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pilot_app.R
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#load libraries
library(shinythemes)
library(thematic)
library(shiny)
library(magrittr)
library(ggplot2)
library(tidyr)
library(viridis)
library(cowplot)
library(TidyMultiqc)
library(dplyr)
library(tidyverse)
library(data.table)
library(ggpubr)
library(RColorBrewer)
library(janitor)
library(reactlog)
library(DT)
library(ggrepel)
library(DESeq2)
library(shinyWidgets)
library(shinyjs)
library(fgsea)
library(WGCNA)
library(plotly)
library(BiocParallel)
library(ComplexHeatmap)
library(InteractiveComplexHeatmap)
library(circlize)
library(colourpicker)
options(shiny.reactlog = TRUE)
#reactlogShow(time = TRUE)
#Data ####
meta_lut_ven <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/meta_lut_ven.Rds")
qcdt<-load_multiqc("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/multiqc_data.json", sections="raw")
vst.goi <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/vst.goi.rds")
#DESeq data table
dds.res <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/DEtable.rds")
#sample metadata table
metadata <- read.table(file = "/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/SampleSheetJordanLab.txt")
# DE table with singscore
dds.resscore <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/dds.resscore.rds")
#tables for PCA
vsd.pca <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/vsd.pca.rds")
bcvsd.pca <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/bcvsd.pca.rds")
#tables for variance
vsd.variance <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/vsd.variance.rds")
bcvsd.variance <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/bcvsd.variance.rds")
vsd2.pca <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/vsd2.pca.rds")
bcvsd2.pca <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/bcvsd2.pca.rds")
#GSEA data table
ranks <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/ranks.rds")
#load pathways
pathways.hallmark <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/h.all.v7.4.symbols.gmt")
pathways.GOall <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c5.go.v2022.1.Hs.symbols.gmt")
pathways.GOmolec <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c5.go.mf.v7.4.symbols.gmt")
pathways.GOcellcomp <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c5.go.cc.v2022.1.Hs.symbols.gmt")
pathways.GObio <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c5.go.bp.v7.4.symbols.gmt")
pathways.TFtargets <-gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c3.tft.v2022.1.Hs.symbols.gmt")
pathways.allReg <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c3.all.v2022.1.Hs.symbols.gmt")
pathways.Wiki <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c2.cp.wikipathways.v2022.1.Hs.symbols.gmt")
pathways.Reactome <-gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c2.cp.reactome.v2022.1.Hs.symbols.gmt")
pathways.KEGG <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c2.cp.kegg.v2022.1.Hs.symbols.gmt")
pathways.Positional <-gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c1.all.v2022.1.Hs.symbols.gmt")
pathways.Biocarta <-gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/c2.cp.biocarta.v2022.1.Hs.symbols.gmt")
pathways.lsc <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/lsc_sigs.gmt")
pathways.aeg <- gmtPathways("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/gmt_pathway_files copy/aeg_genesets_20220602.gmt")
vstlimma <- readRDS("/Users/stephanie/Documents/GitHub/EAGLe-2.0/data/vstlimma.rds")
names(pathways.aeg)[10] <- "PM_Primitive_Blast"
names(pathways.aeg)[9] <- "PM_Monocytic_Blast"
pathways.aegGOBP <- c(pathways.aeg, pathways.GObio)
# UI ####
ui <-
navbarPage(
"EAGLe: Cancer Discovery",
tabPanel( #QC Menu ####
"QC",
fluidPage(
theme =
shinytheme(
"flatly"
),
titlePanel(
"QC Analysis Plots"
),
sidebarLayout(
sidebarPanel(
materialSwitch(
inputId =
"PCAplots",
label =
"PCA",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"PCAscreeplots",
label =
"Scree",
value =
FALSE,
right =
TRUE
),
condition = "input.PCAplots == true",
radioButtons(
"PCAvar",
h4(
"Choose PCA plot"
),
choices =
list("VST PCA", "VST + batch corrected PCA"),
selected =
"VST PCA"
),
hr(),
colourInput(
"PCAcolor1",
label = "Choose 1st color",
value = "#009292",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
hr(),
colourInput(
"PCAcolor2",
label = "Choose 2nd color",
value = "#FFFF6D",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
hr(),
downloadButton("downloadPlotPCA", label = "Download Plot"),
conditionalPanel(
condition = "input.PCAscreeplots == true",
downloadButton("downloadPlotscree",
label =
"Download Plot")
) #end conditional
),
mainPanel(
conditionalPanel(condition = "input.PCAplots == true",
plotOutput("PCAplot")),
conditionalPanel(condition = "input.PCAscreeplots == true",
plotOutput("PCAvarplot"))
)
)
)
),
# tabPanel( #MultiQC Plots ####
# "MultiQC",
# fluidPage(
# theme =
# shinytheme("flatly"),
# titlePanel(
# "QC Analysis: MultiQC Plots"
# ),
# sidebarLayout(
# sidebarPanel(
# selectInput(
# "QCvar",
# label=
# "Choose MultiQC test",
# choices =
# c("% mapped reads", "# mapped reads", "% uniquely mapped reads", "# uniquely mapped reads"),
# selected =
# "% mapped reads"
# ) #end selectInput
# ), #end sidebar panel
# mainPanel(
# plotOutput(
# "QCplot"
# )
# )
# )
# )
# )
# ),
tabPanel( #Gene centric analysis ####
"Gene Expression",
fluidPage(
theme=
shinytheme("flatly"),
titlePanel(
"Gene Expression Plot"
),
sidebarLayout(
sidebarPanel(
useShinyjs(),
selectizeInput(
"VSTCDgenechoice",
label=
"Choose a gene for analysis",
choices =
NULL,
selected = NULL,
options = list(maxItems = NULL)
),
radioButtons("XaxisVar_CDgene", h4("X axis variable"),
choices = list("Value" = "xvalue",
"Gene" = "xgene"), selected = "xgene"),
radioButtons("YaxisVar_CDgene", h4("Y axis variable"),
choices = list("Value" = "yvalue",
"Gene" = "ygene"), selected = "yvalue"),
# radioButtons("FillVar_CDgene", h4("Fill variable"),
# choices = list("Class" = "fillclass", "Gene" = "fillgene"), selected = "fillclass"),
hr(),
h3(
"Aesthetics:"
),
hr(),
materialSwitch(
inputId =
"genefacetbutton",
label =
"Facet Grid",
value =
FALSE,
right =
TRUE
),
hr(),
#Palettes from colorBrewer
# selectInput("PaletteChoices", "Choose a color palette", choices =
# c("Dark2", "Paired", "Set1"), selected = "Dark2"),
colourInput(
"genecolor1",
label = "Choose 1st color",
value = "#009292",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
colourInput(
"genecolor2",
label = "Choose 2nd color",
value = "#ffff6d",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
colourInput(
"genecolor3",
label = "Choose 3rd color",
value = "#490092",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
hr(),
sliderInput("geneheightslider", "Adjust plot height",
min = 200, max = 1200, value = 600
),
hr(),
sliderInput("genewidthslider", "Adjust plot width",
min = 200, max = 1200, value = 800
),
hr(),
downloadButton("downloadGenePlot", label = "Download Plot"),
),
mainPanel(
plotOutput(
"VSTCDplot"
)
)
)
)
),
tabPanel("DESeq Analysis",# DESeq Menu ####
fluidPage(
theme =
shinytheme("flatly"),
titlePanel(
"DESeq Table and Plots"
),#end title
sidebarLayout(
sidebarPanel(
materialSwitch(
inputId =
"DESeqtable",
label =
"DE Table",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"singscorebutton",
label =
"DE Table w/ Monocytic Contribution",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"DESeqvolcano",
label =
"Volcano Plot",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"DESeqMA",
label =
"MA Plot",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"DESeqHeat",
label =
"Heatmap",
value =
FALSE,
right =
TRUE
),
hr(),
radioButtons("padjbutton", label = "Filter DE tables by padj",
choices = list("<= 0.01" = "sigvar1", "<= 0.05" = "sigvar5", "All" = "allvar"), selected = "allvar"),
hr(),
h4("Aesthetics"),
colourInput(
"volcanocolor1",
label = "Choose 1st color",
value = "#009292",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
colourInput(
"volcanocolor2",
label = "Choose 2nd color",
value = "grey",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
colourInput(
"volcanocolor3",
label = "Choose 3rd color",
value = "#490092",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
hr(),
h4("Table and Plot Downloads"),
downloadButton("downloadDEtable", label = "Download DE Table"),
hr(),
downloadButton(
"downloadDEVolcano",
label =
"Download Volcano Plot"
),
hr(),
downloadButton(
"downloadDEMA",
label =
"Download MA Plot"
)
),
mainPanel(
conditionalPanel(
condition = "input.DESeqtable == true",
DTOutput(
"DETable"
)
),
conditionalPanel(
condition = "input.DESeqvolcano == true",
plotlyOutput(
"DEVolcanoPlot"
)
),
conditionalPanel(
condition = "input.DESeqMA == true",
plotlyOutput(
"DEMAPlot"
)
),
conditionalPanel(
condition = "input.DESeqHeat == true",
InteractiveComplexHeatmapOutput(heatmap_id =
"ht"
)
)
)
)
)
),
#GSEA menu ####
tabPanel("GSEA", ####GSEAtables
fluidPage(
theme =
shinytheme("flatly"),
titlePanel(
"GSEA"
),#end title
sidebarLayout(
sidebarPanel(
materialSwitch(
inputId =
"fgseaTable",
label =
"Table",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"rankedplot",
label =
"Waterfall",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"moustache",
label =
"Moustache",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"eplot",
label =
"Enrichment",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"volcanoplot",
label =
"Volcano",
value =
FALSE,
right =
TRUE
),
materialSwitch(
inputId =
"heatmap",
label =
"Heatmap",
value =
FALSE,
right =
TRUE
),
selectInput("filechoice", label = "Choose gmt file to load pathway sets",
choices = c(Hallmark = "hallmark", GOall = "goall",GOmolecular = "GOmolec",
GOcellcomp = "GOcellcomp", GObio = "GObio", TFtargets = "TFtargets",
allRegular = "allReg", Wiki = "wiki", Reactome = "reactome", KEGG = "KEGG",
Positional = "positional", Biocarta = "biocarta", lsc = "lsc", aeg = "aeg")),
hr(),
selectizeInput(
"pathwaylist",
label=
"Choose a pathway",
choices =
NULL,
selected = NULL,
options = list(maxItems = NULL)
),
conditionalPanel(
condition = "input.fgseaTable == true",
downloadButton(
"downloadfgsea",
label =
"Download GSEA Table"
)
),
conditionalPanel(
condition = "input.rankedplot == true",
downloadButton(
"downloadranks",
label =
"Download Waterfall Plot"
),
h4("Waterfall Plot Specific Options"),
sliderInput("howmanypathways", "Choose How Many Pathways to Rank",
min = 5, max = 60, value = 15
),
sliderInput("rankedheightslider", "Adjust plot height",
min = 200, max = 1000, value = 400
),
hr(),
sliderInput("rankedwidthslider", "Adjust plot width",
min = 200, max = 1000, value = 600
),
colourInput(
"waterfallcolor1",
label = "Choose 1st color",
value = "#009292",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
colourInput(
"waterfallcolor2",
label = "Choose 2nd color",
value = "#490092",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
downloadButton(
"downloadranks",
label =
"Download Waterfall Plot")
),
conditionalPanel(
condition = "input.moustache == true",
h4("Moustache Plot Specific Options"),
colourInput(
"choice1color",
label = "Choose 1st color",
value = "#009292",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
),
colourInput(
"choice2color",
label = "Choose 2nd color",
value = "#000000",
showColour = ("both"),
palette = ("square"),
allowedCols = NULL,
allowTransparent = FALSE,
returnName = FALSE,
closeOnClick = FALSE
)
# hr(),
# sliderInput("mheightslider", "Adjust plot height",
# min = 200, max = 1000, value = 400
# ),
# hr(),
#
# sliderInput("mwidthslider", "Adjust plot width",
# min = 200, max = 1000, value = 600
# ),
#
# downloadButton(
# "downloadmoustache",
# label =
# "Download Plot"
# )
),
conditionalPanel(
condition = "input.eplot == true",
h4("Enrichment Plot Specific Options"),
radioButtons("topupordownbutton", h4("Top Ranked Up or Down Pathway"),
choices = list("Top Ranked Up Pathway" = "topup", "Top Ranked Down Pathway" = "topdown"), selected = "topup"),
downloadButton(
"downloadeplot",
label =
"Download Enrichment Plot"
)
),
conditionalPanel(
condition = "input.volcanoplot == true",
h4("Volcano Plot Specific Options"),
# colourInput(
# "gseavolcolor1",
# label = "Choose 1st color",
# value = "#009292",
# showColour = ("both"),
# palette = ("square"),
# allowedCols = NULL,
# allowTransparent = FALSE,
# returnName = FALSE,
# closeOnClick = FALSE
# ),
# colourInput(
# "gseavolcolor2",
# label = "Choose 2nd color",
# value = "#490092",
# showColour = ("both"),
# palette = ("square"),
# allowedCols = NULL,
# allowTransparent = FALSE,
# returnName = FALSE,
# closeOnClick = FALSE
# ),
# colourInput(
# "gseavolcolor3",
# label = "Choose 3rd color",
# value = "grey",
# showColour = ("both"),
# palette = ("square"),
# allowedCols = NULL,
# allowTransparent = FALSE,
# returnName = FALSE,
# closeOnClick = FALSE
# ),
# hr(),
downloadButton(
"downloadvolcano",
label =
"Download Volcano Plot"
)
),
conditionalPanel(
condition = "input.heatmap == true",
h4("Heatmap Specific Options"),
# downloadButton(
# "downloadheatmap",
# label =
# "Download Plot"
# )
),
),
mainPanel(
conditionalPanel(
condition = "input.fgseaTable == true",
DTOutput(
"fgseaTable"
)
),
conditionalPanel(
condition = "input.rankedplot == true",
plotOutput(
"GSEAranked"
)
),
conditionalPanel(
condition = "input.moustache == true",
plotOutput(
"GSEAMoustache"
)
),
conditionalPanel(
condition = "input.eplot == true",
plotOutput(
"GSEAenrichment"
)
),
conditionalPanel(
condition = "input.volcanoplot == true",
plotOutput(
"GSEAvolcano"
)
),
conditionalPanel(
condition = "input.heatmap == true",
InteractiveComplexHeatmapOutput(heatmap_id = "htgsea")
)
)
)
)
),
tabPanel("GSEA Pathway/Gene Visualization",
fluidPage(
theme =
shinytheme("flatly"),
titlePanel(
"GSEA:Pathway/Gene Visualization"
),#end title
h4("Positive NES is upregulated in Primitive cells and negative NES is upregulated in Monocytic cells"),
sidebarLayout(
sidebarPanel(
selectizeInput(
"Pathwaygenechoice",
label=
"Choose a gene of interest",
choices =
NULL,
selected = NULL,
options = list(maxItems = 1)
),
hr(),
selectInput("genefilechoice", "Choose gmt file to load pathways containing the gene of interest",
choices = c(Hallmark = "hallmark", GOall = "GOall", GOmolecular = "GOmolec",
GOcellcomp = "GOcellcomp", GObio = "GObio", TFtargets = "TFtargets",
allRegular = "allReg", Wiki = "wiki", Reactome = "reactome", KEGG = "KEGG",
Positional = "positional", Biocarta = "biocarta", lsc = "lsc", aeg = "aeg")),
hr(),
sliderInput("goiheightslider", "Adjust plot height",
min = 200, max = 1000, value = 400
),
hr(),
sliderInput("goiwidthslider", "Adjust plot width",
min = 200, max = 1000, value = 600
)
),
mainPanel(
plotOutput(
"PathwaysGenePlot"
)
)
)
)
),
#WGCNA menu####
tabPanel(
"WGCNA"
)
)
#Server ####
server <-
function(input, output, session) {
print("Initializing renderPlots")
options(shiny.reactlog = TRUE)
##QC-MultiQC plots####
# output$QCplot <- renderPlot({
# QCdata <- switch(
# input$QCvar,
# "% mapped reads" = qcdt$raw.salmon.percent_mapped,
# "# mapped reads" = qcdt$raw.salmon.num_mapped,
# "% uniquely mapped reads" = qcdt$raw.star.uniquely_mapped_percent,
# "# uniquely mapped reads" = qcdt$raw.star.uniquely_mapped,
# "Sample_ID" = qcdt$metadata.sample_id
# )
#
# Sample_ID <-qcdt$metadata.sample_id
#
# ggplot(
# qcdt,
# aes(
# x = Sample_ID,
# y = QCdata
# )) +
# geom_point(alpha = 0.5) +
# theme_cowplot (12) +
# theme(axis.text.x =
# element_text(angle = 60, hjust = 1))
# }) #end renderPlot
# PCA plots ####
PCAdata <-
eventReactive(input$PCAvar, {
if (input$PCAvar == "VST PCA") {
vsd.pca
} else if (input$PCAvar == "VST + batch corrected PCA") {
bcvsd.pca
}
})
# PCA Scree data ####
# VSD PCA variance
PC_var_VST <- data.frame(PC =paste0("PC", 1:12),variance =(((vsd2.pca$sdev) ^ 2 / sum((vsd2.pca$sdev) ^ 2)) * 100))
lorder_VST <- as.vector(outer(c("PC"), 1:12, paste, sep = ""))
PC_var_VST$PC <-factor(PC_var_VST$PC,levels = lorder_VST)
#batch corrected PCA variance
PC_var_bc <-data.frame(PC =paste0("PC", 1:12),variance =(((bcvsd2.pca$sdev) ^ 2 / sum((bcvsd2.pca$sdev) ^ 2)) * 100))
lorder_bc <-as.vector(outer(c("PC"), 1:12, paste, sep = ""))
PC_var_bc$PC <-factor(PC_var_bc$PC,levels = lorder_bc)
PC_var_data <-
eventReactive(input$PCAvar, {
if (input$PCAvar == "VST PCA") {
PC_var_VST
} else if (input$PCAvar == "VST + batch corrected PCA") {
PC_var_bc
}
})
# reactive function for plot title
PCA_title <-
reactive({
if (input$PCAvar == "VST PCA") {
print("VST PCA")
} else if (input$PCAvar == "VST + batch corrected PCA") {
print("VST + batch corrected PCA")
}
})
#define objects for defining x label to include % variance of PC1
pc1varvsd <- paste("PC1", (round(vsd.variance[3, 1] * 100, 1)), "% variance")
pc1varbcvsd <- paste("PC1", (round(bcvsd.variance[3, 1] * 100, 1)), "% variance")
# add reactive expression for x label for PC1
variance_PC1 <-
eventReactive(input$PCAvar, {
if (input$PCAvar == "VST PCA") {
pc1varvsd
} else if (input$PCAvar == "VST + batch corrected PCA") {
pc1varbcvsd
}
})
#new objects with calculation only to use in calculation of PC2 % variance
pc12 <- round(vsd.variance[3, 1] * 100, 1)
pc13 <- round(bcvsd.variance[3, 1] * 100, 1)
#create objects for defining Y label of PC2
pc2varvsd <-
paste("PC2", (round(vsd.variance[3, 2] * 100 - pc12, 1)), "% variance")
pc2varbcvsd <-
paste("PC2", (round(bcvsd.variance[3, 2] * 100 - pc13, 1)), "% variance")
#reactive expression for adding y labels for pc2
variance_PC2 <-
reactive({
if (input$PCAvar == "VST PCA") {
pc2varvsd
} else if (input$PCAvar == "VST + batch corrected PCA") {
pc2varbcvsd
}
})
output$PCAplot <- renderPlot ({
colors <-
c(input$PCAcolor1, input$PCAcolor2)
ggplot(PCAdata(), aes(x = PC1, y = PC2, fill = batch, shape = condition)) +
geom_point(size = 5) +
scale_shape_manual(values = c(21, 24), name = '') +
scale_fill_manual(values = colors) +
theme_cowplot() +
theme(plot.background = element_rect(fill = "#FFFFFF", colour = "#FFFFFF")) +
theme(panel.background = element_rect(fill = "#FFFFFF", colour = "#FFFFFF")) +
xlab(variance_PC1()) +
ylab(variance_PC2()) +
ggtitle(PCA_title()) +
guides(fill=guide_legend(override.aes = list(color=colors))) +
geom_text_repel(aes(label=sample_name),hjust=0, vjust=0)
})
#PCA plots download ####
output$downloadPlotPCA <- downloadHandler(
filename = function() { paste(input$PCAvar, '.png', sep='') },
content = function(file) {
ggsave(file, device = "png", width = 8, height = 6, units = "in",dpi = 72)
}
)
PCA_var_title <-
reactive({
if (input$PCAvar == "VST PCA") {
print("VST PC variance")
} else if (input$PCAvar == "VST + batch corrected PCA") {
print("VST + batch corrected PC variance")
}
})
# PCA scree plot ####
output$PCAvarplot <- renderPlot ({
ggplot(PC_var_data(),
aes(x = PC,
y = variance,
group = 2)) +
geom_point(size = 2) +
geom_line() +
theme_cowplot() +
labs(x = "PC",
y = "% Variance") +
labs(title =
PCA_var_title())
})
#PCA Scree download ####
output$downloadPlotscree <- downloadHandler(
filename = function() { paste(input$PCAvarscree, '.png', sep='') },
content = function(file) {
ggsave(file, device = "png", width = 8, height = 6, units = "in",dpi = 72)
}
)
##Gene Centric output ####
updateSelectizeInput(session,"VSTCDgenechoice", choices = vst.goi$ext_gene, server = TRUE)
datavst<-
reactive({
vst.goi %>%
dplyr::filter(ext_gene %in% input$VSTCDgenechoice)
})
#make sure duplicate selections are not allowed with radio buttons
observeEvent(input$XaxisVar_CDgene, {
if(input$XaxisVar_CDgene == "xvalue") {
mychoices <- c("Gene" = "ygene")
} else if(input$XaxisVar_CDgene=="xgene") {
mychoices <- c("Value" = "yvalue")
}
updateRadioButtons(session, "YaxisVar_CDgene", choices = mychoices)
})
#x axis output
xvar_CDgene <-
eventReactive(input$XaxisVar_CDgene, {
if (input$XaxisVar_CDgene == "xvalue") {
"value"
} else if (input$XaxisVar_CDgene == "xgene") {
"ext_gene"
}
})
#y axis output f