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app.R
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app.R
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####### Install R packages automatically #########
source("install_R_packages.R")
######## Load R packages ###############
library(devtools)
require(usethis)
library(shiny)
library(servr)
library(ggplot2)
library(pheatmap)
library(M3C)
library(RUVSeq)
library(scales)
library(dtwclust)
library(dplyr)
library(DESeq2)
library(ggcorrplot)
library(tibble)
library(ReactomePA)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(AnnotationDbi)
library(EnhancedVolcano)
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(TxDb.Mmusculus.UCSC.mm9.knownGene)
library(clusterProfiler)
library(cowplot)
library(scater)
library(MAST)
library(Seurat)
library(shinycssloaders)
library(shinyWidgets)
library(hdf5r)
library(enrichR)
#########################
#### Setup enrichR
setEnrichrSite("Enrichr")
websiteLive <- TRUE
dbs <- listEnrichrDbs()
dbs = "KEGG_2016"
################
options(shiny.maxRequestSize=10000*1024^2)
ui <- fluidPage(
tags$head(includeHTML(("GoogleAnalytics.html"))),
setBackgroundColor(
color = c("#F7FBFF", "#2171B5"),
gradient = "linear",
direction = "bottom"
),
titlePanel(h1("Computational Suite For Bioinformaticians and Biologists",style="font-size:20px;color:DarkBlue;font-weight: bold;"),windowTitle = "Computational Suite For Bioinformaticians and Biologists"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Slider for the number of Dataset ----
selectInput("Module",
label = "Module",
choices = c("Normalization", "Basic Stats", "Visualization", "Differential Expression", "Correlation Profiles", "Functional and Pathway Enrichment", "ChIP-ATAC Seq Analysis", "Single Cell RNASeq Analysis"),
selected = "Visualization"),
conditionalPanel(
condition = "input.Module == 'Normalization' || input.Module == 'Visualization' || input.Module == 'Basic Stats' || input.Module == 'Correlation Profiles'",
fileInput("File",
label = "Upload Expression File (Accepted Format: tab delimited text)",
accept = c("text", "text", ".txt"))),
conditionalPanel(
condition = "input.Module == 'Functional and Pathway Enrichment'",
fileInput("GeneList_FP",
label = "Upload a Gene List (Accepted Format: .txt)",
accept = c("text", "text", ".txt"))),
conditionalPanel(
condition = "input.Module == 'Differential Expression'",
fileInput("Counts",
label = "Upload Counts File (Accepted Format: tab delimited text)",
accept = c("text", "text", ".txt"))),
conditionalPanel(
condition = "input.Module == 'Normalization'",
selectInput("Norm",
label = "Choose Normalization",
choices = c("upper quantile", "median", "full", "log2", "zScore", "none"),
selected = "upper quantile")),
conditionalPanel(
condition = "input.Module == 'Differential Expression'",
selectInput("Controls",
label = "Select Control Samples",
choices = NULL,
selected = NULL,
multiple = TRUE)),
conditionalPanel(
condition = "input.Module == 'Differential Expression'",
selectInput("Treatments",
label = "Select Treatment Samples",
choices = NULL,
selected = NULL,
multiple = TRUE)),
conditionalPanel(
condition = "input.Module == 'Differential Expression'",
numericInput("DEFilterLog",
label = "Log2 Fold Change Cutoff",
value = 0.5)),
conditionalPanel(
condition = "input.Module == 'Differential Expression'",
numericInput("DEFilterFDR",
label = "False Discovery Rate (FDR) Cutoff",
value = 0.1)),
conditionalPanel(
condition = "input.Module == 'Correlation Profiles'",
selectInput("Correlation",
label = "Get Correlation among",
choices = c("Genes","Samples"),
selected = "Samples",
multiple = FALSE)),
conditionalPanel(
condition = "input.Module == 'Correlation Profiles' && input.Correlation == 'Genes'",
fileInput("GeneList",
label = "Upload Gene List",
accept = c("text", "text", ".txt"))),
conditionalPanel(
condition = "input.Module == 'Correlation Profiles'",
selectInput("CorrelationMethod",
label = "Select Correlation Method",
choices = c("pearson", "kendall", "spearman"),
selected = "pearson")),
conditionalPanel(
condition = "input.Module == 'Normalization' || input.Module == 'Visualization' || input.Module == 'Differential Expression'",
selectInput("PlotType",
label = "Visualization",
choices = c("pca", "tsne", "heatmap"),
selected = "pca")),
conditionalPanel(
condition = "input.PlotType == 'tsne'",
sliderInput("perplexity",
label = "Choose a perplexity value (If number of samples <= 50 : recommended value 10",
min = 2,
max = 30,
value = 10)),
conditionalPanel(
condition = "input.PlotType == 'tsne'",
fileInput("GroupFile",
label = "Upload a Sample Group File",
accept = c("text", "text", ".txt"))),
conditionalPanel(
condition = "input.PlotType == 'heatmap'",
selectInput("Cluster",
label = "Perform Clustering on",
choices = c("Rows", "Columns", "Rows and Columns", "None"),
selected = "Rows and Columns")),
conditionalPanel(
condition = "input.PlotType == 'heatmap'",
selectInput("Scaling",
label = "Perform Scaling on",
choices = c("row", "column", "none"),
selected = "row")),
conditionalPanel(
condition = "input.PlotType == 'Functional and Pathway Enrichment'",
selectInput("SpeciesUse",
label = "Choose Species",
choices = c("human", "mouse"),
selected = "human")),
conditionalPanel(
condition = "input.Module == 'Functional and Pathway Enrichment'",
selectInput("Species",
label = "Choose Species",
choices = c("human", "mouse"),
selected = "human")),
conditionalPanel(
condition = "input.Module == 'ChIP-ATAC Seq Analysis'",
fileInput("PeakFile",
label = "Upload Peak File (Peak file in .narrowPeak, .broadPeak or .bed and in .gz format are accepted",
accept = c(".narrowPeak", ".broadPeak", ".bed", ".narrowPeak.gz", ".broadPeak.gz", ".bed.gz"))),
conditionalPanel(
condition = "input.Module == 'ChIP-ATAC Seq Analysis'",
selectInput("SpeciesChIP",
label = "Choose Species and genome version",
choices = c("human (hg19)", "human (hg38)", "mouse (mm10)", "mouse (mm9)"),
selected = "human (hg19)")),
conditionalPanel(
condition = "input.Module == 'ChIP-ATAC Seq Analysis'",
selectInput("PlotChIP",
label = "Select Analysis for Peaks",
choices = c("Coverage Plot (Visualize coverage of peaks across chromosomes)", "Average Profile of peaks binding to TSS regions", "Peak Annotation (finding closest genes, postion of Peaks wrt TSS etc.)"),
selected = "Peak Annotation (finding closest genes, postion of Peaks wrt TSS etc.)")),
conditionalPanel(
condition = "input.Module == 'ChIP-ATAC Seq Analysis' && input.PlotChIP == 'Peak Annotation (finding closest genes, postion of Peaks wrt TSS etc.)'",
selectInput("PlotAnnotation",
label = "Select Visualization",
choices = c("Peak genomic annotation", "Functional enrichemnt"),
selected = "Peak genomic annotation")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis'",
selectInput("scInput",
label = "Select Data Input Type",
choices = c("Raw Counts Matrix", "H5", "Seurat Object"),
selected = "Raw Counts Matrix")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix'",
fileInput("scCounts",
label = "Upload Counts File (Accepted Format: tab delimited text)",
accept = ".txt")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'H5'",
fileInput("scH5",
label = "Upload H5 output from Cellranger or other toolkits (Accepted Format: H5)",
accept = ".h5")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Seurat Object'",
fileInput("scRobj",
label = "Upload .rds file",
accept = ".rds")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
selectInput("Species_singlecell",
label = "Select Species",
choices = c("human", "mouse"),
selected = "human")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
numericInput("scMinCells",
label = "Input Minimum number of cells to express all genes",
value = 3,
min = 0,
max = 200000),
verbatimTextOutput("3")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
numericInput("scMinFeatures",
label = "Input Minimum number of features all cells should express",
value = 100,
min = 0,
max = 30000),
verbatimTextOutput("100")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
selectInput("scNormalization",
label = "Select Normalization Method",
choices = c("LogNormalize", "SCTransform"),
selected = "LogNormalize")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
selectInput("scReduction",
label = "Select Dimension Reduction",
choices = c("umap", "tsne"),
selected = "umap")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
selectInput("scDETest",
label = "Select Differential Expression Test (Please see: some of these tests increase run time significantly)",
choices = c("wilcox", "bimod", "roc", "t", "negbinom", "poisson", "LR", "MAST", "DESeq2"),
selected = "wilcox")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
selectInput("scCellCycle",
label = "Regress Cell Cycle Effect",
choices = c("Yes", "No"),
selected = "Yes")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
numericInput("VarFeatures",
label = "Input Number of Variable Features to use",
value = 2000,
min = 100,
max = 10000),
verbatimTextOutput("2000")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
numericInput("scDims",
label = "Input Number of Dimensions to use",
value = 10,
min = 1,
max = 100),
verbatimTextOutput("10")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
numericInput("scRes",
label = "Input resolution for clustering",
value = 0.5,
min = 0,
max = 10),
verbatimTextOutput("0.5")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5' || input.scInput == 'Seurat Object'",
selectInput("scVisualization",
label = "Select Single Cell Visualization",
choices = c("Gene Expression Plot", "Dimension Reduction Plot", "Violin Plot", "DotPlot", "QC Metrics Plot", "Cell Cycle Phase", "Top10 Markers Heatmap"),
selected = "Dimension Reduction Plot")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Seurat Object'",
selectInput("MarkerCalculate",
label = "Find Differential Markers per Cluster (Please set to TRUE if interested in Markers Heatmap)",
choices = c("TRUE", "FALSE"),
selected = "FALSE")),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5' || input.scInput == 'Seurat Object' && input.scVisualization == 'Dimension Reduction Plot' || input.scVisualization == 'Gene Expression Plot' || input.scVisualization == 'Violin Plot' || input.scVisualization == 'DotPlot'",
selectInput("ColorCells",
label = "Color/Group Cells By",
choices = NULL,
selected = NULL)),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5' || input.scInput == 'Seurat Object' && input.scVisualization == 'Gene Expression Plot' || input.scVisualization == 'Violin Plot' || input.scVisualization == 'DotPlot'",
selectInput("scGene",
label = "Select Genes",
choices = NULL,
selectize = TRUE,
selected = NULL,
multiple = TRUE)),
conditionalPanel(
condition = "input.Module == 'Single Cell RNASeq Analysis' && input.scInput == 'Raw Counts Matrix' || input.scInput == 'H5'",
downloadButton('scRNAObjectDownload', 'Download scRNA-Seq Seurat v3.0 Object'))
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("About", fluidRow(
p(strong("Computational Suite for Bioinformaticians and Biologists (CSBB)"), "is a RShiny application developed with an intention to empower researchers from wet and dry lab to perform downstream Bioinformatics analysis. CSBB powered by RShiny is packed with 8 modules", strong("Visualization, Normalization, Basic Stats, Differential Expression, Correlation Profiles, Function/Pathway Enrichment, ChIP/ATAC Seq and Single Cell RNA-Seq analysis."), " These modules are designed in order to help researchers design a hypothesis or answer research questions with little or no expertise in Bioinformatics. CSBB is also available as a command line application and has Next generation sequencing data processing capabilities. New modules and functionalities will be added periodically.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p("CSBB RShiny is avaibale on", a("GitHub", href = "https://github.com/praneet1988/CSBB-Shiny", target = "_blank"), ", if interested in hosting on your own servers.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Single Cell Transcriptomics Analysis Using CSBB-Shiny Tutorial Video"), a("YouTube", href = "https://youtu.be/s8Q4o1e-f1E", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p("Please post issues, suggestions and improvements using", a("Issues/suggestions", href = "https://github.com/praneet1988/CSBB-Shiny", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p("To use CSBB command line application please access", a("CSBB CMD", href = "https://github.com/praneet1988/Computational-Suite-For-Bioinformaticians-and-Biologists", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p("If using CSBB RShiny in your research please cite the GitHub page", a("Cite", href = "https://github.com/praneet1988/CSBB-Shiny", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p("Developed and maintained by Praneet Chaturvedi. To view other tools and contributions please visit", a("GitHub", href = "https://github.com/praneet1988/", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px")), imageOutput('Pipeline')),
tabPanel("Result Window", downloadButton('downloadResult', 'Download Results'), shinycssloaders::withSpinner(DT::dataTableOutput("result"), size = 3)),
tabPanel("Visualization Window", downloadButton('downloadPlot', 'Save Plot'), shinycssloaders::withSpinner(plotOutput("Plot"), size = 3)),
tabPanel("Getting Started", fluidRow(
p(strong("CSBB"), "is easy to use and is packed with some very powerful modules to help you analyze your data. Results generated from the modules are loaded on the Result window whereas the Visualization plots are displyed on Visualization windows. Now let's see what each module helps with and what are the options users can explore.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Tutorial Video"), a("YouTube", href = "https://youtu.be/c0P7TMu_IyY", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Single Cell Transcriptomics Analysis Using CSBB-Shiny Tutorial Video"), a("YouTube", href = "https://youtu.be/s8Q4o1e-f1E", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Normalization module"), "can help users perform normalization on their data using following methods: upper quantile, median, full, log2 and zScore. Normalized data can also visualized using Principal component analysis (pca), t-stochastic neighbor embedding (tSNE) and heatmap. For tSNE visualization a group file is required. Group file should provide group name for each sample in the data. PCA is linear dimension reduction technique and tSNE is non-linear dimension reduction technique.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Visualization module"), "lets user visualize their data using pca, tSNE and heatmap", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Basic Stats module"), "is very helpful for estimating mean, median, standard deviation, median adjusted deviation, sum, min and max expression per gene in the expression matrix.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Differential Expression module"), "helps user perform differential expression analysis on raw counts of genes across samples using RUVSeq. Please cite RUVSeq if using Differential Expression module in your research using", a("Cite", href = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4404308/", target = "_blank"), ". Differentially expressed (DE) genes are reported in result tab and results can be filtered using logFC and FDR filters. Users can visualize their data based on DE genes using pca, tSNE, heatmap, volcano plots and perform functional/pathway enrichemnt on DE genes", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Correlation Profiles module"), "is developed to help users analyze Correlation among the samples in the data or see how a gene set is correlated based on the expression across samples", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Functional/Pathway Enrichment module"), "is designed to compute and visualize top enriched functions and pathways based on user provided gene list. ReactomePA R package is used to perform and visualize enrichment. Please cite ReactomePA when using the module in your research", a("Cite", href = "https://pubs.rsc.org/en/content/articlelanding/2016/MB/C5MB00663E#!divAbstract", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("ChIP-ATAC Seq Analysis module"), "is designed to perform downstream analysis with peaks like obtaining coverage plot across chromosomes, quantifying profile of peaks binding to transcription start site (TSS) and performing peak annotation. TSS is defined as +- 3kb regions of flanking sequences of the TSS sites. ChIPseeker package in R is used for performing downsream analysis on peaks. Please cite ChIPseeker when using this module.", a("Cite", href = "https://academic.oup.com/bioinformatics/article/31/14/2382/255379", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Single Cell RNASeq Analysis module"), "is designed to perform single cell RNA-Seq analysis. CSBB-Shiny uses Seurat", a("Cite", href = "https://www.nature.com/articles/nbt.4096", target = "_blank"), "which has been one of most cited and widely used analysis toolkit for analyzing single cell RNA-Seq data. The purpose of providing a module for analyzing scRNA data is to enable users with feasible and powerful front-end to seurat. Please check Seurat Vignettes to undertand the steps behind analyzing the analysis framework. In CSBB-Shiny a default filtering on number of RNA features and percentage mitochondrial expression is used. The filter is set to 95th quantile for filtering cells. Please check out the video tutorial to undertand the specifics and parameters. Users can input raw counts matrix, H5 output from cellranger or a processed Seurat v3.0 object. Please cite Seurat when using this module for your research or grants. Please see that CSBB allows users to download R Objects after processing and the object name is set seurat.object.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p("Please access sample files for each module using", a("Sample Files", href = "https://github.com/praneet1988/CSBB-Shiny", target = "_blank"), style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"))),
tabPanel("What's New in CSBB Shiny", fluidRow(
p(strong("Version 1.5 Log:"), "Added new features to Single Cell Analysis Module like option to use it as a cell browser. Upload your own processed data using Seurat (v[>3.0]) in rds format to generate visualization plots, get markers, download plots etc. CSBB Shiny will be updated periodically. Stay Tuned.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Version 1.4 Log:"), "Added new features to Single Cell Analysis Module like option to regress cell-cycle effect, choose between LogNormalize or SCTransform, choose from multiple differential test for marker prediction, generate QC plot and lastly generate dimension plot with cell cycle phase. Users can input raw counts, H5 output from cellranger or a processed R object. CSBB-Shiny uses Seurat (a powerful scRNA-Seq analysis toolkit) to help users analyze scRNA-Seq datasets. CSBB Shiny will be updated periodically. Stay Tuned.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Version 1.3 Log:"), "Presenting Single cell RNA-Seq analysis module for analyzing scRNA-Seq datasets. Users can input raw counts, H5 output from cellranger or a processed R object. CSBB-Shiny uses Seurat (a powerful scRNA-Seq analysis toolkit) to help users analyze scRNA-Seq datasets. CSBB Shiny will be updated periodically. Stay Tuned.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Version 1.2 Log:"), "Dusted off some bugs in ChIP-ATAC Seq Analysis Module, thereby enhancing user experience. Bugs removed include: plots not being saved or not being refereshed. Removed the option to create tag density heatmap. Replaced pie chart with bar plot for visualizing peak region enrichment. CSBB Shiny will be updated periodically. Stay Tuned.", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Version 1.1 Log:"), "Brushed off some known bugs which entered the system and added new Module ChIP-ATAC Seq Analysis for users. CSBB Shiny will be updated periodically. Some known bugs kicked out include result files and plots file naming, threshold changes, text changes", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px"),
p(strong("Version 1.0 Log:"), "CSBB powered by Shiny has 6 modules and is built with an idea to empower researchers in performing bioinformatics analysis with little or no bioinformatics expertise", style="text-align:justify;color:black;background-color:white;padding:20px;border-radius:10px;font-size:15px")))
)
)
)
)
server <- function(input, output, session) {
output$Pipeline <- renderImage({
list(src = 'www/CSBB.png',
contentType = 'image/png',
width = 1200,
height = 1200,
alt = "This is alternate text")
}, deleteFile = F)
observe({
if(input$Module == "Differential Expression"){
inFile <- input$Counts
if (is.null(inFile))
return(NULL)
data <- as.matrix(read.table(inFile$datapath, sep="\t", header=T, row.names=1, check.names=TRUE))
samples <- colnames(data)
samples <- data.frame(samples)
samples <- samples$samples
updateSelectInput(session, "Controls",
label = "Select Control Samples",
choices = samples)
}
})
observe({
if(input$Module == "Differential Expression"){
inFile <- input$Counts
if (is.null(inFile))
return(NULL)
data <- as.matrix(read.table(inFile$datapath, sep="\t", header=T, row.names=1, check.names=TRUE))
samples <- colnames(data)
samples <- data.frame(samples)
samples <- samples$samples
updateSelectInput(session, "Treatments",
label = "Select Treatment Samples",
choices = samples)
}
})
observe({
if(input$Module == "Differential Expression"){
updateSelectInput(session, "PlotType",
label = "Visualization",
choices = c("pca", "tsne", "heatmap", "Functional and Pathway Enrichment", "Volcano Plots"),
selected = "heatmap")
}
})
scData <- reactive({
if(input$Module == "Single Cell RNASeq Analysis"){
if(input$scInput == "Raw Counts Matrix"){
inFile <- input$scCounts
if (is.null(inFile))
return(NULL)
data <- c()
data <- readSparseCounts(inFile$datapath, sep = "\t", row.names = TRUE, col.names = TRUE)
return(data)
}
else if(input$scInput == "H5"){
inFile <- input$scH5
if (is.null(inFile))
return(NULL)
data <- c()
data <- Read10X_h5(inFile$datapath, use.names = TRUE, unique.features = TRUE)
return(data)
}
else if(input$scInput == "Seurat Object"){
inFile <- input$scRobj
if (is.null(inFile))
return(NULL)
seurat.object = readRDS(inFile$datapath)
return(seurat.object)
}
}
})
observe({
if(input$Module == "Single Cell RNASeq Analysis"){
if(input$scInput == "Raw Counts Matrix"){
inFile <- input$scCounts
if (is.null(inFile))
return(NULL)
data <- c()
data <- scData()
genes <- rownames(data)
genes <- data.frame(genes)
genes <- genes$genes
updateSelectizeInput(session, "scGene",
label = "Select Genes",
choices = genes, server = TRUE)
}
else if(input$scInput == "H5"){
inFile <- input$scH5
if (is.null(inFile))
return(NULL)
data <- c()
data <- scData()
genes <- rownames(data)
genes <- data.frame(genes)
genes <- genes$genes
updateSelectizeInput(session, "scGene",
label = "Select Genes",
choices = genes, server = TRUE)
}
else if(input$scInput == "Seurat Object"){
inFile <- input$scRobj
if (is.null(inFile))
return(NULL)
seurat.object = scData()
DefaultAssay(seurat.object) = "RNA"
genes <- rownames(seurat.object)
genes <- data.frame(genes)
genes <- genes$genes
updateSelectizeInput(session, "scGene",
label = "Select Genes",
choices = genes, server = TRUE)
updateSelectInput(session, "ColorCells",
label = "Color Cells By",
choices = colnames(seurat.object@meta.data))
}
}
})
NormalizationData <- reactive({
if(input$Module == "Normalization"){
inFile <- input$File
if (is.null(inFile))
return(NULL)
if(input$Norm == "upper quantile"){
source("app_bin/UQ_Norm.r", local = TRUE)
return(UData)
}
else if(input$Norm == "median"){
source("app_bin/Median_Norm.r", local = TRUE)
return(UData)
}
else if(input$Norm == "full"){
source("app_bin/Full_Norm.r", local = TRUE)
return(UData)
}
else if(input$Norm == "none"){
source("app_bin/No_Norm.r", local = TRUE)
return(UData)
}
else if(input$Norm == "log2"){
source("app_bin/log2_Norm.r", local = TRUE)
return(UData)
}
else if(input$Norm == "zScore"){
source("app_bin/Zscore_Norm.r", local = TRUE)
return(UData)
}
}
})
BasicStatsData <- reactive({
if(input$Module == "Basic Stats"){
inFile <- input$File
if (is.null(inFile))
return(NULL)
source("app_bin/BasicStats.r", local = TRUE)
return(output)
}
})
VisualizationData <- reactive({
if(input$Module == "Visualization"){
inFile <- input$File
if (is.null(inFile))
return(NULL)
data <- read.table(inFile$datapath, sep="\t", header=T, row.names=1, check.names=F)
return(data)
}
})
DEData <- reactive({
if(input$Module == "Differential Expression"){
inFile <- input$Counts
if (is.null(inFile))
return(NULL)
data <- as.matrix(read.table(inFile$datapath, sep="\t", header=T, row.names=1, check.names=TRUE))
data <- data.frame(data)
samplelist <- c(input$Controls, input$Treatments)
data_temp <- data[,samplelist]
data_temp <- as.matrix(data_temp)
lengthcontrol <- length(input$Controls)
lengthtreatment <- length(input$Treatments)
if((lengthcontrol == 1)&(lengthtreatment == 1)){
source("app_bin/RUVseq_NoReps.r", local = TRUE, echo=FALSE)
DEresult_filter <- DEresult %>% rownames_to_column('gene') %>% filter(logFC >= input$DEFilterLog | logFC <= -1*(input$DEFilterLog), FDR <= input$DEFilterFDR) %>% column_to_rownames('gene')
if(is.null(DEresult_filter))
return(NULL)
return(DEresult_filter)
}
else if((lengthcontrol > 1)&(lengthtreatment >= 1)){
cutoff <- round((lengthcontrol + lengthtreatment)/2)
source("app_bin/RUVseq_replicates.r", local = TRUE, echo=FALSE)
DEresult_filter <- DEresult %>% rownames_to_column('gene') %>% filter(logFC >= input$DEFilterLog | logFC <= -1*(input$DEFilterLog), FDR <= input$DEFilterFDR) %>% column_to_rownames('gene')
if(is.null(DEresult_filter))
return(NULL)
return(DEresult_filter)
}
else if((lengthcontrol >= 1)&(lengthtreatment > 1)){
cutoff <- round((lengthcontrol + lengthtreatment)/2)
source("app_bin/RUVseq_replicates.r", local = TRUE, echo=FALSE)
DEresult_filter <- DEresult %>% rownames_to_column('gene') %>% filter(logFC >= input$DEFilterLog | logFC <= -1*(input$DEFilterLog), FDR <= input$DEFilterFDR) %>% column_to_rownames('gene')
if(is.null(DEresult_filter))
return(NULL)
return(DEresult_filter)
}
else if((lengthcontrol == 0)&(lengthtreatment == 0)){
return(NULL)
}
}
})
CorrelationData <- reactive({
if(input$Module == "Correlation Profiles"){
if(input$Correlation == "Samples"){
inFile <- input$File
if (is.null(inFile))
return(NULL)
data <- as.matrix(read.table(inFile$datapath, sep="\t", header=T, row.names=1, check.names=F))
cormat <- cor(data, method=input$CorrelationMethod, use = "na.or.complete")
return(cormat)
}
else if(input$Correlation == "Genes"){
inFile <- input$File
if (is.null(inFile))
return(NULL)
data <- as.matrix(read.table(inFile$datapath, sep="\t", header=T, row.names=1, check.names=F))
Genes_List <- input$GeneList
if (is.null(Genes_List))
return(NULL)
GenesUpload <- as.matrix(read.table(Genes_List$datapath, sep="\n", header=F))
GenesUpload <- data.frame(GenesUpload)
GenesUpload <- unique(GenesUpload$V1)
datause <- subset(data, rownames(data) %in% GenesUpload)
datause <- t(datause)
cormat <- cor(datause, method=input$CorrelationMethod, use = "na.or.complete")
return(cormat)
}
}
})
FPEnrichmentData <- reactive({
if(input$Module == "Functional and Pathway Enrichment"){
if(input$Species == "human"){
inFile <- input$GeneList_FP
if (is.null(inFile))
return(NULL)
GenesUpload <- as.matrix(read.table(inFile$datapath, sep="\n", header=F))
GenesUpload <- data.frame(GenesUpload)
GenesUpload <- unique(GenesUpload$V1)
#genes_ids <- mapIds(org.Hs.eg.db, as.character(GenesUpload), 'ENTREZID', 'SYMBOL')
enrichemnt <- enrichr(GenesUpload, dbs)
return(enrichemnt)
}
else if(input$Species == "mouse"){
inFile <- input$GeneList_FP
if (is.null(inFile))
return(NULL)
GenesUpload <- as.matrix(read.table(inFile$datapath, sep="\n", header=F))
GenesUpload <- data.frame(GenesUpload)
GenesUpload <- unique(GenesUpload$V1)
#genes_ids <- mapIds(org.Mm.eg.db, as.character(GenesUpload), 'ENTREZID', 'SYMBOL')
enrichemnt <- enrichr(GenesUpload, dbs)
return(enrichemnt)
}
}
})
CHIPData <- reactive({
if(input$Module == "ChIP-ATAC Seq Analysis"){
if(input$SpeciesChIP == "human (hg19)"){
if((input$PlotChIP == "Coverage Plot (Visualize coverage of peaks across chromosomes)")|(input$PlotChIP == "Average Profile of peaks binding to TSS regions")){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
return(peak)
}
else if(input$PlotChIP == "Peak Annotation (finding closest genes, postion of Peaks wrt TSS etc.)"){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene
if(input$PlotAnnotation == "Peak genomic annotation"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Hs.eg.db")
return(peakAnno)
}
else if(input$PlotAnnotation == "Functional enrichemnt"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Hs.eg.db")
pathway <- enrichPathway(as.data.frame(peakAnno)$geneId, pvalueCutoff = 0.05, readable=T, organism = "human")
return(pathway)
}
}
}
else if(input$SpeciesChIP == "human (hg38)"){
if((input$PlotChIP == "Coverage Plot (Visualize coverage of peaks across chromosomes)")|(input$PlotChIP == "Average Profile of peaks binding to TSS regions")){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
return(peak)
}
else if(input$PlotChIP == "Peak Annotation (finding closest genes, postion of Peaks wrt TSS etc.)"){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
txdb = TxDb.Hsapiens.UCSC.hg38.knownGene
if(input$PlotAnnotation == "Peak genomic annotation"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Hs.eg.db")
return(peakAnno)
}
else if(input$PlotAnnotation == "Functional enrichemnt"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Hs.eg.db")
pathway <- enrichPathway(as.data.frame(peakAnno)$geneId, pvalueCutoff = 0.05, readable=T, organism = "human")
return(pathway)
}
}
}
else if(input$SpeciesChIP == "mouse (mm10)"){
if((input$PlotChIP == "Coverage Plot (Visualize coverage of peaks across chromosomes)")|(input$PlotChIP == "Average Profile of peaks binding to TSS regions")){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
return(peak)
}
else if(input$PlotChIP == "Peak Annotation (finding closest genes, postion of Peaks wrt TSS etc.)"){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
txdb = TxDb.Mmusculus.UCSC.mm10.knownGene
if(input$PlotAnnotation == "Peak genomic annotation"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Mm.eg.db")
return(peakAnno)
}
else if(input$PlotAnnotation == "Functional enrichemnt"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Mm.eg.db")
pathway <- enrichPathway(as.data.frame(peakAnno)$geneId, pvalueCutoff = 0.05, readable=T, organism = "mouse")
return(pathway)
}
}
}
else if(input$SpeciesChIP == "mouse (mm9)"){
if((input$PlotChIP == "Coverage Plot (Visualize coverage of peaks across chromosomes)")|(input$PlotChIP == "Average Profile of peaks binding to TSS regions")){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
return(peak)
}
else if(input$PlotChIP == "Peak Annotation (finding closest genes, postion of Peaks wrt TSS etc.)"){
inFile <- input$PeakFile
if (is.null(inFile))
return(NULL)
peak <- readPeakFile(inFile$datapath)
txdb = TxDb.Mmusculus.UCSC.mm9.knownGene
if(input$PlotAnnotation == "Peak genomic annotation"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Mm.eg.db")
return(peakAnno)
}
else if(input$PlotAnnotation == "Functional enrichemnt"){
peakAnno <- annotatePeak(peak, tssRegion=c(-3000, 3000), TxDb=txdb, annoDb="org.Mm.eg.db")
pathway <- enrichPathway(as.data.frame(peakAnno)$geneId, pvalueCutoff = 0.05, readable=T, organism = "mouse")
return(pathway)
}
}
}
}
})
SingleCell <- reactive({
if(input$Module == "Single Cell RNASeq Analysis"){
if((input$scInput == "Raw Counts Matrix")||(input$scInput == "H5")){
data <- c()
data <- scData()
if(is.null(data))
return(NULL)
seurat.object <- CreateSeuratObject(counts = data, project = "scAnalysis", min.cells = input$scMinCells, min.features = input$scMinFeatures)
if(input$Species_singlecell == "human"){
seurat.object[["percent.mt"]] <- PercentageFeatureSet(seurat.object, pattern = "^MT-")
cc.genes <- readLines(con = "data/CellCycle_Human.txt")
}
else if(input$Species_singlecell == "mouse"){
seurat.object[["percent.mt"]] <- PercentageFeatureSet(seurat.object, pattern = "^mt-")
cc.genes <- readLines(con = "data/CellCycle_Mouse.txt")
}
nFeature_RNA_cutoff <- quantile(seurat.object@meta.data$nFeature_RNA, .95)
nFeature_RNA_cutoff <- data.frame(nFeature_RNA_cutoff)
percentMT_cutoff <- quantile(seurat.object@meta.data$percent.mt, .95)
percentMT_cutoff <- data.frame(percentMT_cutoff)
if(percentMT_cutoff$percentMT_cutoff == 0){
seurat.object <- subset(seurat.object, subset = nFeature_RNA > 0 & nFeature_RNA < nFeature_RNA_cutoff$nFeature_RNA_cutoff & percent.mt < 1)
}
else if(percentMT_cutoff$percentMT_cutoff > 0){
seurat.object <- subset(seurat.object, subset = nFeature_RNA > 0 & nFeature_RNA < nFeature_RNA_cutoff$nFeature_RNA_cutoff & percent.mt < percentMT_cutoff$percentMT_cutoff)
}
if(input$scNormalization == 'LogNormalize'){
seurat.object <- NormalizeData(seurat.object)
seurat.object <- FindVariableFeatures(seurat.object, selection.method = "vst", nfeatures = input$VarFeatures)
if(input$scCellCycle == "Yes"){
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]
seurat.object <- CellCycleScoring(object = seurat.object, s.features = s.genes, g2m.features = g2m.genes, set.ident = FALSE)
seurat.object@meta.data$CC.Difference <- seurat.object@meta.data$S.Score - seurat.object@meta.data$G2M.Score
seurat.object <- ScaleData(seurat.object, vars.to.regress = c("percent.mt", "CC.Difference"))
}
else if(input$scCellCycle == "No"){
seurat.object <- ScaleData(seurat.object, vars.to.regress = "percent.mt")
}
seurat.object <- RunPCA(seurat.object)
seurat.object <- FindNeighbors(seurat.object, dims = 1:input$scDims)
seurat.object <- FindClusters(seurat.object, resolution = input$scRes)
if(input$scReduction == "umap"){
seurat.object <- RunUMAP(seurat.object, dims = 1:input$scDims)
}
else if(input$scReduction == "tsne"){
seurat.object <- RunTSNE(seurat.object, dims = 1:input$scDims)
}
return(seurat.object)
}
else if(input$scNormalization == 'SCTransform'){
if(input$scCellCycle == "Yes"){
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]
seurat.object <- SCTransform(seurat.object, variable.features.n = input$VarFeatures)
seurat.object <- CellCycleScoring(object = seurat.object, s.features = s.genes, g2m.features = g2m.genes, set.ident = FALSE)
seurat.object@meta.data$CC.Difference <- seurat.object@meta.data$S.Score - seurat.object@meta.data$G2M.Score
seurat.object <- SCTransform(seurat.object, vars.to.regress = c("percent.mt", "CC.Difference"), variable.features.n = input$VarFeatures)
}
else if(input$scCellCycle == "No"){
seurat.object <- SCTransform(seurat.object, vars.to.regress = "percent.mt", variable.features.n = input$VarFeatures)
}
seurat.object <- RunPCA(seurat.object)
seurat.object <- FindNeighbors(seurat.object, dims = 1:input$scDims)
seurat.object <- FindClusters(seurat.object, resolution = input$scRes)
if(input$scReduction == "umap"){
seurat.object <- RunUMAP(seurat.object, dims = 1:input$scDims)
}
else if(input$scReduction == "tsne"){
seurat.object <- RunTSNE(seurat.object, dims = 1:input$scDims)
}
return(seurat.object)
}
}
else if(input$scInput == "Seurat Object"){
inFile <- input$scRobj
if(is.null(inFile))
return(NULL)
seurat.object = scData()
DefaultAssay(seurat.object) = "RNA"
Idents(seurat.object) <- seurat.object@meta.data$seurat_clusters
return(seurat.object)
}
}
})
observe({
if(input$Module == "Single Cell RNASeq Analysis"){
if(input$scInput == "Raw Counts Matrix"){
seurat.object = SingleCell()
if(is.null(seurat.object))
return(NULL)
updateSelectInput(session, "ColorCells",
label = "Color Cells By",
choices = colnames(seurat.object@meta.data))
}
else if(input$scInput == "H5"){
seurat.object = SingleCell()
if(is.null(seurat.object))
return(NULL)
updateSelectInput(session, "ColorCells",
label = "Color Cells By",
choices = colnames(seurat.object@meta.data))
}
}
})
SingleCellMarkers <- reactive({
seurat.object <- SingleCell()
if(is.null(seurat.object))
return(NULL)
if((input$scInput == "Seurat Object") && (input$MarkerCalculate == 'TRUE')) {
markers_clusters <- FindAllMarkers(seurat.object, max.cells.per.ident = 100, min.pct = 0.25)
return(markers_clusters)
}
if((input$scInput == "Seurat Object") && (input$MarkerCalculate == 'FALSE')) {
markers_clusters = NULL
return(markers_clusters)
}
if((input$scInput == "Raw Counts Matrix")||(input$scInput == "H5")) {
markers_clusters <- FindAllMarkers(seurat.object, test.use = input$scDETest, min.pct = 0.25)
return(markers_clusters)
}
})
PCAplot <- reactive({
inFile <- input$File
if (is.null(inFile))
return(NULL)
data <- c()
if(input$Module == "Normalization"){
data <- as.matrix(NormalizationData())
}
else if(input$Module == "Visualization"){
data <- as.matrix(VisualizationData())
}
data <- data[apply(data[,-1], 1, function(x) !all(x==0)),]
data.t <- t(data)
pca <- prcomp(data.t, center=T, scale. = T)
pc1 <- round(pca$sdev[1]^2/sum(pca$sdev^2)*100,2)
pc2 <- round(pca$sdev[2]^2/sum(pca$sdev^2)*100,2)
PC1_use <- paste0("PC1", "(", pc1, "%)")
PC2_use <- paste0("PC2", "(", pc2, "%)")
Samples_temp <- rownames(data.t)
Samples <- factor(Samples_temp)
scores <- data.frame(Samples_temp, pca$x[,1:3])
MIN_X <- min(scores$PC1)
Max_X <- max(scores$PC1)
header <- "Principal Component Analysis"
qplot(x=PC1, y=PC2, data=scores, colour=Samples, xlim=c(MIN_X-75,Max_X+75)) + xlab(PC1_use) + ylab(PC2_use) + geom_point(shape=1) + geom_text(aes(label=Samples_temp), hjust=0, vjust=0) + scale_size_area() + theme(axis.text = element_text(size = 14),axis.line.x = element_line(colour = "black"),axis.line.y = element_line(colour = "black"),legend.key = element_rect(fill = "white"),legend.background = element_rect(fill = "white"),panel.grid.major = element_line(),panel.grid.minor = element_blank(),panel.background = element_rect(fill = "white")) + ggtitle(header) + guides(colour = guide_legend(ncol = 1, override.aes = list(size = 5)))
})
TSNEplot <- reactive({
inFile <- input$File
if (is.null(inFile))
return(NULL)
inGroupFile <- input$GroupFile
if (is.null(inGroupFile))
return(NULL)
data <- c()
if(input$Module == "Normalization"){
data <- as.matrix(NormalizationData())
}
else if(input$Module == "Visualization"){
data <- as.matrix(VisualizationData())
}
Group <- as.matrix(read.table(inGroupFile$datapath, header=T, sep="\t", row.names=1, check.names=F))
Group <- data.frame(Group)
GroupUse <- as.factor(Group$group)
tsne(data, labels=GroupUse, perplex=input$perplexity) + xlab("tSNE_1") + ylab("tSNE_2") + guides(colour = guide_legend(ncol = 1, override.aes = list(size = 5)))
})
HEATMAPplot <- reactive({
inFile <- input$File
if (is.null(inFile))
return(NULL)
data <- c()
if(input$Module == "Normalization"){
data <- as.matrix(NormalizationData())
}
else if(input$Module == "Visualization"){
data <- as.matrix(VisualizationData())
}
data <- data[apply(data, MARGIN = 1, FUN = function(x) sd(x) != 0),]
if(input$Cluster == "Rows"){
pheatmap(data, scale=input$Scaling, cluster_rows=TRUE, cluster_cols=FALSE, main="Heatmap", border_color = "NA")
}
else if(input$Cluster == "Columns"){
pheatmap(data, scale=input$Scaling, cluster_rows=FALSE, cluster_cols=TRUE, main="Heatmap", border_color = "NA")
}
else if(input$Cluster == "Rows and Columns"){
pheatmap(data, scale=input$Scaling, cluster_rows=TRUE, cluster_cols=TRUE, main="Heatmap", border_color = "NA")
}
else if(input$Cluster == "None"){
pheatmap(data, scale=input$Scaling, cluster_rows=FALSE, cluster_cols=FALSE, main="Heatmap", border_color = "NA")
}
})
CorrelationPlot <- reactive({
if(input$Correlation == "Samples"){
inFile <- input$File
if (is.null(inFile))
return(NULL)
titleuse <- paste0("Displaying Correlation Plot of ", input$Correlation)
data <- CorrelationData()
ggcorrplot(data, hc.order = TRUE, outline.color = "white", ggtheme = ggplot2::theme_gray, colors = c("#6D9EC1", "white", "#E46726"), legend.title = "Correlation") + labs(title = titleuse)
}
else if(input$Correlation == "Genes"){
inFile <- input$File
if (is.null(inFile))
return(NULL)
Genes_List <- input$GeneList
if (is.null(Genes_List))
return(NULL)
titleuse <- paste0("Displaying Correlation Plot of ", input$Correlation)
data <- CorrelationData()
ggcorrplot(data, hc.order = TRUE, outline.color = "white", ggtheme = ggplot2::theme_gray, colors = c("#6D9EC1", "white", "#E46726"), legend.title = "Correlation") + labs(title = titleuse)