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app.R
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app.R
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library(shiny)
library(httr)
library(rjson)
library(DT)
library(plotly)
# profvis::profvis({ runApp() })
DEBUG <- Sys.getenv('DEBUG') == 'TRUE'
OFFLINE <- FALSE
REQUIRE_LOGIN <- FALSE
# TODO
# Add GBID to the tSNE tables for easier mapping and filtering by species, etc
# ----------------------------------- App UI --------------------------------- #
# Check whether user has auth'd
has_auth_code <- function(params) {
# params is a list object containing the parsed URL parameters. Return TRUE if
# based on these parameters, it looks like auth code is present that we can
# use to get an access token. If not, it means we need to go through the OAuth
# flow.
return(!is.null(params$code))
}
# UI will change depending on whether the user has logged in
uiFunc <- function(req) {
if (OFFLINE | !REQUIRE_LOGIN){
AuthenticatedUI
} else {
if (!has_auth_code(parseQueryString(req$QUERY_STRING))) {
# Login button
AnonymousUI
} else {
# App UI
AuthenticatedUI
}
}
}
# Import UI to be shown after user before and after auth'd
source('app_ui.R')
if (!dir.exists('data')){
dir.create('data')
}
# ------------------------ Virtualenv setup -------------------------- #
if (Sys.info()[['sysname']] != 'Darwin'){
# When running on shinyapps.io, create a virtualenv
reticulate::virtualenv_create(envname = 'python35_gly_env',
python = '/usr/bin/python3')
reticulate::virtualenv_install('python35_gly_env',
packages = c('synapseclient', 'requests',
'pandas', 'numpy'))
}
reticulate::use_virtualenv('python35_gly_env', required = T)
# ---------------------------- OAuth --------------------------------- #
if (!OFFLINE){
reticulate::source_python('connect_to_synapse.py')
# Initialize Synapse client
login_to_synapse(username = Sys.getenv('SYN_USERNAME'),
api_key = Sys.getenv('SYN_API_KEY'))
logged_in <- reactiveVal(FALSE)
source('oauth.R')
}
# ----------------------------------- Server --------------------------------- #
server <- function(input, output, session) {
if (REQUIRE_LOGIN){
# Click on the 'Log in' button to kick off the OAuth round trip
observeEvent(input$action, {
session$sendCustomMessage("customredirect", oauth2.0_authorize_url(API, APP, scope = SCOPE))
return()
})
params <- parseQueryString(isolate(session$clientData$url_search))
if (!has_auth_code(params)) {
return()
}
url <- paste0(API$access, '?', 'redirect_uri=', APP_URL, '&grant_type=',
'authorization_code', '&code=', params$code)
# Get the access_token and userinfo token
token_request <- POST(url,
encode = 'form',
body = '',
authenticate(APP$key, APP$secret, type = 'basic'),
config = list()
)
stop_for_status(token_request, task = 'Get an access token')
token_response <- httr::content(token_request, type = NULL)
access_token <- token_response$access_token
id_token <- token_response$id_token
if (token_request$status_code == 201){
logged_in(T)
}
# ------------------------------ App --------------------------------- #
# Get information about the user
user_response = get_synapse_userinfo(access_token)
user_id = user_response$userid
user_content_formatted = paste(lapply(names(user_response),
function(n) paste(n, user_response[n])), collapse="\n")
# Get user profile
profile_response <- get_synapse_user_profile()
# Cache responses
if (DEBUG){
saveRDS(token_response, 'cache/token_response.rds')
saveRDS(user_response, 'cache/user_response.rds')
saveRDS(profile_response, 'cache/profile_response.rds')
}
output$userInfo <- renderText(user_content_formatted)
output$teamInfo <- renderText(teams_content_formatted)
# See in app_ui.R with verbatimTextOutput("userInfo")
# ---------------------------- Menus --------------------------------- #
# Logout modal
observeEvent(input$user_account_modal, {
showModal(
modalDialog(title = "Synapse Account Information",
h4(paste0(profile_response$firstName, ' ', profile_response$lastName)),
p(profile_response$company),
p(user_response$email, style = 'color: #00B07D;'),
easyClose = T,
footer = tagList(
actionButton("button_view_syn_profile", "View Profile on Synapse",
style = 'color: #ffffff; background-color: #00B07D; border-color: #0f9971ff;',
onclick = paste0("window.open('https://www.synapse.org/#!Profile:", profile_response$ownerId, "', '_blank')")),
modalButton("Back to Analysis")
#actionButton("button_logout", "Log Out")
)
)
)
})
output$logged_user <- renderText({
if(logged_in()){
return(paste0('Welcome, ', profile_response$firstName, '!'))
}
})
} else {
# Logout modal
observeEvent(input$user_account_modal, {
showModal(
modalDialog(title = "We appreciate your interest in GlycoBase!",
HTML('<div><strong>Additional resources</strong></div>'),
br(),
HTML('<div style="float:left;margin-right: 15px;margin-top: 23px;">
<a href="https://www.synapse.org/#!Synapse:syn21568077/wiki/600880""><img src="synapse_logo.png" title="View the Synapse project" width="200" /></a><p><a href="https://www.synapse.org/#!Synapse:syn21568077/wiki/600880" style="color: #00B07D;">View and download GlycoBase data from Synapse</a></p></div>'),
br(),
HTML('<div style="float:left;padding-bottom: 15px;padding-left: 15px;">
<a href="https://github.com/midas-wyss/glycobase"><img src="github_logo.png" title="View the code" width="100" /></a><p><a href="https://github.com/midas-wyss/glycobase" style="color: #00B07D;">View the GlycoBase code on Github</a></p></div>'),
br(),
div('For additional questions or suggestions, please email [email protected].', style="clear:left;"),
easyClose = T,
footer = tagList(
modalButton("Back to Analysis")
)
)
)
})
output$logged_user <- renderText({
paste0('Welcome, Guest!')
})
}
# end REQUIRE_LOGIN
# Citation info modal
observeEvent(input$citation_modal, {
showModal(modalDialog(
title = 'Citing GlycoBase',
p('When using GlycoBase and our glycan alignment tool in your research, please cite the following:'),
div(style = 'padding-left: 30px;',
p('D. Bojar, R.K. Powers, D.M. Camacho, J.J. Collins. SweetOrigins: Extracting Evolutionary Information from Glycans.'),
a('Read the preprint on bioRxiv (opens in a new window)', href = 'https://www.biorxiv.org/content/10.1101/2020.04.08.031948v1.full.pdf+html',
target = '_blank',
style = 'color: #00B07D;')
),
easyClose = T,
footer = NULL
))
})
# ----------------------------- TAB 1: OVERVIEW --------------------------- #
glycobaseData <- reactiveValues(glycobase_df = NULL,
monosaccharides = NULL,
species = NULL,
tsne_glycans_df = NULL,
tsne_glycoletters_df = NULL,
tsne_glycowords_df = NULL,
num_glycans = 19299,
num_glycoletters = 1027,
num_glycowords = 19866)
# Load the current version of GlycoBase to display
DATASET = PROJECT_CONFIG$DATASETS$V2
if (OFFLINE){
glycobase_csv = 'data/v2_glycobase.csv'
tsne_glycans_csv = 'data/v2_tsne_glycans_isomorph.csv'
tsne_glycoletters_csv = 'data/v2_tsne_glycoletters.csv'
tsne_glycowords_csv = 'data/v2_tsne_glycowords.csv'
} else{
glycobase_csv = fetch_synapse_filepath(DATASET$glycobase)
tsne_glycans_csv = fetch_synapse_filepath(DATASET$tsne_glycans)
tsne_glycoletters_csv = fetch_synapse_filepath(DATASET$tsne_glycoletters)
tsne_glycowords_csv = fetch_synapse_filepath(DATASET$tsne_glycowords)
}
# Load glycobase
glycobase_df <- read.csv(glycobase_csv, stringsAsFactors = F)
glycobase_df$glycan_id = paste0('GBID', glycobase_df$glycan_id)
glycobase_df$species = gsub("\\['|\\']|'", '', glycobase_df$species)
glycobase_df$species = gsub("_", ' ', glycobase_df$species)
humans = glycobase_df[grepl('Homo sapiens', glycobase_df$species), ]
others = glycobase_df[!grepl('Homo sapiens', glycobase_df$species), ]
glycobase_df = rbind(humans, others)
glycobase_df$immunogenicity[glycobase_df$immunogenicity == 0] = 'No'
glycobase_df$immunogenicity[glycobase_df$immunogenicity == 1] = 'Yes'
glycobase_df$immunogenicity[is.na(glycobase_df$immunogenicity)] = 'Unknown'
glycobase_df$link[glycobase_df$link == ''] = 'None'
glycobase_df$link[glycobase_df$link == 'free'] = 'Free'
names(glycobase_df)[c(1:4,6)] = c('GlycoBase_ID', 'Glycan', 'Species', 'Immunogenic', 'Link')
glycobaseData$glycobase_df <- glycobase_df
# Unique monosaccharides, bonds, species for filtering
glycobaseData$monosaccharides <- readRDS('rdata/v2_monosaccharides.rds')
glycobaseData$bonds <- readRDS('rdata/v2_bonds.rds')
glycobaseData$species <- readRDS('rdata/v2_species.rds')
glycobaseData$kingdoms <- readRDS('rdata/v2_kingdoms.rds')
# All data loaded
glyco_data <- reactive({ glycobaseData$glycobase_df })
tsne_glycans_data <- reactive({ glycobaseData$tsne_glycans_df })
tsne_glycowords_data <- reactive({ glycobaseData$tsne_glycowords_df })
tsne_glycoletters_data <- reactive({ glycobaseData$tsne_glycoletters_df })
n_glycans <- reactive({ glycobaseData$num_glycans })
n_glycoletters <- reactive({ glycobaseData$num_glycoletters })
n_glycowords <- reactive({ glycobaseData$num_glycowords })
monos <- reactive({ glycobaseData$monosaccharides })
bonds <- reactive({ glycobaseData$bonds })
kingdoms <- reactive({ glycobaseData$kingdoms })
specs <- reactive({ glycobaseData$species })
# Three boxes on first row
output$num_glycans <- renderText({ n_glycans() })
output$num_glycowords <- renderText({ n_glycowords() })
output$num_glycoletters <- renderText({ n_glycoletters() })
# tSNE plot for glycans
output$tsne_glycans <- renderPlotly({
# Load glycans tSNE if needed
if (is.null(glycobaseData$tsne_glycans_df)){
tsne_glycans <- read.csv(tsne_glycans_csv,
stringsAsFactors = F)
names(tsne_glycans) = c('Glycan', 'Dim1', 'Dim2')
glycobaseData$tsne_glycans_df <- tsne_glycans
}
plot_df = tsne_glycans_data()
p <- plot_ly(data = plot_df, x = ~Dim1, y = ~Dim2,
symbols = 21,
text = ~Glycan,
hovertemplate = '<b>Glycan:</b> %{text}<extra></extra>',
type = 'scatter', mode = 'markers',
showlegend = FALSE,
marker = list(size = 8,
# TODO color them by a meaningful grouping?
color = '#01B07D',
line = list(
color = '#212D32',
width = 1
)
)
) %>%
layout(title = 'Glycans (tSNE plot)')
# Display Plotly plot in UI
return(p)
})
# tSNE plot for glycowords
output$tsne_glycowords <- renderPlotly({
# Load glycowords tSNE if needed
if (is.null(glycobaseData$tsne_glycowords_df)){
tsne_glycowords = read.csv(tsne_glycowords_csv,
stringsAsFactors = F)
tsne_glycowords = tsne_glycowords[,c(3,1,2)]
names(tsne_glycowords) = c('Glycoword', 'Dim1', 'Dim2')
tsne_glycowords$Glycoword = gsub("\\(|\\)|'|,", "", tsne_glycowords$Glycoword)
glycobaseData$tsne_glycowords_df <- tsne_glycowords
}
# Load glycans tSNE if needed
if (is.null(glycobaseData$tsne_glycocans_df)){
tsne_glycans <- read.csv(tsne_glycans_csv,
stringsAsFactors = F)
names(tsne_glycans) = c('Glycan', 'Dim1', 'Dim2')
glycobaseData$tsne_glycocans_df <- tsne_glycans
}
plot_df = tsne_glycowords_data()
p <- plot_ly(data = plot_df, x = ~Dim1, y = ~Dim2,
symbols = 21,
text = ~Glycoword,
hovertemplate = '<b>Glycoword:</b> %{text}<extra></extra>',
type = 'scatter', mode = 'markers',
showlegend = FALSE,
marker = list(size = 8,
# TODO color them by a meaningful grouping?
color = '#019DB0',
line = list(
color = '#212D32',
width = 1
)
)
) %>%
layout(title = 'Glycowords (tSNE plot)')
# Display Plotly plot in UI
return(p)
})
# tSNE plot for glycowords
output$tsne_glycowords <- renderPlotly({
# Load glycowords tSNE if needed
if (is.null(glycobaseData$tsne_glycowords_df)){
tsne_glycowords = read.csv(tsne_glycowords_csv,
stringsAsFactors = F)
tsne_glycowords = tsne_glycowords[,c(3,1,2)]
names(tsne_glycowords) = c('Glycoword', 'Dim1', 'Dim2')
tsne_glycowords$Glycoword = gsub("\\(|\\)|'|,", "", tsne_glycowords$Glycoword)
glycobaseData$tsne_glycowords_df <- tsne_glycowords
}
plot_df = tsne_glycowords_data()
p <- plot_ly(data = plot_df, x = ~Dim1, y = ~Dim2,
symbols = 21,
text = ~Glycoword,
hovertemplate = '<b>Glycoword:</b> %{text}<extra></extra>',
type = 'scatter', mode = 'markers',
showlegend = FALSE,
marker = list(size = 8,
# TODO color them by a meaningful grouping?
color = '#019DB0',
line = list(
color = '#212D32',
width = 1
)
)
) %>%
layout(title = 'Glycowords (tSNE plot)')
# Display Plotly plot in UI
return(p)
})
# tSNE plot for glycoletters
output$tsne_glycoletters <- renderPlotly({
# Load glycoletters tSNE if needed
if (is.null(glycobaseData$tsne_glycoletters_df)){
tsne_glycoletters <- read.csv(tsne_glycoletters_csv,
stringsAsFactors = F)
tsne_glycoletters = tsne_glycoletters[,c(3,1,2)]
names(tsne_glycoletters) = c('Glycoletter', 'Dim1', 'Dim2')
tsne_glycoletters = tsne_glycoletters[tsne_glycoletters$Glycoletter != '',]
glycobaseData$tsne_glycoletters_df <- tsne_glycoletters
}
plot_df = tsne_glycoletters_data()
p <- plot_ly(data = plot_df, x = ~Dim1, y = ~Dim2,
symbols = 21,
text = ~Glycoletter,
hovertemplate = '<b>Glycoletter:</b> %{text}<extra></extra>',
type = 'scatter', mode = 'markers',
showlegend = FALSE,
marker = list(size = 8,
# TODO color them by a meaningful grouping?
color = '#903C83',
line = list(
color = '#212D32',
width = 1
)
)
) %>%
layout(title = 'Glycoletters (tSNE plot)')
# Display Plotly plot in UI
return(p)
})
# Glycans modal
observeEvent(input$modal_glycans, {
showModal(modalDialog(
title = 'Unique glycans in GlycoBase',
div(style='padding-left: 20px;',
p(style='color: #D2D6DD;', 'Hover over a point to view glycan')
),
div(withSpinner(plotlyOutput('tsne_glycans'), type = 4, color = '#00B07D'),
style = "overflow-y: auto;"),
selectInput('select_tsne_glycans', 'Highlight glycans containing monosaccharide:',
choices = c('All', monos()), selected = 'All', selectize = T),
easyClose = T,
footer = NULL
))
})
# Glycowords modal
observeEvent(input$modal_glycowords, {
showModal(modalDialog(
title = 'Unique glycowords in GlycoBase',
div(style='padding-left: 20px;',
p(style='color: #D2D6DD;', 'Hover over a point to view glycoword')
),
div(withSpinner(plotlyOutput('tsne_glycowords'), type = 4, color = '#019DB0'),
style = "overflow-y: auto;"),
selectInput('select_tsne_glycowords', 'Highlight glycowords containing monosaccharide:',
choices = c('All', monos()), selected = 'All', selectize = T),
easyClose = T,
footer = NULL
))
})
# Glycoletters modal
observeEvent(input$modal_glycoletters, {
showModal(modalDialog(
title = 'Unique glycoletters in GlycoBase',
div(style='padding-left: 20px;',
p(style='color: #D2D6DD;', 'Hover over a point to view glycoletter')
),
div(withSpinner(plotlyOutput('tsne_glycoletters'), type = 4, color = '#903C83'),
style = "overflow-y: auto;"),
selectInput('select_tsne_glycoletters', 'Highlight monosaccharide:',
choices = c('All', monos()), selected = 'All', selectize = T),
easyClose = T,
footer = NULL
))
})
output$button_download_glycobase <- downloadHandler(
filename = paste0("GlycoBase_v2_", gsub('-', '_', Sys.Date()), ".csv"),
content = function(file) {
if (file.exists('data/glycobase_df.csv')){
file.copy('data/glycobase_df.csv', file)
} else{
NULL
}
}
)
# Show the whole glycobase table
output$table_glycobase <- DT::renderDT({
df = glyco_data()
df = df[,c('GlycoBase_ID', 'Glycan', 'Link', 'Species', 'Immunogenic')]
df$Link = factor(df$Link, c('N', 'O', 'Free', 'None'))
df$Immunogenic = factor(df$Immunogenic, c('Yes', 'No', 'Unknown'))
df$Species = factor(df$Species, specs())
write.table(df, 'data/glycobase_df.csv', sep = ',', row.names = F)
if (!is.null(df)){
# Display
return(datatable(df, rownames = F, selection = 'none',
style = 'bootstrap', escape = F,
filter = 'top',
options = list(
dom = 'tipr',
pageLength = 20,
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(0,2,4)),
list(width = '200px', targets = 3))
)))
} else{
return(NULL)
}
})
# ---------------------- TAB 2: STRUCTURAL CONTEXT ------------------------ #
reticulate::source_python('structural_context.py')
context_query_criteria <- reactive({ input$select_context_criteria })
selected_context_glycoletter <- reactive({ input$select_context_glycoletter })
context_tax_level <- reactive({ input$select_context_taxonomy_level })
selected_tax_value <- reactive({ input$select_context_taxonomy_value })
observeEvent(input$info_environment_modal, {
showModal(
modalDialog(title = "Analyzing the local structural context of glycoletters",
p('This tab highlights the characteristic local structural context of a glycoletter (monosaccharide or bond). It also shows its frequency by position in the glycan structure (main versus side branch).'),
p('For all glycans in our database with species information, we constructed a library of disaccharide motifs that are present in any species to generate leads for glycosyltransferase biomining.'),
a('Read the full methods in our preprint (opens in a new window)', href='https://www.biorxiv.org/content/10.1101/2020.04.08.031948v1.full.pdf+html',
style='color: #00B07D;', target='_blank'),
easyClose = T,
footer = NULL)
)
})
observeEvent(context_query_criteria(), {
criteria = context_query_criteria()
if (criteria == 'Observed monosaccharides making bond:'){
updateSelectInput(session, 'select_context_glycoletter',
choices = bonds())
} else{
updateSelectInput(session, 'select_context_glycoletter',
choices = monos(), selected = 'Rha')
}
})
observeEvent(context_tax_level(), {
tax_level = context_tax_level()
if (tax_level == 'Kingdom'){
updateSelectInput(session, 'select_context_taxonomy_value',
label = 'Kingdom',
choices = c('All', kingdoms()))
} else{
updateSelectInput(session, 'select_context_taxonomy_value',
label = 'Species',
choices = c('All', specs()))
}
})
output$context_observed_barplot <- renderPlotly({
motif = selected_context_glycoletter()
criteria = context_query_criteria()
if (criteria == 'Observed monosaccharides making bond:'){
mode = 'bond'
} else if (criteria == 'Observed monosaccharides paired with:'){
mode = 'sugar'
} else{
mode = 'sugarbond'
}
context_results = characterize_context(motif, mode = mode,
taxonomy_filter=context_tax_level(), taxonomy_value = selected_tax_value())
plot_title = context_results[[1]]
plot_x = factor(context_results[[2]], levels = context_results[[2]]) # keep the order that characterize_context() returns
plot_y = context_results[[3]]
plot_ly(x = plot_x, y = plot_y, type = 'bar', marker = list(color = '#00B07D')) %>%
layout(title = plot_title,
xaxis = list(title = 'Monosaccharide'),
yaxis = list(title = 'Number of glycans'))
})
output$context_main_side_barplot <- renderPlotly({
motif = selected_context_glycoletter()
main_side = main_v_side_branch(motif, taxonomy_filter=context_tax_level(), taxonomy_value = selected_tax_value())
plot_title = motif
plot_x = c('Main branch', 'Side branch')
plot_y = main_side
plot_ly(x = plot_x, y = plot_y, type = 'bar',
marker = list(color = c('#019DB0', '#903C83'))) %>%
layout(title = plot_title,
xaxis = list(title = 'Position'),
yaxis = list(title = 'Ocurrence'))
})
# ---------------------- TAB 3: GLYCAN ALIGNMENT ------------------------ #
reticulate::source_python('glycan_alignment.py')
alignmentData <- reactiveValues(alignment_message = NULL,
input_valid = NULL,
alignment_df = NULL)
glycan_query_seq <- reactive({ input$glycan_query })
validated_query_seq <- reactive({ alignmentData$input_valid })
observeEvent(input$info_alignment_modal, {
showModal(
modalDialog(title = "Glycan alignment",
p('A common method of analyzing motifs in biological sequences that capitalizes on evolutionary information is the use
of alignments. This tab on GlycoBase performs gapped, pairwise alignments of glycan sequences, assisted
by a substitution matrix analogous to the BLOSUM matrices utilized in protein alignments (which we termed GLYcan SUbstitution Matrix, GLYSUM).'),
HTML('<p>Global sequence alignment of glycans was implemented according to the Needleman Wunsch algorithm by adapting
the <a href="https://github.com/eseraygun/pythonalignment" style="color: #00B07D;">Python Alignment library</a>.</p>'),
br(),
HTML('<strong>Scoring with the GLYcan SUbstitution Matrix (GLYSUM)</strong>'),
HTML('<p>The exhaustive list of in silico modifications resulting in glycans with observed glycowords was generated (n
= 1,238,879). All thereby observed monosaccharide and/or bond substitutions were recorded in a
symmetric matrix and converted into substitution frequencies by dividing them by the total number of
retained modifications.</p>'),
HTML('<p>Substitutions never observed during this procedure received a final value of -5, lower than any of the observed substitution scores,
while the diagonal values of the substitution matrix re set at 5, higher than any of the observed substitution scores.
The penalty for gaps for alignments in this work was set at -5, to match the minimal substitution score. The penalty for mismatches was -10.</p>'),
a('Read the full methods in our preprint (opens in a new window)', href='https://www.biorxiv.org/content/10.1101/2020.04.08.031948v1.full.pdf+html',
style='color: #00B07D;', target='_blank'),
easyClose = T,
footer = NULL)
)
})
observeEvent(input$button_run_alignment, {
alignmentData$alignment_df <- NULL
query = glycan_query_seq()
if (!is.null(query)){
if (!grepl('\\(', query)){
alignmentData$alignment_message <- 'Please use the IUPAC condensed nomenclature shown on GlycoBase.'
} else{
alignmentData$alignment_message <- ''
alignmentData$input_valid <- query
}
}
})
output$message_run_alignment <- renderText({
return(alignmentData$alignment_message)
})
observeEvent(input$button_show_alignment_example, {
query = 'ManNAcA(b1-4)FucNAcOAc(a1-3)D-FucNAc(b1-4)ManNAcA'
updateTextInput(session, 'glycan_query', value = query)
})
observeEvent(validated_query_seq(), {
withProgress(message = 'Aligning...', value = 0, {
incProgress(0.2, detail = '(1-2 minutes)')
query = validated_query_seq()
incProgress(0.3, detail = '(1-2 minutes)')
if (!is.null(query)){
alignment_df = pairwiseAlign(query, n=0) # n=0 returns all
alignment_df$Species = unlist(lapply(alignment_df$Species, function(x){
gsub("\\['|'\\]|'", "", x)
}))
incProgress(0.4, detail = 'Finishing up')
if (!is.null(alignment_df)){
df = as.data.frame(alignment_df)
df$Species = gsub("_", ' ', df$Species)
df = unique(df)
df = df[,c('Query_Sequence', 'Aligned_Sequence', 'Score',
'Species', 'Percent_Identity', 'Percent_Coverage', 'Glycobase_ID')]
alignmentData$alignment_df <- df
# Save csv
write.table(df, 'data/alignment_df.csv', sep = ',',
row.names = F)
} else{
alignmentData$alignment_df <- NULL
}
} else{
alignmentData$alignment_df <- NULL
}
})
})
alignment_table <- reactive({ alignmentData$alignment_df })
output$button_download_alignments <- downloadHandler(
filename = paste0("GlycoBase_glycan_pairwise_alignment_", gsub('-', '_', Sys.Date()), ".csv"),
content = function(file) {
if (file.exists('data/alignment_df.csv')){
file.copy('data/alignment_df.csv', file)
} else{
NULL
}
}
)
USE_PRECOMPUTED_ALIGNMENT = FALSE
output$alignments_ui <- renderUI({
if (USE_PRECOMPUTED_ALIGNMENT){
dat = readRDS('cache/dat.rds')
} else{
dat = alignment_table()
}
if (!is.null(dat)){
n = nrow(dat)
return(tagList(
div(downloadButton('button_download_alignments', label = 'Download all (csv)', style = 'margin-right: 15px; float: right;', icon = icon('download')),
HTML('<h2>Pairwise alignment results <span style="font-size: 10pt;">(Top 5 shown)</span></h2>'),
style = 'padding-left: 15px;'),
lapply(1:5, function(i) {
a = sapply(strsplit(as.character(dat[i, 'Query_Sequence']), ' ')[[1]], function(s) {
if (s != ''){
paste0('<span class="alignment_item">', s, '</span>')
}
})
b = sapply(strsplit(as.character(dat[i, 'Aligned_Sequence']), ' ')[[1]], function(s) {
if (s != ''){
paste0('<span class="alignment_item">', s, '</span>')
}
})
gbid = as.character(dat[i, 'Glycobase_ID'])
species = as.character(dat[i, 'Species'])
score = as.character(dat[i, 'Score'])
percent_id = as.character(round(dat[i, 'Percent_Identity'], 3))
percent_coverage = as.character(round(dat[i, 'Percent_Coverage'], 3))
box(title = paste0('Alignment #', i, ': ', gbid),
width = 12,
div(style = 'padding-left: 15px; padding-bottom: 15px;',
h4({ paste0('Score: ', score) }, style='color: #00B07D'),
p({ paste0('Percent identity: ', percent_id) }),
p({ paste0('Percent coverage: ', percent_coverage) }),
p(HTML({ paste0('Species: <i>', species, '</i>') })),
br(),
strong('Alignment: '),
div(HTML(a), class = 'alignment_row1'),
div(HTML(b), class = 'alignment_row2')
)
)
})
))
}
})
}
# uiFunc instead of ui
shinyApp(uiFunc, server)