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app.R
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library("shinythemes")
library("ggplot2")
library("edgeR")
library("clusterProfiler")
library("scales")
library("org.Hs.eg.db")
library("pheatmap")
library("shiny")
library("shinydashboard")
library("DT")
library("png")
library("grid")
library("AnnotationDbi")
COVID_19_1 <<- read.csv(file = "data/COVID-19 related genes.csv")
COVID_19_2 <<- read.csv(file = "data/COVID-19 Disease Map genes.csv")
COVID_19_1 <<- COVID_19_1$hgnc_symbol
COVID_19_2 <<- COVID_19_2$hgnc_symbol
COVID_19_3 <<- union(COVID_19_1, COVID_19_2)
COVID_19_name <<- c()
extract_tissue <<- function (tissue) {
tissueSample <- Sample[Sample$SMTSD == tissue,] # Extract the whole blood tissue sample ID
tissueSampleID <- tissueSample$SAMPID
tissueSampleID <- gsub("-", ".", tissueSampleID)
allSampleID <- as.character(colnames(gctdf))
position2 <- c() # Extract blood sample position in all the expression matrix
for (i in 1:length(tissueSampleID)) {
for (j in 1:length(allSampleID)) {
if (tissueSampleID[i] == allSampleID[j]) {
position2 [j] = j
}
}
}
position2 <- position2[!is.na(position2)]
return(position2)
}
## Function to extract DE genes
findCOVID_DE_Genes <<- function(DE.genes) {
DE.genes <- DE.genes$table
rownames(DE.genes) <- gsub("\\.[0-9]*$", "", rownames(DE.genes))
DE.genes_COVID.19 <- DE.genes[rownames(DE.genes) %in% COVID_19_ID,]
DE.genes_COVID.19$padj <- p.adjust(DE.genes_COVID.19$PValue, method = 'fdr', n = length(COVID_19))
return(DE.genes_COVID.19)
}
server <- function(input, output, session){
observeEvent(input$tabs == "introduction", {
if (req(input$tabs) == "DataAnalysis") {
values <- reactiveValues()
values$successStep1 <- FALSE
observeEvent(input$run, {
withProgress(message = 'Start analysis:', value = 0, {
n <- 7
incProgress(1/n, detail = c("Loading data..."))
backgroundGene <<- input$background
if (backgroundGene == "All") {
COVID_19 <<- COVID_19_3
} else if (backgroundGene == "COVID-19 Disease Map genes") {
COVID_19 <<- COVID_19_2
} else {
COVID_19 <<- COVID_19_1
}
COVID_19_ID <- as.character(mapIds(org.Hs.eg.db,
keys=as.character(COVID_19),
column="ENSEMBL",
keytype="SYMBOL",
multiVals="first"))
COVID_19_ID <<- as.character(COVID_19_ID)[!is.na(COVID_19_ID)]
#mart <<- useMart("ENSEMBL_MART_ENSEMBL")
#mart <<- useDataset("hsapiens_gene_ensembl", mart)
COVID_19_name <<- as.character(COVID_19)
updateSelectInput(session, "GeneSymbol", choices = COVID_19_name)
#COVID_19_ID <<- getBM(attributes='ensembl_gene_id',
# filters = 'hgnc_symbol',
# values = COVID_19_name,
# mart = mart)
#COVID_19_ID <<- as.data.frame(COVID_19_ID)
#COVID_19_ID <<- unique(as.character(COVID_19_ID$ensembl_gene_id))
Sample <<- read.delim("data/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt") # The ID here is the sample ID
annotationFIle <<- read.delim("data/GTEx_Analysis_v8_Annotations_SubjectPhenotypesDS.txt") ## The ID here is the subject ID
#tissue <- "Whole Blood"
tissue <<- input$Tissue # need to change to any tissue you like
if (tissue == "Whole Blood") {
gctdf <<- read.delim(file = "data/gctFileBlood.txt")
} else if (tissue == "Spleen") {
gctdf <<- read.delim(file = "data/Spleen.txt")
} else if (tissue == "lymphocytes") {
gctdf <<- read.delim(file = "data/lymphocytes.txt")
} else if (tissue == "Stomach") {
gctdf <<- read.delim(file = "data/gctFileStomach.txt")
} else if (tissue == "Kidney - Cortex") {
gctdf <<- read.delim(file = "data/gctFileKidney.txt")
} else if (tissue == "Lung") {
gctdf <<- read.delim(file = "data/gctFileLung.txt")
} else if (tissue == "Heart - Left Ventricle") {
gctdf <<- read.delim(file = "data/gctFileHeartLeftVentricle.txt")
} else if (tissue == "Heart - Atrial Appendage") {
gctdf <<- read.delim(file = "data/gctFileHeartAtrialAppendage.txt")
} else if (tissue == "Brain - Cortex") {
gctdf <<- read.delim (file = "data/gctFileBrainCortex.txt")
} else if (tissue == "Brain - Hippocampus") {
gctdf <<- read.delim(file = "data/gctFileBrainHippocampus.txt")
}
#Pvalue <- 0.05
#ageSelect <- "age>60"
ageSelect <<- input$ageSelect
PValue <<- as.numeric(input$PValue)
LogFC <<- as.numeric(input$LogFC)
if (input$factor == "Gender") {
factorValue = TRUE
} else {
factorValue = FALSE
}
gctFIle <- gctdf
#gctFile= gctFIle
analysisGct <<- function(gctFile, gctFileTPM) {
gctFile <- gctFile[rowSums(gctFile) !=0,] ## Remove the 0 reads row
#gctFileTPM <- gctFileTPM[rowSums(gctFileTPM) !=0,] ## Remove the 0 reads row
## Extract the ID from the annotation file
AllID <- annotationFIle$SUBJID
sex <- annotationFIle$SEX
age <- annotationFIle$AGE
#gctFile <- gctdf[,position2]
gctFileID <- colnames(gctFile)
# change sample ID to subject ID
SampleNewID <- gsub("\\.", "-", colnames(gctFile)) # change sample ID to subject ID
SampleNewID <- gsub("-[0-9][0-9][0-9][0-9]-.*", "", SampleNewID)
colnames(gctFile) <- SampleNewID
## Extract the annotation file
annotationTissue <- annotationFIle[as.character(AllID) %in% as.character(SampleNewID),]
## Remove the data without the annotation
removeData <<- which(is.na(annotationTissue$DTHHRDY)) # remove the NA data
## If NA data is existed, remove it
if (length(removeData) != 0){
annotationTissue <- annotationTissue[-c(removeData),]
gctFile <- gctFile[,-c(removeData)]
#gctFileTPM <- gctFileTPM[,-c(removeData)]
}
incProgress(1/n, detail = c("Normalizing data..."))
gender <- as.character(annotationTissue$SEX)
gender[gender == 1] <- "Male"
gender[gender == 2] <- "Female"
dieReason <- as.character(annotationTissue$DTHHRDY)
ageGroup <- as.character(annotationTissue$AGE)
if (ageSelect == "age>30") {
ageGroup[ageGroup != "30-39" & ageGroup != "40-49" & ageGroup != "50-59" & ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "30-39" | ageGroup == "40-49"| ageGroup == "50-59" | ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>40") {
ageGroup[ageGroup != "40-49" & ageGroup != "50-59" & ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "40-49"| ageGroup == "50-59" | ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>50") {
ageGroup[ageGroup != "50-59" & ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "50-59" | ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>60") {
ageGroup[ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>70") {
ageGroup[ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "70-79"] <- "Old"
}
dieReason[dieReason == "0" | dieReason == "N"] <- "A"
dieReason[dieReason != "A"] <- "N"
## Make it global
gender <<- gender
dieReason <<- dieReason
ageGroup <<- ageGroup
if (tissue == "Whole Blood") {
gctFileNormal <- gctFile[,dieReason == "N"]
rownames(gctFileNormal) <- gsub("\\.[0-9]*$", "", rownames(gctFileNormal))
genderNormal <- as.factor(gender[dieReason != "A"])
ageGroupNormal <- as.factor(ageGroup[dieReason != "A"])
} else {
#gctFileNormal <- gctFile[,dieReason == "N"]
gctFileNormal <- gctFile
rownames(gctFileNormal) <- gsub("\\.[0-9]*$", "", rownames(gctFileNormal))
#genderNormal <- as.factor(gender[dieReason != "A"])
genderNormal <- as.factor(gender)
#ageGroupNormal <- as.factor(ageGroup[dieReason != "A"])
ageGroupNormal <- as.factor(ageGroup)
}
# Experiment design
designNormal <- model.matrix(~ genderNormal + ageGroupNormal)
GroupNormal <<- factor(paste(genderNormal, ageGroupNormal,sep="."))
DE.genes.Normal <- DGEList(gctFileNormal)
incProgress(1/n, detail = c("calculating normal factors..."))
DE.genes.Normal <- calcNormFactors(DE.genes.Normal)
incProgress(1/n, detail = c("estimating binomial dispersions..."))
DE.genes.Normal <- estimateDisp(DE.genes.Normal, designNormal)
incProgress(1/n, detail = c("Fit linear model"))
DE.genes.Normal <- glmQLFit(DE.genes.Normal, designNormal)
incProgress(1/n, detail = c("Conducting genewise statistical tests..."))
if (factorValue) {
DE.genes.normal.list <<- glmQLFTest(DE.genes.Normal, coef = 2)
} else {
DE.genes.normal.list <<- glmQLFTest(DE.genes.Normal, coef = 3)
}
DE.genes.normal.list_p <- findCOVID_DE_Genes(DE.genes.normal.list)
DE.genes.normal.list_p <- DE.genes.normal.list_p[DE.genes.normal.list_p$PValue < PValue,]
fittedValuesRaw <<- DE.genes.Normal$fitted.values
DE.genes.set <<- DE.genes.normal.list_p[abs(DE.genes.normal.list_p$logFC) > LogFC,]
if (nrow(DE.genes.set) < 2) {
return(FALSE)
} else {
fittedValues <- DE.genes.Normal$fitted.values[rownames(DE.genes.set),]
medians.normal <<- t(apply(fittedValues, 1, function (x) tapply(x, GroupNormal, median)))
GroupName.normal <- as.character(mapIds(org.Hs.eg.db,
keys=rownames(DE.genes.set),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first"))
#name <- getBM(attributes=c('hgnc_symbol','ensembl_gene_id'),
# filters = 'ensembl_gene_id',
# values = rownames(DE.genes.set),
# mart = mart,uniqueRows = FALSE)
#temp <- rownames(DE.genes.set)
#colnames(name) <- c("SYMBOL", "ID")
#GroupName.normal <- name[match(temp,name$ID),]
rownames(medians.normal) <- GroupName.normal
HM.data.normal <- t(scale(t(medians.normal)))
rownames(DE.genes.set) <- GroupName.normal
DE.genes.set$SYMBOL <- GroupName.normal
DE.genes.set$logFC <- round(DE.genes.set$logFC,2)
DE.genes.set$PValue <- scientific(DE.genes.set$PValue, digits = 2)
DE.genes.set$padj <- scientific(DE.genes.set$padj, digits = 2)
DE.genes.set$logCPM <- scientific(DE.genes.set$logCPM, digits = 2)
DE.genes.set$F <- round(DE.genes.set$F, 2)
col_order <- c("SYMBOL", "logFC","logCPM", "F", "PValue", "padj")
DE.genes.set <- DE.genes.set[,col_order]
resultAll <- list(HM.data.normal, DE.genes.set)
return(resultAll)
}
}
result <<- analysisGct(gctFile = gctFIle, gctFileTPM = gctFIleTPM)
if (is.logical(result)) {
img1 <- readPNG("www/WrongGene.PNG")
output$myHeatmap <- renderPlot({grid.raster(img1)})
error <- c("We can't find DE genes. Please higher the threshold!")
output$geneName <- renderDataTable({grid.raster(error)})
thedata <<- reactive(geneName)
} else {
output$myHeatmap <- renderPlot(
{
pheatmap(result[[1]], angle_col = c("0"), fontsize_col = 14 ,fontsize = 9,cluster_cols = FALSE)
}
)
geneName <<- as.data.frame(result[[2]])
rownames(geneName) <- NULL
output$geneName <<- renderDataTable({geneName})
thedata <<- reactive(geneName)
}
})
values$successStep1<-TRUE
showNotification("Done. Please check the result.", type= "message")
})
observeEvent(input$showBoxPlot, {
shiny::validate(
need( values$successStep1==TRUE, message = ('Missing succesful Step 1')),
errorClass = showNotification("Missing succesful Step 1", type= "error")
)
if (tissue == "Whole Blood") {
genderSubGroup <- gender[dieReason != "A"]
ageSubGroup <- ageGroup[dieReason != "A"]
} else {
genderSubGroup <- gender
ageSubGroup <- ageGroup
}
subGroup <- paste0(genderSubGroup,".",ageSubGroup)
DE.genes_COVID.19 <- fittedValuesRaw[rownames(fittedValuesRaw) %in% COVID_19_ID,]
#COVID_Name <- select(EnsDb.Hsapiens.v79,
# keys = rownames(DE.genes_COVID.19),
# keytype = "GENEID", columns = c("SYMBOL","GENEID"))
#name <<- getBM(attributes=c('hgnc_symbol','ensembl_gene_id'),
# filters = 'ensembl_gene_id',
# values = rownames(DE.genes_COVID.19),
# mart = mart,uniqueRows = FALSE)
#
#temp <- rownames(DE.genes_COVID.19)
#colnames(name) <- c("SYMBOL", "ID")
#COVID_Name <- name[match(temp,name$ID),]
COVID_Name <- as.character(mapIds(org.Hs.eg.db,
keys=rownames(DE.genes_COVID.19),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first"))
rownames(DE.genes_COVID.19) <- COVID_Name
## The position of the gene you like to return
geneSymbol <- input$GeneSymbol
i <- which(rownames(DE.genes_COVID.19) %in% geneSymbol)
tempGroup <- as.data.frame(t(as.data.frame(t(DE.genes_COVID.19[i,]))))
tempGroup$group <- subGroup
tempGroup$ageGroup <- ageSubGroup
tempGroup$Gender <- genderSubGroup
output$myBoxPlot <- renderPlot(
{
ggplot(tempGroup, aes(x = group , y = log(tempGroup[,1]), fill = Gender)) +
geom_boxplot() +
ylab(rownames(DE.genes_COVID.19)[i]) +
scale_fill_brewer(palette = "Accent") +
theme(axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 16))
}
)
})# close show boxplot
observeEvent(input$EnrichmentAnalysis,{
shiny::validate(
need( values$successStep1==TRUE, message = ('Missing succesful Step 1')),
errorClass = showNotification("Missing succesful Step 1", type= "error")
)
withProgress(message = 'Generating Plot...(be patient)', value = 0,{
n = 5
# P value and sample size
PValue2<<-as.numeric(input$PValue2)
if (input$factor2 == "All DE genes") {
target_gene_id <- as.vector(rownames(result[[2]]))
} else if (input$factor2 == "DE genes expressed more in males") {
target_gene_id <- as.vector(rownames(result[[2]]))[geneName$logFC > 0]
} else {
target_gene_id <- as.vector(rownames(result[[2]]))[geneName$logFC < 0]
}
target_gene_id <- as.character(mapIds(org.Hs.eg.db, target_gene_id, 'ENTREZID', 'SYMBOL'))
incProgress(1/n, detail = c("GO enrichment for molecular function"))
COVID_19_Background <- as.character(mapIds(org.Hs.eg.db, COVID_19_name,'ENTREZID', 'SYMBOL'))
## GO enrichment with clusterProfiler
ego_MF <- enrichGO(OrgDb="org.Hs.eg.db",
universe = COVID_19_Background,
gene = target_gene_id,
pvalueCutoff = PValue2,
ont = "MF",
readable=TRUE)
if (nrow(as.data.frame(ego_MF)) > 5) {
MF = 5
} else {
MF = nrow(as.data.frame(ego_MF))
}
ego_result_MF <- as.data.frame(ego_MF)[1:MF, ]
# ego_result_MF <- ego_result_MF[order(ego_result_MF$Count),]
incProgress(1/n, detail = c("GO enrichment for cellular component"))
ego_CC <- enrichGO(OrgDb="org.Hs.eg.db",
gene = target_gene_id,
universe = COVID_19_Background,
pvalueCutoff = PValue2,
ont = "CC",
readable=TRUE)
if (nrow(as.data.frame(ego_CC)) > 5) {
CC = 5
} else {
CC = nrow(as.data.frame(ego_CC))
}
ego_result_CC <- as.data.frame(ego_CC)[1:CC, ]
# ego_result_CC <- ego_result_CC[order(ego_result_CC$Count),]
incProgress(1/n, detail = c("GO enrichment for biological process"))
ego_BP <- enrichGO(OrgDb="org.Hs.eg.db",
gene = target_gene_id,
universe = COVID_19_Background,
pvalueCutoff = PValue2,
ont = "BP",
readable=TRUE)
if (nrow(as.data.frame(ego_BP)) > 5) {
BP = 5
} else {
BP = nrow(as.data.frame(ego_BP))
}
ego_result_BP <- na.omit(as.data.frame(ego_BP)[1:BP, ])
# ego_result_BP <- ego_result_BP[order(ego_result_BP$Count),]
incProgress(1/n, detail = c("Drawing plot"))
if (CC == 0 & MF == 0 & BP == 0) {
cat("No Go term is matched")
img <- readPNG("www/wrong.PNG")
output$myGO <- renderPlot({grid.raster(img)})
} else {
if (CC == 0 & MF == 0) {
go_enrich_df <- data.frame(ID=c(ego_result_BP$ID),
Description=c(ego_result_BP$Description),
GeneNumber=c(ego_result_BP$Count),
type=factor(c(rep("biological process", BP)), levels=c("biological process")))
CPCOLS <- c("#66C3A5")#BP
} else if (CC == 0 & BP == 0) {
go_enrich_df <- data.frame(ID=c(ego_result_MF$ID),
Description=c(ego_result_MF$Description),
GeneNumber=c(ego_result_MF$Count),
type=factor(c(rep("molecular function", MF)), levels=c("molecular function")))
CPCOLS <- c("#FD8D62")#MF
} else if (MF == 0 & BP == 0) {
go_enrich_df <- data.frame(ID=c(ego_result_CC$ID),
Description=c(ego_result_CC$Description),
GeneNumber=c(ego_result_CC$Count),
type=factor(c(rep("cellular component", CC)), levels=c("cellular component")))
CPCOLS <- c("#8DA1CB")# CC
} else if (CC == 0) {
go_enrich_df <- data.frame(ID=c(ego_result_BP$ID, ego_result_MF$ID),
Description=c(ego_result_BP$Description, ego_result_MF$Description),
GeneNumber=c(ego_result_BP$Count, ego_result_MF$Count),
type=factor(c(rep("biological process", BP),
rep("molecular function", MF)), levels=c("biological process","molecular function")))
CPCOLS <- c("#66C3A5","#FD8D62")
} else if (MF == 0) {
go_enrich_df <- data.frame(ID=c(ego_result_BP$ID, ego_result_CC$ID),
Description=c(ego_result_BP$Description, ego_result_CC$Description),
GeneNumber=c(ego_result_BP$Count, ego_result_CC$Count),
type=factor(c(rep("biological process", BP),
rep("cellular component", CC)), levels=c("biological process", "cellular component")))
CPCOLS <- c("#66C3A5", "#8DA1CB")
} else if (BP == 0) {
go_enrich_df <- data.frame(ID=c(ego_result_CC$ID, ego_result_MF$ID),
Description=c(ego_result_CC$Description, ego_result_MF$Description),
GeneNumber=c(ego_result_CC$Count, ego_result_MF$Count),
type=factor(c(rep("molecular function", MF),
rep("cellular component", CC)), levels=c("molecular function", "cellular component")))
CPCOLS <- c("#8DA1CB", "#FD8D62")
} else {
go_enrich_df <- data.frame(ID=c(ego_result_BP$ID, ego_result_CC$ID, ego_result_MF$ID),
Description=c(ego_result_BP$Description, ego_result_CC$Description, ego_result_MF$Description),
GeneNumber=c(ego_result_BP$Count, ego_result_CC$Count, ego_result_MF$Count),
type=factor(c(rep("biological process", BP), rep("cellular component", CC),
rep("molecular function", MF)), levels=c("biological process","cellular component", "molecular function")))
CPCOLS <- c( "#66C3A5", "#8DA1CB", "#FD8D62")
}
## numbers as data on x axis
go_enrich_df$number <- factor(rev(1:nrow(go_enrich_df)))
go_enrich_df$Description <- as.factor(go_enrich_df$Description)
## shorten the names of GO terms
shorten_names <- function(x, n_word=4, n_char=40){
if (length(strsplit(x, " ")[[1]]) > n_word || (nchar(x) > 40))
{
if (nchar(x) > 40) x <- substr(x, 1, 40)
x <- paste(paste(strsplit(x, " ")[[1]][1:min(length(strsplit(x," ")[[1]]), n_word)],
collapse=" "), "...", sep="")
return(x)
}
else
{
return(x)
}
}
labels=(sapply(
levels(go_enrich_df$Description)[as.numeric(go_enrich_df$Description)],
shorten_names))
names(labels) = rev(1:nrow(go_enrich_df))
## colors for bar // green, blue, orange
myGO <<- ggplot(data=go_enrich_df, aes(x=number, y=GeneNumber, fill=type)) +
geom_bar(stat="identity", width=0.8) + coord_flip() +
scale_fill_manual(values = CPCOLS) + theme_bw() +
scale_x_discrete(labels=labels) +
xlab("GO term") +
theme(axis.text=element_text(face = "bold", color="gray50", size = 15),
legend.title = element_text(size = 15),
legend.text = element_text(size = 15),
axis.title.x = element_text(size = 15),
axis.title.y = element_text(size = 15)) +
labs(title = "The Most Enriched GO Terms - COVID-19 Disease Map Genes")
output$myGO <- renderPlot(
{myGO}
)
}
})
showNotification("Done. Already Finish the GO plot", type= "message")
})
output$downloadData1 <- downloadHandler(
filename = function() {"GeneTable.csv"},
content = function(file) {
write.csv(thedata(), file, row.names = FALSE)
}
)
}
else if (req(input$tabs) == "AdvancedAnalysis") {
values_A <- reactiveValues()
values_A$successStep1 <- FALSE
values_A$successStep2 <- FALSE
observeEvent(input$addGroupInf, {
controlGroup <<- input$controlGroup
experimentalGroup <<- input$experimentalGroup
shiny::validate(
need(all(!controlGroup%in%experimentalGroup) == TRUE, message = ('A Group is existed in both control and experimental group!')),
errorClass = showNotification("Group existed in both control and experimental group!", type= "error")
)
if (input$factor_A == "Gender") {
factorValue <<- TRUE
} else {
factorValue <<- FALSE
}
if (factorValue == TRUE) {
tempControl <- gsub("\ .*", "", controlGroup)
tempExp <- gsub("\ .*", "", experimentalGroup)
shiny::validate(
need(all(!tempControl%in%tempExp) == TRUE, message = ('Group Wrong: Please check if experimental/control group have same gender')),
errorClass = showNotification("Group Wrong: Please check if experimental/control group have same gender!", type= "error")
)
} else {
tempControl <<-gsub(".* ", "", controlGroup)
tempExp <<- gsub(".* ", "", experimentalGroup)
shiny::validate(
need(all(!tempControl%in%tempExp) == TRUE, message = ('Group Wrong: Please check if experimental/control group have same age')),
errorClass = showNotification('Group Wrong: Please check if experimental/control group have same age', type = "error")
)
}
ageIndexC <- paste(gsub(".* ", "", controlGroup), collapse = " ")
matches <- regmatches(ageIndexC, gregexpr("[[:digit:]]+", ageIndexC))
ageIndexC <- max(as.numeric(unlist(matches)))
ageIndexE <- gsub(".* ", "", experimentalGroup)
matches <- regmatches(ageIndexE, gregexpr("[[:digit:]]+", ageIndexE))
ageIndexE <- max(as.numeric(unlist(matches)))
if (ageIndexC > ageIndexE) {
ageIndexC <- paste(gsub(".* ", "", controlGroup), collapse = " ")
matches <- regmatches(ageIndexC, gregexpr("[[:digit:]]+", ageIndexC))
ageIndexC <<- min(as.numeric(unlist(matches)))
} else {
ageIndexE <- gsub(".* ", "", experimentalGroup)
matches <- regmatches(ageIndexE, gregexpr("[[:digit:]]+", ageIndexE))
ageIndexE <<- min(as.numeric(unlist(matches)))
}
ageSelect <<- input$ageSelect_A
ageIndexS <- as.numeric(unlist(regmatches(ageSelect, gregexpr("[[:digit:]]+", ageSelect))))
PValue <<- as.numeric(input$PValue_A)
LogFC <<- as.numeric(input$LogFC_A)
showNotification("Success!", type = "message" )
values_A$successStep1 <- TRUE
})
observeEvent(input$run_A, {
shiny::validate(
need(values_A$successStep1, message = ('Missing experimental/control Group')),
errorClass = showNotification('Missing experimental/control Group', type = "error")
)
if (factorValue == FALSE) {
shiny::validate(
need(ageIndexC >= ageIndexS && ageIndexS >= ageIndexE || ageIndexC <= ageIndexS && ageIndexS <= ageIndexE,
message = ('Old threshold should between the expermintal group and control group')),
errorClass = showNotification('Please select a correct old people threshold', type = "error")
)
}
withProgress(message = 'Start analysis:', value = 0, {
n <- 7
backgroundGene <<- input$background_A
if (backgroundGene == "All") {
COVID_19 <<- COVID_19_3
} else if (backgroundGene == "COVID-19 Disease Map genes") {
COVID_19 <<- COVID_19_2
} else {
COVID_19 <<- COVID_19_1
}
incProgress(1/n, detail = c("Loading data..."))
COVID_19_ID <- as.character(mapIds(org.Hs.eg.db,
keys=as.character(COVID_19),
column="ENSEMBL",
keytype="SYMBOL",
multiVals="first"))
COVID_19_ID <<- as.character(COVID_19_ID)[!is.na(COVID_19_ID)]
#mart <<- useMart("ENSEMBL_MART_ENSEMBL")
#mart <<- useDataset("hsapiens_gene_ensembl", mart)
COVID_19_name <<- as.character(COVID_19)
updateSelectInput(session, "GeneSymbol_A", choices = COVID_19_name)
?updateSelectInput
#COVID_19_ID <<- getBM(attributes='ensembl_gene_id',
# filters = 'hgnc_symbol',
# values = COVID_19_name,
# mart = mart)
#COVID_19_ID <<- as.data.frame(COVID_19_ID)
#COVID_19_ID <<- unique(as.character(COVID_19_ID$ensembl_gene_id))
Sample <<- read.delim("data/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt") # The ID here is the sample ID
annotationFIle <<- read.delim("data/GTEx_Analysis_v8_Annotations_SubjectPhenotypesDS.txt") ## The ID here is the subject ID
#tissue <- "Whole Blood"
tissue <- input$Tissue_A # need to change to any tissue you like
if (tissue == "Whole Blood") {
gctdf <<- read.delim(file = "data/gctFileBlood.txt")
} else if (tissue == "Stomach") {
gctdf <<- read.delim(file = "data/gctFileStomach.txt")
} else if (tissue == "Kidney - Cortex") {
gctdf <<- read.delim(file = "data/gctFileKidney.txt")
} else if (tissue == "Lung") {
gctdf <<- read.delim(file = "data/gctFileLung.txt")
} else if (tissue == "Heart - Left Ventricle") {
gctdf <<- read.delim(file = "data/gctFileHeartLeftVentricle.txt")
} else if (tissue == "Heart - Atrial Appendage") {
gctdf <<- read.delim(file = "data/gctFileHeartAtrialAppendage.txt")
} else if (tissue == "Brain - Cortex") {
gctdf <<- read.delim (file = "data/gctFileBrainCortex.txt")
} else if (tissue == "Brain - Hippocampus") {
gctdf <<- read.delim(file = "data/gctFileBrainHippocampus.txt")
} else if (tissue == "Spleen") {
gctdf <<- read.delim(file = "data/Spleen.txt")
} else if (tissue == "lymphocytes") {
gctdf <<- read.delim(file = "data/lymphocytes.txt")
}
#Pvalue <- 0.05
#ageSelect <- "age>60"
gctFIle <- gctdf
#gctFile <- gctFIle
analysisGct_A <<- function(gctFile, gctFileTPM) {
gctFile <- gctFile[rowSums(gctFile) !=0,] ## Remove the 0 reads row
#gctFileTPM <- gctFileTPM[rowSums(gctFileTPM) !=0,] ## Remove the 0 reads row
## Extract the ID from the annotation file
AllID <- annotationFIle$SUBJID
sex <- annotationFIle$SEX
age <- annotationFIle$AGE
#gctFile <- gctdf[,position2]
gctFileID <- colnames(gctFile)
# change sample ID to subject ID
SampleNewID <- gsub("\\.", "-", colnames(gctFile)) # change sample ID to subject ID
SampleNewID <- gsub("-[0-9][0-9][0-9][0-9]-.*", "", SampleNewID)
colnames(gctFile) <- SampleNewID
## Extract the annotation file
annotationTissue <- annotationFIle[as.character(AllID) %in% as.character(SampleNewID),]
## Remove the data without the annotation
removeData <<- which(is.na(annotationTissue$DTHHRDY)) # remove the NA data
## If NA data is existed, remove it
if (length(removeData) != 0){
annotationTissue <- annotationTissue[-c(removeData),]
gctFile <- gctFile[,-c(removeData)]
#gctFileTPM <- gctFileTPM[,-c(removeData)]
}
incProgress(1/n, detail = c("Normalizing data..."))
gender <- as.character(annotationTissue$SEX)
gender[gender == 1] <- "Male"
gender[gender == 2] <- "Female"
dieReason <- as.character(annotationTissue$DTHHRDY)
ageGroup <- as.character(annotationTissue$AGE)
if (ageSelect == "age>30") {
ageGroup[ageGroup != "30-39" & ageGroup != "40-49" & ageGroup != "50-59" & ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "30-39" | ageGroup == "40-49"| ageGroup == "50-59" | ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>40") {
ageGroup[ageGroup != "40-49" & ageGroup != "50-59" & ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "40-49"| ageGroup == "50-59" | ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>50") {
ageGroup[ageGroup != "50-59" & ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "50-59" | ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>60") {
ageGroup[ageGroup != "60-69" & ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "60-69" | ageGroup == "70-79"] <- "Old"
} else if (ageSelect == "age>70") {
ageGroup[ageGroup != "70-79"] <- "Young"
ageGroup[ageGroup == "70-79"] <- "Old"
}
dieReason[dieReason == "0" | dieReason == "N"] <- "A"
dieReason[dieReason != "A"] <- "N"
## Make it global
gender <<- gender
dieReason <<- dieReason
ageGroup <<- ageGroup
if (tissue == "Whole Blood") {
gctFileNormal <- gctFile[,dieReason == "N"]
rownames(gctFileNormal) <- gsub("\\.[0-9]*$", "", rownames(gctFileNormal))
genderNormal <- as.factor(gender[dieReason != "A"])
ageGroupNormal <- as.factor(ageGroup[dieReason != "A"])
} else {
#gctFileNormal <- gctFile[,dieReason == "N"]
gctFileNormal <- gctFile
rownames(gctFileNormal) <- gsub("\\.[0-9]*$", "", rownames(gctFileNormal))
#genderNormal <- as.factor(gender[dieReason != "A"])
genderNormal <- as.factor(gender)
#ageGroupNormal <- as.factor(ageGroup[dieReason != "A"])
ageGroupNormal <- as.factor(ageGroup)
}
## Extract User Select Group
expFactor1 <- gsub(" .*","",gsub("sex:","",experimentalGroup))
if (expFactor1 == "male") {
expFactor1 = "Male"
} else {
expFactor1 = "Female"
}
expFactor2 <- gsub(".*:", "",experimentalGroup)
contFactor1 <- gsub(" .*","",gsub("sex:","",controlGroup))
if (contFactor1 == "male"){
contFactor1 = "Male"
} else {
contFactor1 = "Female"
}
contFactor2 <- gsub(".*:", "",controlGroup)
if (as.numeric(gsub("..-","",expFactor2)) - as.numeric(gsub("...>","",ageSelect)) <= 0) {
expFactor2 = c("Young")
} else {
expFactor2 = c("Old")
}
if (as.numeric(gsub("..-","",contFactor2)) - as.numeric(gsub("...>","",ageSelect)) <= 0) {
contFactor2 = c("Young")
} else {
contFactor2 = c("Old")
}
NormalAll <- paste0(as.character(genderNormal), ".",as.character(ageGroupNormal))
ExperAll <- paste0(as.character(expFactor1), ".", as.character(expFactor2))
contrAll <- paste0(as.character(contFactor1), ".", as.character(contFactor2))
SelectAll <- c(ExperAll, contrAll)
genderSelect <<- as.factor(as.character(genderNormal[NormalAll %in% SelectAll]))
ageGroupSelect <<- as.factor(as.character(ageGroupNormal[NormalAll %in% SelectAll]))
gctFileSelect <<- gctFileNormal[,NormalAll %in% SelectAll]
# Experiment design
## Determine how many contrast I need to put in the experiment
if (factorValue == TRUE) {
if (length(levels(ageGroupSelect)) == 1 ){
designSelect <- model.matrix(~genderSelect)
} else {
designSelect <- model.matrix(~genderSelect + ageGroupSelect)
}
} else {
if (length(levels(genderSelect)) == 1) {
designSelect <- model.matrix(~ageGroupSelect)
} else {
designSelect <- model.matrix(~genderSelect + ageGroupSelect)
}
}
GroupSelect <<- factor(paste(genderSelect, ageGroupSelect,sep="."))
DE.genes.Select <- DGEList(gctFileSelect)
incProgress(1/n, detail = c("calculating normal factors..."))
DE.genes.Select <- calcNormFactors(DE.genes.Select)
incProgress(1/n, detail = c("estimating binomial dispersions..."))
DE.genes.Select <- estimateDisp(DE.genes.Select, designSelect)
incProgress(1/n, detail = c("Fit linear model"))
DE.genes.Select <- glmQLFit(DE.genes.Select, designSelect)
incProgress(1/n, detail = c("Conducting genewise statistical tests..."))
## Test position
if(factorValue) {
if (length(levels(ageGroupSelect)) == 1){
DE.genes.Select.list <<- glmQLFTest(DE.genes.Select, coef = 2)
} else {
DE.genes.Select.list <<- glmQLFTest(DE.genes.Select, coef = 2)
}
} else {
if (length(levels(genderSelect)) == 1){
DE.genes.Select.list <<- glmQLFTest(DE.genes.Select, coef = 2)
} else {
DE.genes.Select.list <<- glmQLFTest(DE.genes.Select, coef = 3)
}
}
DE.genes.Select.list_p <- findCOVID_DE_Genes(DE.genes.Select.list)
DE.genes.Select.list_p <- DE.genes.Select.list_p[DE.genes.Select.list_p$PValue < PValue,]
fittedValuesRaw <<- DE.genes.Select$fitted.values
DE.genes.set <<- DE.genes.Select.list_p[abs(DE.genes.Select.list_p$logFC) > LogFC,]
if (nrow(DE.genes.set) < 2) {
return(FALSE)
} else {
fittedValues <- DE.genes.Select$fitted.values[rownames(DE.genes.set),]
medians.Select <<- t(apply(fittedValues, 1, function (x) tapply(x, GroupSelect, median)))
GroupName.Select <- as.character(mapIds(org.Hs.eg.db,
keys=rownames(DE.genes.set),
column="SYMBOL",
keytype="ENSEMBL",
multiVals="first"))
#name <- getBM(attributes=c('hgnc_symbol','ensembl_gene_id'),
# filters = 'ensembl_gene_id',
# values = rownames(DE.genes.set),
# mart = mart,uniqueRows = FALSE)
#temp <- rownames(DE.genes.set)
#colnames(name) <- c("SYMBOL", "ID")
#GroupName.Select <- name[match(temp,name$ID),]
rownames(medians.Select) <- GroupName.Select
HM.data.Select <- t(scale(t(medians.Select), center = FALSE))
rownames(DE.genes.set) <- GroupName.Select
DE.genes.set$SYMBOL <- GroupName.Select
DE.genes.set$logFC <- round(DE.genes.set$logFC,2)
DE.genes.set$PValue <- scientific(DE.genes.set$PValue, digits = 2)
DE.genes.set$padj <- scientific(DE.genes.set$padj, digits = 2)
DE.genes.set$logCPM <- scientific(DE.genes.set$logCPM, digits = 2)
DE.genes.set$F <- round(DE.genes.set$F, 2)
col_order <- c("SYMBOL", "logFC","logCPM", "F", "PValue", "padj")
DE.genes.set <- DE.genes.set[,col_order]
resultAll <- list(HM.data.Select, DE.genes.set)
return(resultAll)
}
}
result <<- analysisGct_A(gctFile = gctFIle, gctFileTPM = gctFIleTPM)
if (is.logical(result)) {
img1 <- readPNG("www/WrongGene.PNG")
output$myHeatmap_A <- renderPlot({grid.raster(img1)})
error <- c("We can't find DE genes. Please higher the threshold!")
output$geneName_A <- renderDataTable({grid.raster(error)})
thedata <<- reactive(geneName_A)
} else {
output$myHeatmap_A <- renderPlot(
{
pheatmap(result[[1]], angle_col = c("0"), fontsize_col = 14 ,fontsize = 9,cluster_cols = FALSE)
}
)
geneName_A <<- as.data.frame(result[[2]])