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irisServer.R
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irisServer.R
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#---------------------------------------------------------------------
# Title: IRIS - Server Script
# Author: Brandon Monier
# Created: 2018-01-26 at 11:32:02
# Last Modified: 2018-10-29 at 11:01:10
#---------------------------------------------------------------------
#---------------------------------------------------------------------
# CONTENTS
#---------------------------------------------------------------------
#
# IRIS server function organization
#
# 01 DATA...... data loading
# 02 QC........ quality control for preliminary analysis
# 03 DGE....... differential gene expression analysis
# 04 HEAT...... heatmap plots generation
# 05 BIC....... biclustering algorithms and visualization
# 06 COR....... correlation analysis and visualization
# 07 SESSION... generate session info
# 08 GEO....... gene expression omnibus metadata generation
# 09 TABS...... tab links using `updateNavbarPage`
#
#---------------------------------------------------------------------
#
# IRIS application layout
#
# IRIS
# |- Welcome
# |- Submit and QC
# |- File Summary
# |- Count Summary
# |- Discovery-Driven Analysis
# |- Correlation
# |- PCA
# |- MDS
# |- tSNE
# |- Heatmap
# |- Biclustering
# |- Clustering
# |- DGE Analysis
# |- Overview
# |- Plots
# |- GEO
# |- Dynamic sample generation (see "GEO" layout)
# |- More
# |- Tutorial
# |- FAQ
# |- About Us
# |- Session Info
#
#---------------------------------------------------------------------
#
# IRIS gene expression omnibus (GEO) layout
#
# IRIS
# |- GEO
# |- Series
# |- Title
# |- Summary
# |- Overall design
# |- Contributor (DYNAMIC)
# |- Supplementary file
# |- SRA center name code
# |- Samples
# |- Sample name
# |- Title
# |- Source name
# |- Organism
# |- Chacteristics (BASED ON METADATA)
# |- Molecule
# |- Description
# |- Processed data file (DYNAMIC)
# |- Raw file (DYNAMIC)
# |- Protocols
# |- Growth protocol
# |- Treatment protocol
# |- Extract protocol
# |- Library construction protocol
# |- Library strategy
# |- Data processing pipeline
# |- Data processing step (DYNAMIC)
# |- Genome build
# |- Processed data files format and content
# |- Processed data files
# |- File name
# |- File type
# |- File checksum
# |- Raw files
# |- File name
# |- File type
# |- File checksum
# |- Instrument model
# |- Read length
# |- Single or paired-end
# |- Paired-end experiments (IF RAW FILE -> PAIRED END)
# |- File name 1
# |- File name 2
# |- Average insert size
# |- Standard deviation
# |- SOLiD experiments (IF RAW FILE -> SOLiD)
# |- File name 1
# |- File name 2
# |- File name 3
# |- File name 4
# |- Average insert size
# |- Standard deviation
# |- Download options
# |- Metadata (.xlsx)
# |- Processed data (.zip)
# |- Both meta- and processed data (.zip)
#
#---------------------------------------------------------------------
# Change file upload size to 50 MB and sanitize errors
options(
shiny.maxRequestSize = 50 * 1024^2,
shiny.sanitize.errors = TRUE
)
# Server function
irisServer <- function(input, output, session) {
###################################################################
###################################################################
### SECTION 01 - DATA LOAD (DATA)
###################################################################
###################################################################
## DATA - Data load option - count data
output$file1 <- renderUI({
if (input$examplechoice == "no" | input$examplechoice == "scrna" | input$examplechoice == "scrna10x") {
fileInput(
inputId = "file1",
label = "Submit count data (CSV)",
accept = c(
"text/csv",
"text/comma-separated-values,text/plain",
".csv"
)
)
} else {
return()
}
})
## DATA - Data load option - metadata
output$file2 <- renderUI({
if (input$examplechoice == "no" | input$examplechoice == "scrna" | input$examplechoice == "scrna10x") {
fileInput(
inputId = "file2",
label = "Submit metadata (CSV)",
accept = c(
"text/csv",
"text/comma-separated-values,text/plain",
".csv"
)
)
} else {
return()
}
})
## DATA - Data load option - gene length
output$file3 <- renderUI({
if (input$examplechoice == "scrna") {
fileInput(
inputId = "file3",
label = "Submit ID lengths (CSV)",
accept = c(
"text/csv",
"text/comma-separated-values,text/plain",
".csv"
)
)
} else {
return()
}
})
## DATA - Filter choice
output$filter_choice <- renderUI({
if (input$examplechoice == "yes3" | input$examplechoice == "scrna") {
textInput(
inputId = "prefilt",
label = withMathJax(
"Filter cutoff (TPM \\( < n\\))"
),
value = as.numeric(1)
)
} else if (input$examplechoice == "scrna10x") {
textInput(
inputId = "prefilt",
label = withMathJax(
"Filter cutoff (TPM \\( < n\\))"
),
value = as.numeric(0)
)
} else {
textInput(
inputId = "prefilt",
label = withMathJax(
"Filter cutoff (count data row sums \\( < n\\))"
),
value = as.numeric(10)
)
}
})
###################################################################
###################################################################
### SECTION 02 - QUALITY CONTROL (QC)
###################################################################
###################################################################
## QC - reactive - load and add data to DESeqDataSet class
ddsout <- eventReactive(input$goqc, {
if (input$examplechoice == "yes1") { # Small example
cts <- as.matrix(
read.csv(
"./data/count-data-small.csv",
header=TRUE,
row.names = 1
)
)
coldata <- read.csv(
"./data/col-data-small.csv",
header = TRUE,
row.names = 1
)
## Add check for "raw counts"
cols <- colnames(coldata)
coldata[cols] <- lapply(coldata[cols], factor)
cts <- cts[, rownames(coldata)]
n <- input$prefilt
n <- as.numeric(n)
cts.filt <- cts[rowSums(cts) > n, ]
dds <- DESeqDataSetFromMatrix(
countData = cts.filt,
colData = coldata,
design = ~ 1
)
cts_raw_check <- "pass"
} else if (input$examplechoice == "yes2") { # Big example
cts <- as.matrix(
read.csv(
"./data/count-data-big.csv",
header = TRUE,
row.names = 1
)
)
coldata <- read.csv(
"./data/col-data-big.csv",
header = TRUE,
row.names = 1
)
cols <- colnames(coldata)
coldata[cols] <- lapply(coldata[cols], factor)
cts <- cts[, rownames(coldata)]
n <- input$prefilt
n <- as.numeric(n)
cts.filt <- cts[rowSums(cts) > n, ]
dds <- DESeqDataSetFromMatrix(
countData = cts.filt,
colData = coldata,
design = ~ 1
)
cts_raw_check <- "pass"
} else if (input$examplechoice == "yes3") { # scRNA example
cts <- read.csv(
"./data/count-data-scrna.csv",
header = TRUE,
row.names = 1
)
coldata <- read.csv(
"./data/col-data-scrna.csv",
header = TRUE,
row.names = 1
)
gen.len <- read.csv(
"./data/gen-len-scrna.csv",
header = TRUE,
row.names = 1
)
cols <- colnames(coldata)
coldata[cols] <- lapply(coldata[cols], factor)
cts.nam <- row.names(cts)
gen.len <- gen.len[order(
match(row.names(gen.len), row.names(cts))), , drop = FALSE]
# Normalize for gene length (TPM)
x <- cts / as.matrix(gen.len)
# Normalize for sequencing depth (TPM)
tpm.mat <- t( t(x) * 1e6 / colSums(x) )
# Filter raw counts based TPM values
n <- input$prefilt
n <- as.numeric(n)
tpm.mat <- cts[rowSums(cts) > n, ]
cts.filt <- cts[rownames(tpm.mat), ]
# Assign to DESeqDataSet object
dds <- DESeqDataSetFromMatrix(
countData = cts.filt,
colData = coldata,
design = ~ 1
)
cts_raw_check <- "pass"
} else if (input$examplechoice == "no" | input$examplechoice == "scrna10x") { # user input (normal)
cts <- input$file1
coldata <- input$file2
cts <- read.csv(
cts$datapath,
header = TRUE,
row.names = NULL
)
# Set the first column name to 'gene_id' if missing
if(colnames(cts)[1] == ""){
colnames(cts)[1] == "gene_id"
}
# If there are duplicated row names, only keep the first
if (length(which(duplicated.default(cts[,1]))) > 0) {
cts <- cts[-which(duplicated.default(cts[,1]) == T),]
}
rownames(cts) <- cts[,1]
cts <- as.matrix(cts[,-1])
if (!is.integer(cts)) {
warning("Not raw counts - rounding object...")
cts <- round(cts, 0)
cts_raw_check <- "fail"
} else {
cts_raw_check <- "pass"
}
coldata <- read.csv(
coldata$datapath,
header = TRUE,
row.names = 1
)
cols <- colnames(coldata)
coldata[cols] <- lapply(coldata[cols], factor)
cts <- cts[, rownames(coldata)]
n <- input$prefilt
n <- as.numeric(n)
cts.filt <- cts[rowSums(cts) > n, ]
cts.filt <- na.omit(cts.filt)
dds <- DESeqDataSetFromMatrix(
countData = cts.filt,
colData = coldata,
design = ~ 1
)
} else if (input$examplechoice == "scrna") { # user input (scRNA)
cts <- input$file1
coldata <- input$file2
gen_len <- input$file3
cts <- read.csv(
cts$datapath,
header = TRUE,
row.names = NULL
)
#set the first column name to 'gene_id' if missing
if(colnames(cts)[1] == ""){
colnames(cts)[1] == "gene_id"
}
# if there are duplicated row names, only keep the first
if (length(which(duplicated.default(cts[,1]))) > 0) {
cts <- cts[-which(duplicated.default(cts[,1]) == T),]
}
rownames(cts) <- cts[,1]
cts <- as.matrix(cts[,-1])
if (!is.integer(cts)) {
warning("Not raw counts - rounding object...")
cts <- round(cts, 0)
cts_raw_check <- "fail"
} else {
cts_raw_check <- "pass"
}
coldata <- read.csv(
coldata$datapath,
header = TRUE,
row.names = 1
)
gen_len <- read.csv(
gen_len$datapath,
header = TRUE,
row.names = 1
)
cts.nam <- row.names(cts)
gen_len <- gen_len[order(
match(row.names(gen_len), row.names(cts))), , drop = FALSE]
# Normalize for gene length (TPM)
x <- cts / as.matrix(gen_len)
# Normalize for sequencing depth (TPM)
tpm.mat <- t( t(x) * 1e6 / colSums(x) )
# Filter raw counts based TPM values
n <- input$prefilt
n <- as.numeric(n)
tpm.mat <- cts[rowSums(cts) > n, ]
cts.filt <- cts[rownames(tpm.mat), ]
# Assign to DESeqDataSet object
dds <- DESeqDataSetFromMatrix(
countData = cts.filt,
colData = coldata,
design = ~ 1
)
}
# Return objects for downstream analysis
return(list(dds, coldata, cts.filt, cts, cts_raw_check))
})
## QC - reactive - data transformation
ddstran <- eventReactive(input$goqc, {
dds <- ddsout()[[1]]
if (input$transform == "log") {
tmp <- normTransform(dds)
lab <- "log<sub>2</sub>(counts + 1)"
} else if (input$transform == "rlog") {
tmp <- rlog(dds)
lab <- "rlog(counts)"
} else if (input$transform == "vst") {
tmp <- vst(dds)
lab <- "vst(counts)"
} else if (input$transform == "raw") {
tmp <- dds
lab <- "Raw counts"
}
return(list(tmp, lab))
})
## QC - header (2) - file summary (count data)
output$filesummarycts <- renderUI({
if(input$goqc == 0) {
p(
br(),
em(
"Load data and click the 'submit' button to see the results."
),
style = "color:grey"
)
} else {
h4("Count data (first 5 rows and last 5 rows)")
}
})
## QC - header (2) - file summary (col data)
output$filesummarycoldata <- renderUI({
if(input$goqc == 0) {
return()
} else {
h4("Sample metadata")
}
})
## QC - header (2) - ID counts (pre)
output$headcountpre <- renderUI({
if(input$goqc == 0) {
return()
} else {
h4("Number of IDs (pre-filtration)")
}
})
## QC - header (2) - ID counts (post)
output$headcountpost <- renderUI({
if(input$goqc == 0) {
return()
} else {
h4("Number of IDs (post-filtration)")
}
})
## Raw data warn output
output$cts_warn <- renderUI({
if(input$goqc == 0) {
return()
} else {
if (ddsout()[[5]] == "fail") {
p(
br(),
em(
paste0(
"Your count data does not contain raw reads ",
"(i.e. whole numbers). You will not be able to ",
"run any further analyses!"
)
),
style = "color:red"
)
} else {
return()
}
}
})
## QC - verbatim console - count data head
output$fileoutputcts <- renderPrint(width = 400, {
cts <- ddsout()[[4]]
trubble(cts)
})
## QC - verbatim console - metadata
output$fileoutputcoldata <- renderPrint({
coldata <- ddsout()[[2]]
coldata
})
## QC - verbatim console - gene no. (pre-filter)
output$fileoutputcountpre <- renderPrint({
cts.pre <- ddsout()[[4]]
nrow(cts.pre)
})
## QC - verbatim console - gene no. (pre-filter)
output$fileoutputcountpost <- renderPrint({
cts.post <- ddsout()[[3]]
nrow(cts.post)
})
## QC - header (2) - boxplot
output$countbox <- renderUI({
if(input$goqc == 0) {
p(
br(),
em(
"Load data and click the 'submit' button to see the results."
),
style = "color:grey"
)
} else {
h4("Count data distributions - box and whisker")
}
})
## QC - visualize - boxplot
output$boxplot <- renderPlotly({
withProgress(message = "Compiling boxplots...", value = 0, {
incProgress()
input$goqc
tmp <- ddstran()[[1]]
tmp <- assay(tmp)
lab <- ddstran()[[2]]
isolate({
box <- as.data.frame(tmp)
box <- tidyr::gather(box)
plot_ly(
box,
type = "box",
y = ~value,
color = ~key
) %>%
layout(
margin = list(b = 90),
xaxis = list(title = "", tickangle = -45),
yaxis = list(title = lab)
)
})
})
})
## QC - DOWNLOAD BUTTON - Boxplot (pdf)
output$dlqcboxplotpdf <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqcboxplotpdfimg", "Download Static R Plot (PDF)")
}
})
## QC - DOWNLOAD PLOT - Boxplot (pdf)
output$dlqcboxplotpdfimg <- downloadHandler(
filename = function() {
paste("qc-boxplot.pdf")
},
content = function(file) {
pdf(file, width = 8, height = 6.5, onefile = FALSE) # open the pdf device
qcBoxPlot(
tmp = ddstran()[[1]],
lab = ddstran()[[2]],
tran = input$transform
)
dev.off()
}
)
## QC - DOWNLOAD BUTTON - Boxplot (png)
output$dlqcboxplotpng <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqcboxplotpngimg", "Download Static R Plot (PNG)")
}
})
## QC - DOWNLOAD PLOT - Boxplot (png)
output$dlqcboxplotpngimg <- downloadHandler(
filename = function() {
paste("qc-boxplot.png")
},
content = function(file) {
png(file, width = 900, height = 750)
qcBoxPlot(
tmp = ddstran()[[1]],
lab = ddstran()[[2]],
tran = input$transform
)
dev.off()
}
)
## QC - header (2) - histogram
output$counthist <- renderUI({
if(input$goqc == 0) {
return()
} else {
h4("Count data distributions - histogram")
}
})
## QC - visualize - histogram
output$hist <- renderPlotly({
withProgress(message = "Compiling histograms...", value = 0, {
incProgress()
input$goqc
tmp <- ddstran()[[1]]
tmp <- assay(tmp)
lab <- ddstran()[[2]]
isolate({
hist <- as.data.frame(tmp)
hist <- tidyr::gather(hist)
plot_ly(
data = hist,
type = "histogram",
x = ~value,
color = ~key
) %>%
layout(
xaxis = list(title = lab),
yaxis = list(title = "Frequency")
)
})
})
})
## QC - DOWNLOAD BUTTON - Histogram (pdf)
output$dlqchistpdf <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqchistpdfimg", "Download Static R Plot (PDF)")
}
})
## QC - DOWNLOAD PLOT - Histogram (pdf)
output$dlqchistpdfimg <- downloadHandler(
filename = function() {
paste("qc-histogram.pdf")
},
content = function(file) {
pdf(file, width = 9, height = 6.5, onefile = FALSE) # open the pdf device
qcHist(
tmp = ddstran()[[1]],
lab = ddstran()[[2]],
tran = input$transform
)
dev.off()
}
)
## QC - DOWNLOAD BUTTON - Histogram (png)
output$dlqchistpng <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqchistpngimg", "Download Static R Plot (PNG)")
}
})
## QC - DOWNLOAD PLOT - Histogram (png)
output$dlqchistpngimg <- downloadHandler(
filename = function() {
paste("qc-histogram.png")
},
content = function(file) {
png(file, width = 900, height = 750)
qcHist(
tmp = ddstran()[[1]],
lab = ddstran()[[2]],
tran = input$transform
)
dev.off()
}
)
## QC - header (2) - barplot
output$counttotal <- renderUI({
if(input$goqc == 0) {
p(
br(),
em(
"Load data and click the 'submit' button to see the results."
),
style = "color:grey"
)
} else {
h4("Total reads")
}
})
## QC - visualize - barplot
output$barplot <- renderPlotly({
withProgress(message = "Compiling barplots...", value = 0, {
incProgress()
input$goqc
tmp <- ddstran()[[1]]
tmp <- assay(tmp)
lab <- ddstran()[[2]]
isolate({
incProgress()
dds <- ddsout()[[1]]
bar <- as.data.frame(assay(dds))
bar <- colSums(bar)
bar <- as.data.frame(t(bar))
bar <- gather(bar)
plot_ly(
bar,
type = "bar",
y = ~value,
x = ~key,
color = ~key
) %>%
layout(
margin = list(b = 90),
xaxis = list(title = "", tickangle = -45),
yaxis = list(title = "Counts")
)
})
})
})
## QC - DOWNLOAD BUTTON - Barplot (pdf)
output$dlqcbarplotpdf <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqcbarplotpdfimg", "Download Static R Plot (PDF)")
}
})
## QC - DOWNLOAD PLOT - Barplot (pdf)
output$dlqcbarplotpdfimg <- downloadHandler(
filename = function() {
paste("qc-barplot.pdf")
},
content = function(file) {
pdf(file, width = 8, height = 6.5, onefile = FALSE) # open the pdf device
qcBarplot(
tmp = ddsout()[[1]]
)
dev.off()
}
)
## QC - DOWNLOAD BUTTON - Barplot (png)
output$dlqcbarplotpng <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqcbarplotpngimg", "Download Static R Plot (PNG)")
}
})
## QC - DOWNLOAD PLOT - Barplot (png)
output$dlqcbarplotpngimg <- downloadHandler(
filename = function() {
paste("qc-barplot.png")
},
content = function(file) {
png(file, width = 900, height = 750)
qcBarplot(
tmp = ddsout()[[1]]
)
dev.off()
}
)
## QC - header (2) - barplot
output$counttotal <- renderUI({
if(input$goqc == 0) {
p(
br(),
em(
"Load data and click the 'submit' button to see the results."
),
style = "color:grey"
)
} else {
h4("Total reads")
}
})
## QC - header (2) - PCA
output$headpca <- renderUI({
if(input$goqc == 0) {
p(
br(),
em(
"Load data and click the 'submit' button on the 'Submit and QC' tab to see the results."
),
style = "color:grey"
)
} else {
h4("Principal Component Analysis")
}
})
## QC - select input - choose factor - PCA
output$pcafact <- renderUI({
tmp <- ddsout()[[2]]
selectInput(
inputId = "pcafact",
label = "Choose factor",
choices = colnames(tmp)
)
})
## QC - visualize - PCA
output$pca <- renderPlotly({
validate(
need(input$pcafact != "", "")
)
tmp <- ddstran()[[1]]
lab <- ddstran()[[2]]
validate(
need(
expr = class(tmp) == "DESeqTransform",
message = "Please transform raw counts to visualize PCA."
)
)
pca.lab <- plotPCA(
tmp,
intgroup = input$pcafact
)
pca <- plotPCA(
tmp,
intgroup = input$pcafact,
returnData = TRUE
)
tooltips <- paste0(
"<b>Sample:</b> ", rownames(pca), "<br />",
"<b>PC1:</b> ", round(pca$PC1, 3), "<br />",
"<b>PC2:</b> ", round(pca$PC2, 3)
)
plot_ly(
data = pca,
type = "scatter",
mode = "markers",
x = ~PC1,
y = ~PC2,
symbol = ~group,
marker = list(size = 9),
text = tooltips,
hoverinfo = "text"
) %>%
layout(
xaxis = list(title = pca.lab$labels$x),
yaxis = list(title = pca.lab$labels$y)
)
})
## QC - Show download button - PCA (PDF)
output$dlqcpcapdf <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqcpcapdfimg", "Download Static R Plot (PDF)")
}
})
## QC - Download plot - PCA (PDF)
output$dlqcpcapdfimg <- downloadHandler(
filename = function() {
paste("qc-pca.pdf")
},
content = function(file) {
pdf(file, width = 7, height = 6.5, onefile = FALSE) # open the pdf device
qcPCAPlot(
tmp = ddstran()[[1]],
pcafact = input$pcafact
)
dev.off()
}
)
## QC - Show download button - PCA (PNG)
output$dlqcpcapng <- renderUI({
if(input$goqc == 0) {
return()
} else {
downloadButton("dlqcpcapngimg", "Download Static R Plot (PNG)")
}
})
## QC - Download plot - PCA (PNG)
output$dlqcpcapngimg <- downloadHandler(
filename = function() {
paste("qc-pca.png")
},
content = function(file) {
png(file, width = 800, height = 750)
qcPCAPlot(
tmp = ddstran()[[1]],
pcafact = input$pcafact
)
dev.off()
}
)
## QC - header (2) - MDS
output$headmds <- renderUI({
if(input$goqc == 0) {
p(
br(),
em(
"Load data and click the 'submit' button on the 'Submit and QC' tab to see the results."
),
style = "color:grey"
)
} else {
h4("Multidimensional Scaling")
}
})
## QC - select input - choose factor - MDS
output$mdsfact <- renderUI({
tmp <- ddsout()[[2]]
selectInput(
inputId = "mdsfact",
label = "Choose factor",
choices = colnames(tmp)
)
})
## QC - visualize - MDS
output$mds <- renderPlotly({
tmp <- ddstran()[[1]]
lab <- ddstran()[[2]]
validate(
need(
expr = class(tmp) == "DESeqTransform",
message = "Please transform raw counts to visualize MDS."
)
)
mds <- dist(t(assay(tmp)))
mds <- as.matrix(mds)
mds <- as.data.frame(colData(tmp)) %>%
cbind(cmdscale(mds))
tooltips <- paste0(
"<b>Sample:</b> ", rownames(mds), "<br />",
"<b>Coord. 1:</b> ", round(mds[, "1"], 3), "<br />",
"<b>Coord. 2:</b> ", round(mds[, "2"], 3)
)
plot_ly(
data = mds,
type = "scatter",
mode = "markers",
x = mds[, "1"],
y = mds[, "2"],
symbol = mds[, input$mdsfact],
marker = list(size = 9),
text = tooltips,
hoverinfo = "text"
) %>%
layout(
xaxis = list(title = "MDS coordinate 1"),