)
} else {
print("Package ReactomePA not installed")
BiocManager::install("ReactomePA")
print("Package ReactomePA installed")
library(ReactomePA)
}
###################################For GEO import###############################
library(xml2)
if (length(find.package(package = "GEOquery", quiet = T)) > 0) {
library(GEOquery)
} else {
BiocManager::install("GEOquery")
library(GEOquery)
}
if (length(find.package(package = "umap", quiet = T)) > 0) {
library(umap)
} else {
install.packages("umap")
library(umap)
}
if (length(find.package(package = "maptools", quiet = T)) > 0) {
library(maptools)
} else {
install.packages("maptools", repos="http://R-Forge.R-project.org")
library(maptools)
}
###################################################################################
########################### Style files for Cytoscape.js ################
styles <- c(
"generic style"="./www/style/basicStyle.js",
"red-yellow"="./www/style/red-yellow.js",
"red-pink" = "./www/style/red-pink.js",
"green-blue"="./www/style/green-blue.js",
"green-blue(ppi)"="./www/style/green-blue(ppi).js")
##########################################################################
#
# ## sourcing util files
source("./www/utils.R")
source("./www/PhyscochemicalSep.R")
#
loadPkg()
id_to_name <- read.csv(paste0("./www/TransTable_Human.csv"))
#################### Complex Enrichment ##########################
complexes <- load(paste0("./www/allComplexes.RData")) #allComplexes is masked under complexes
up_corum_mapping <- read.csv(paste0("./www/UniProt_CORUM_Mapping.csv"))
##################################################################
end.load <- Sys.time()
print("loading time")
print(end.load - start.load)
##### UI from here ###########
ui <- tagList(
shinyjs::useShinyjs(),
navbarPage(
id = "navbar",
theme = shinytheme("flatly"),
title = "",
tabPanel(
"GeneCloudOmics",
br(),
sidebarLayout(
sidebarPanel(
img(
src = "GeneCloudOmics-logo.png",
width = "100%", height = "100%"
)
),
mainPanel(
h2("Welcome to GeneCloudOmics", align = "center",style = "color:#73C6B6;font-weight: bold;"),
p("The Biostatistical Tool for Gene Expression Data Analysis", align = "center"),
h4(span("GeneCloudOmics", style = "color:#73C6B6;font-weight: bold;")," an easy-to-use web server for high-throughput gene expression analysis a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do currently"),
h4(span("GeneCloudOmics", style = "color:#73C6B6;font-weight: bold;")," provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications."),
h4(span("Supported Transcriptome data:", style = "color:#73C6B6;font-weight: bold;"), " RNA-Seq and Microarray "),
h4(span("Data Preprocessing:", style = "color:#73C6B6;font-weight: bold;"), " GeneCloudOmics performs raw data normalization using four normalization methods RPKM,
FPKM, TPM and RUV. The raw vs. normalized data are visualized as boxplots and violin plots."),
h4(span("Differential Gene Expression (DGE) Analysis:", style = "color:#73C6B6;font-weight: bold;"), " GDE using five methods EdgeR, DESeq2 and NOISeq."),
h4(span("Bio-statistical Analysis:", style = "color:#73C6B6;font-weight: bold;"), " GeneCloudOmics provides the user with the following bio-statistical analyses:
Pearson and Spearman rank correlations, PCA, k-means and hierarchical clustering,
Shannon entropy and noise (square of the coefficient of variation), t-SNE, random forest and SOM analyses. All analyses include proper high-resolution visualization."),
h4(span("Bioinformatics Analysis of Gene and Protein sets:", style = "color:#73C6B6;font-weight: bold;"), " For the differential expressed genes (DEG), GeneCloudOmics provides
the users with multiple bioinformatics tools to investigate their
gene/protein list including gene ontology (GO), pathway enrichment analysis, PPI, co-expression, gene/protein function, subcellular localization, complex enrichment, protein domains, tissue expression, sequence properties (acidity, hydrophobicity and charge),
evolutionary analysis (gene tree, phylogenetic tree and species/chromosome location) and pathological analysis (diseases that these genes/proteins are involved in). The analyses include proper high-resolution visualization, when applicable."),
h4( #span("For questions and bug reporting, please write to", a("Mohamed Helmy", href="mailto:[email protected]", target="_blank"), "or ", a("Kumar Selvarajoo", href="mailto:[email protected]", target="_blank")),
withTags({
div(class="header", checked=NA,
h4("User manual can be found ",
a("here", href= "https://github.com/buithuytien/GeneCloudOmics/blob/master/GeneCloudOmics_Help_1.0.pdf", target="_blank")), # "GeneCloudOmics_Help_1.0.pdf"
h4(span("For questions and bug reporting, please write to",
a("Mohamed Helmy", href="mailto:[email protected]", target="_blank"), "or ",
a("Kumar Selvarajoo", href="mailto:[email protected]", target="_blank")),
)
)
})
)
)
)
),
navbarMenu('Preprocessing',
tabPanel(
"RNASeq Data",
value = "active_tab_rnaseq",
tabsetPanel(
id = "Rnaseq_pre",
tabPanel(
"Upload data",
sidebarPanel(
radioButtons(
"file_type", "Choose File Type",
c("Raw file (read count)" = "raw", "Normalised file" = "norm")
),
conditionalPanel(
condition = "input.file_type=='raw'", # raw
withTags({
div(class="header", checked=NA,
p("Example ",
a(target="_blank", href="https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Yeast%20Biofilm%202%20-%205%20genotypes/Yeast-biofilm2-raw.csv", "csv"),
a(target="_blank", href="https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Eg_raw.png", "image"))
)
}),
fileInput("file1", "Choose Raw Counts (required)"),
withTags({
div(class="header", checked=NA,
p("Example ",
a("csv", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Yeast%20Biofilm%202%20-%205%20genotypes/Yeast-biofilm2-length.csv"),
a("image", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Eg_gene_length.png")), # ADD EXAMPLE
)
}),
fileInput("length1", "Choose Gene Length (optional)"), # gene id + length
withTags({
div(class="header", checked=NA,
p("Example ",
a("csv", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Zebra%20fish%20microarray/zfGenes_neg_control.csv", target="_blank"),
a("image", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Eg_negative_control_genes.png", target="_blank")), # ADD EXAMPLE
)
}),
fileInput("spikes1", "Choose negative controls (eg. ERCC Spike-in) (optional)")
),
conditionalPanel(
condition = "input.file_type=='norm'", # normalized
withTags({
div(class = "header",
p("Example ",
a("csv", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Yeast%20Biofilm%202%20-%205%20genotypes/Yeast-biofilm2-normalized-tpm.csv", target="_blank"),
a("image", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Eg_normalised.png", target="_blank")), # ADD EXAMPLE
)
}),
fileInput("file2", "Choose Normalized Expression (required)")
# helpText("* Format requirement: CSV file. Gene names in rows and genotypes in columns, following the usual format of files deposited in the GEO database.")
),
withTags({
div(class = "header",
p("Example ",
a("csv", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Yeast%20Biofilm%202%20-%205%20genotypes/Yeast-Biofilm2-meta.csv", target="_blank"),
a("image", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Eg_metadata.png", target="_blank")), # ADD EXAMPLE
)
}),
fileInput("metafile1", "Choose Meta Data File (required)"),
actionButton("submit_input", "Submit")
),
mainPanel(
# h3("Welcome to GeneCloudOmics --"),
# h3("A Biostatistical tool for Transcriptomics Analysis"),
# img(
# src = "GeneCloudOmics-logo.png",
# width = 570, height = 370
# )
)
),
tabPanel(
"Preprocessing",
value="active_tab_preprocessing_rnaseq",
sidebarPanel(
h4("Filtering"),
splitLayout(
numericInput("min_val", "Min. value", min = 0.1, step = 0.1, value = 1.0),
numericInput("min_col", "Min. columns", min = 1, value = 2)
),
conditionalPanel(
condition = "input.file_type=='raw'",
radioButtons(
"norm_method", "Normalisation method",
c(
"None (Black)" = "None",
"RPKM (Gene length input required)" = "RPKM",
"FPKM (Gene length input required)" = "FPKM",
"TPM (Gene length input required)" = "TPM",
"RUV (Negative control genes input required)" = "RUV"
)
)
),
actionButton("submit_preprocessing", "Submit"),
conditionalPanel(
condition = "input.preprocessing_tabs == 'Data table' ",
br(),
br(),
downloadButton("download_norm_data", "Download table (csv)")
)
),
mainPanel(
h3("Preprocessing Rnaseq data"),
tabsetPanel(
type = "tabs", id = "preprocessing_tabs",
tabPanel(
"RLE plot",
conditionalPanel(
condition = "$('html').hasClass('shiny-busy')",
div(img(src = "load.gif", width = 240, height = 180),
h4("Processing ... Please wait"),
style = "text-align: center;"
)
),
conditionalPanel(
condition = "!$('html').hasClass('shiny-busy')",
plotOutput("RLE.plot2")
),
conditionalPanel(
condition = "input.file_type=='raw'",
conditionalPanel(
condition = "$('html').hasClass('shiny-busy')",
div(img(src = "load.gif", width = 240, height = 180),
h4("Processing ... Please wait"),
style = "text-align: center;"
)
),
conditionalPanel(
condition = "!$('html').hasClass('shiny-busy')",
plotOutput("RLE.plot")
)
)
),
tabPanel(
"Violin Plot",
conditionalPanel(
condition = "$('html').hasClass('shiny-busy')",
div(img(src = "load.gif", width = 240, height = 180),
h4("Processing ... Please wait"),
style = "text-align: center;"
)
),
conditionalPanel(
condition = "!$('html').hasClass('shiny-busy')",
plotlyOutput("violin_plot2")
),
conditionalPanel(
condition = "!$('html').hasClass('shiny-busy')",
plotlyOutput("violin_plot")
)
),
tabPanel(
"Data table",
h3("Normalized data"),
DT::dataTableOutput("norm_table")
),
tabPanel(
"Description table",
h3("Data description"),
DT::dataTableOutput("meta_table")
)
)
)
)
)
),
tabPanel(
"Microarray Data",
value = "active_tab_micro",
sidebarPanel(
withTags({
div(class = "header",
p("Example data ", a("here", href = "https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-2967/", target="_blank")), # ADD EXAMPLE ( have to change )
)
}),
fileInput("file_micro", "Choose Microarray Data"),
downloadButton("downloadMicroRaw", "Download Raw Data as CSV"),
br(), br(),
downloadButton("downloadMicroMeta", "Download Meta Data as CSV")
),
mainPanel(
h3("Preprocessing Microarray Data"),
conditionalPanel(
condition = "$('html').hasClass('shiny-busy')",
div(img(src = "load.gif", width = 240, height = 180),
h4("Processing ... Please wait"),
style = "text-align: center;"
)
)
)
),
tabPanel(
"GEO Data Import",
value = "active_tab_geo",
sidebarPanel(
tabsetPanel(type="tabs", id = "geo_tab",
tabPanel("GEO DATA",
div(class= "header","For Example: GSE153941"),
textInput("geo_acc_no", "Enter Accession Number", value = "", width = NULL, placeholder = NULL),
radioButtons("file_type_button","FILE TYPE",
c("RnaSeq","Microarray","Auto")),
actionButton("submit_geo_acc_no", "Submit")),
tabPanel("PREPROCESSING",value="geo_pre",radioButtons("file_name_button","SELECT FILE",
c("a")),
withTags({
div(class="header", checked=NA,
p("Example ",
a("csv", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Yeast%20Biofilm%202%20-%205%20genotypes/Yeast-biofilm2-length.csv"),
a("image", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Eg_gene_length.png")), # ADD EXAMPLE
)
}),
fileInput("length2", "Choose Gene Length (optional)"), # gene id + length
withTags({
div(class="header", checked=NA,
p("Example ",
a("csv", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Zebra%20fish%20microarray/zfGenes_neg_control.csv", target="_blank"),
a("image", href = "https://github.com/buithuytien/GeneCloudOmics/blob/master/Test%20data/Eg_negative_control_genes.png", target="_blank")), # ADD EXAMPLE
)
}),
fileInput("spikes2", "Choose negative controls (eg. ERCC Spike-in) (optional)"),
actionButton("submit_geo_preprocessing", "Submit"),
)
)),
mainPanel(
h3("GEO DATA IMPORT"),
tabsetPanel(type ="tabs",
tabPanel(
uiOutput("help_text_geo")
)),
h4("Overview of GEO Imported Data"),
tabsetPanel(type ="tabs", tabPanel("Box Plot",plotOutput("geo_box_plot")),
tabPanel("Expression Density", plotOutput("geo_expr_plot")),
tabPanel("Mean variance", plotOutput("geo_mean_plot")),
tabPanel("UMAP", plotOutput("geo_umap_plot")))
)
)
),
navbarMenu('Transcriptome Analysis',
tabPanel(
" Scatter ",
sidebarPanel(
selectInput(inputId = "scatter.x", label = "X-axis", choices = ""),
selectInput(inputId = "scatter.y", label = "Y-axis", choices = ""),
radioButtons(
"trans", "Transformation:",
c("None", "Natural log", "log2", "log10")
),
checkboxInput("regline", "Display regression line", value = FALSE),
actionButton("submit_scatter", "Plot"),
br(),
br(),
downloadButton("downloadscatter", "Download as PDF"),
#h6("Download all pairs of samples in one PDF (this may take some time to run) :"),
br(),
br(),
actionButton("add_scatter","Add to report"),
downloadButton("downloadscatter_collage", "Download collage")
),
mainPanel(
h3("Heatscatter"),
uiOutput("help_text_scatter"),
plotlyOutput("scatter.plot"),
# tags$script('
# document.getElementById("downloadscatter").onclick = function() {
# var gd = document.getElementById("scatter.plot");
# Plotly.Snapshot.toImage(gd, {format: "png"}).once("success", function(url) {
# var a = window.document.createElement("a");
# a.href = url;
# a.type = "image/png";
# a.download = "plot.png";
# document.body.appendChild(a);
# a.click();
# document.body.removeChild(a);
# });
# }
# ')
)
),
tabPanel(
"Distribution Fit",
sidebarPanel(
conditionalPanel(
condition = "input.dist_tabs=='Distribution Fit'",
selectInput(inputId = "dist.var", label = "Choose a column", choices = colnames("dataset")),
checkboxGroupInput("distributions", "Distributions:",
choices = c("Log-normal", "Log-logistic", "Pareto", "Burr", "Weibull", "Gamma"), selected = c("Log-normal", "Pareto")
),
radioButtons("dist_zoom", "Zoom to see fit", c("slider", "text input")),
conditionalPanel(
condition = "input.dist_zoom=='slider'",
sliderInput("dist_range", "Range:",
min = 0.1, max = 1000, step = 1,
value = c(0.1, 1000)
)
),
actionButton("submit_distfit","Plot"),
conditionalPanel(
condition = "input.dist_zoom=='text input'",
textOutput("dist_range_allowed"),
numericInput("dist_range_min", "min", value = 0.1, min = 0.1, max = 1000),
numericInput("dist_range_max", "max", value = 1000, min = 0.1, max = 1000)
),
downloadButton("downloaddist", "Download as PDF")
),
conditionalPanel(
condition = "input.dist_tabs=='AIC table'",
downloadButton("downloaddistaic", "Download as CSV")
),
checkboxInput("checkbox_distfit", label = "Distribution Fit", value = FALSE),
checkboxInput("checkbox_aic", label = "AIC table", value = FALSE),
actionButton("add_distfit","Add to report")
),
mainPanel(
h3("Distribution Fit"),
tabsetPanel(
type = "tabs", id = "dist_tabs",
tabPanel(
"Distribution Fit",
uiOutput("help_text_dis_fit"),
plotOutput("dist.plot")),
tabPanel(
"AIC table",
conditionalPanel(
condition = "$('html').hasClass('shiny-busy')",
div(img(src = "load.gif", width = 240, height = 180),
h4("Processing ... Please wait"),
style = "text-align: center;"
)
),
conditionalPanel(
condition = "!$('html').hasClass('shiny-busy')",
div(tableOutput("dist.aic"), style = "font-size:80%")
)
)
)
)
),
tabPanel(
" Correlation ",
sidebarPanel(
radioButtons(
"cor_method", "Method:",
c("Pearson correlation", "Spearman correlation")
),
actionButton("submit_corr","Plot"),
br(),
br(),
conditionalPanel(
condition = "input.cor_tabs == 'Correlation heatmap'",
downloadButton("downloadcorrplot", "Download as PDF")
),
conditionalPanel(
condition = "input.cor_tabs == 'Correlation plot'",
downloadButton("downloadcorrplot2", "Download as PDF")
),
conditionalPanel(
condition = "input.cor_tabs == 'Correlation matrix'",
downloadButton("downloadcorrmat", "Download as CSV")
),
checkboxInput("checkbox_corrheatmap", label = "Correlation Heatmap", value = FALSE),
checkboxInput("checkbox_corrplot", label = "Correlation Plot", value = FALSE),
checkboxInput("checkbox_corrmat", label = "Correlation Matrix", value = FALSE),
actionButton("add_correlation","Add to report"),
),
mainPanel(
conditionalPanel(
condition = "input.cor_method=='Pearson correlation'",
h3("Pearson correlation")
),
conditionalPanel(
condition = "input.cor_method=='Spearman correlation'",
h3("Spearman correlation")
),
tabsetPanel(
type = "tabs", id = "cor_tabs",
tabPanel(
"Correlation heatmap",
uiOutput("help_text_correlation"),
plotOutput("corr.plot")),
tabPanel("Correlation plot", plotOutput("corr.plot2")),
tabPanel("Correlation matrix", div(tableOutput("corr.matrix"), style = "font-size:80%"))
)
)
),
tabPanel(
"PCA",
sidebarPanel(
conditionalPanel(
condition = "input.pca_tabs == 'PCA-2D plot'",
selectInput(inputId = "pca.x", label = "X-axis", choices = ""),
selectInput(inputId = "pca.y", label = "Y-axis", choices = "")
),
selectInput(inputId = "gene_size", label = "Gene sample size", choices = ""),
radioButtons(
"gene_order", "Gene sample order (wrt column 1)",
c("Descending (highest to lowest)" = "Descending", "Ascending (lowest to highest)" = "Ascending", "Random")
),
conditionalPanel(
condition = "input.pca_tabs == 'PCA-2D plot' || input.pca_tabs == 'PCA-3D plot'",
checkboxInput("pca_cluster", strong("Kmeans clustering on columns"), FALSE),
conditionalPanel(
condition = "input.pca_cluster == true",
sliderInput("pca_cluster_num", "Number of clusters:", value = 1, min = 1, max = 1, step = 1),
checkboxInput("pca_text", strong("Display sample name"), FALSE)
)
),
######################################
radioButtons(
"pca_type", "Type of PCA",
c("PCA" = "PCA", "Sparse PCA" = "SPCA")
),
######################################
conditionalPanel(
condition = "input.gene_order=='Random'",
helpText("* Click multiple times to resample"),
actionButton("pca_refresh", "Resample", style = "background-color: #337ab7;border-color:#337ab7"),
br(), br()
),
actionButton("submit_pca","Plot"),
conditionalPanel(
condition = "input.pca_tabs == 'PCA variance'",
downloadButton("downloadpcavar", "Download as PDF")
),
conditionalPanel(
condition = "input.pca_tabs == 'PCA-2D plot'",
downloadButton("downloadpca2d", "Download as PDF")
),
conditionalPanel(
condition = "input.pca_tabs == 'PCA-3D plot'",
downloadButton("downloadpca3d", "Download as PDF")
),
checkboxInput("checkbox_pcavar", label = "PCA Variance", value = FALSE),
checkboxInput("checkbox_pca2d", label = "PCA-2D Plot", value = FALSE),
checkboxInput("checkbox_pca3d", label = "PCA3D Plot", value = FALSE),
actionButton("add_pca","Add to report")
),
mainPanel(
h3("PCA"),
tabsetPanel(
type = "tabs", id = "pca_tabs",
tabPanel("PCA variance",
uiOutput("help_text_PCA"),
plotlyOutput("pcavar.plot")),
tabPanel("PCA-2D plot", plotlyOutput("pca2d.plot")),
tabPanel("PCA-3D plot", plotlyOutput("pca3d.plot"))
)
)
),
tabPanel(
"DE Analysis",
sidebarPanel(
radioButtons("n_rep", "Replicates?", choices = c("Multiple" = 1, "Single" = 0)),
conditionalPanel(
condition = "input.n_rep=='1'",
radioButtons("de_method1", "DE Method", choices = c("EdgeR", "DESeq2", "NOISeq"))
),
conditionalPanel(
condition = "input.n_rep=='0'",
radioButtons("de_method0", "DE Method", choices = c("NOISeq"))
),
h5("Choose 2 experiment conditions for DE analysis"),
selectInput("f1", "Condition 1", choices = ""),
selectInput("f2", "Condition 2", choices = ""),
h5("DE criteria"),
splitLayout(
numericInput("p_val", "FDR", min = 0.01, max = 1, value = 0.05, step = 0.01),
numericInput("fc", "Fold Change", min = 1, value = 2, step = 0.1)
),
fluidRow(
column(
4,
actionButton("submit_DE", "Plot")
),
column(
6,
conditionalPanel(
condition = "input.DE_tabs=='DE genes' ",
downloadButton("download_de_table", "Download table (csv)")
),
conditionalPanel(
condition = "input.DE_tabs=='Volcano plot' ",
downloadButton("download_volcano", "Download plot (PDF)")
),
conditionalPanel(
condition = "input.DE_tabs=='Dispersion plot' ",
downloadButton("download_dispersion", "Download plot (PDF)")
),
br(),
br(),