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app.R
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# load required libraries
library(shiny)
library(shinyBS)
library(DT)
library(DBI)
library(RSQLite)
library(tidyverse)
library(plotly)
library(igraph)
library(visNetwork)
# source('../www/utils.R')
source('www/utils.R')
# load lincs info
db_fl <- "www/LINPS.sqlite"
file.exists(db_fl)
# UI ----
ui <- navbarPage(title = 'LINPS',
## Main ----
tabPanel(
title = 'Main',
fluidPage(
### Input Panels ----
splitLayout(
#### Cell ----
wellPanel(
tags$h4('Cell'),
selectizeInput(
inputId = 'tissue',
label = 'Tissue',
choices = NULL,
selected = NULL,
multiple = TRUE
),
bsTooltip(placement = 'top', id = 'tissue',
title = 'Select one or more tissue to see which cell lines is available.'),
selectizeInput(
inputId = 'cell_id',
label = 'Cell Line',
choices = NULL,
selected = NULL,
multiple = TRUE
),
bsTooltip(placement = 'top', id = 'cell_id',
title = 'Select one or more cell lines to check which networks are perturbed in them.'),
# actionButton('select_cells',
# 'Select'),
actionButton('reset_cells',
'Reset'),
bsTooltip(placement = 'top', id = 'reset_cells',
title = 'Click "Reset" to go back to default selection.'),
actionButton('lucky_cells',
'Feeling lucky!'),
bsTooltip(placement = 'top', id = 'lucky_cells',
title = 'Click "Feeling lucky!" to get 3 cells selected at random.')
),
#### Network ----
wellPanel(
tags$h4('Network'),
selectizeInput(
inputId = 'family',
label = 'Family',
choices = NULL,
selected = NULL,
multiple = TRUE
),
bsTooltip(placement = 'top', id = 'family',
title = 'Select one or more families to see which networks are available.'),
selectizeInput(
inputId = 'network',
label = 'Model',
choices = NULL,
selected = NULL,
multiple = TRUE
),
bsTooltip(placement = 'top', id = 'network',
title = 'Select one or more networs to see which of them are perturbed.'),
# actionButton('select_network',
# 'Select'),
actionButton('reset_network',
'Reset'),
bsTooltip(placement = 'top', id = 'reset_network',
title = 'Click "Reset" to go back to default selection.'),
actionButton('lucky_network',
'Feeling lucky!'),
bsTooltip(placement = 'top', id = 'lucky_network',
title = 'Click "Feeling lucky!" to get one network selected at random.')
),
#### Perturbation ----
wellPanel(
tags$h4('Perturbation'),
selectizeInput(
inputId = 'pert_type',
label = 'Type',
choices = NULL,
selected = NULL,
multiple = TRUE
),
bsTooltip(placement = 'top', id = 'pert_type',
title = 'Select one type of perturbations to which perturbations are available'),
selectizeInput(
inputId = 'pert_iname',
label = 'Name',
choices = NULL,
selected = NULL,
multiple = TRUE
),
bsTooltip(placement = 'top', id = 'pert_iname',
title = 'Select one or more perturbation to see which of them perturb the networks.'),
# actionButton('select_perturb',
# 'Select'),
actionButton('reset_perturb',
'Reset'),
bsTooltip(placement = 'top', id = 'reset_perturb',
title = 'Click "Reset" to go back to default selection.'),
actionButton('lucky_perturb',
'Feeling lucky!'),
bsTooltip(placement = 'top', id = 'lucky_perturb',
title = 'Click "Feeling lucky!" to get 3 cells selected at random.')
)
),
### Output Panels ----
tabsetPanel(
#### RBIF ----
tabPanel(
title = 'RBIF',
splitLayout(
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Table) Relative imact factor of the perturbations.'))
),
tags$br(),
dataTableOutput(outputId = 'rbif'),
tags$br()
),
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Figure) Relative imact factor of the perturbations.')),
tags$br(),
actionButton(
inputId = 'rbif_back',
label = 'Back'
),
bsTooltip(
id = 'rbif_back',
title = 'Click to go back to previous cell line.'
),
actionButton(
inputId = 'rbif_next',
label = 'Next'
),
bsTooltip(
id = 'rbif_next',
title = 'Click to go to next cell line.'
),
tags$br(),
tags$br(),
textOutput(outputId = 'rbif_cell')
),
tags$br(),
plotlyOutput(outputId = 'rbif_plot'),
tags$br()
)
)
),
#### BIF ----
tabPanel(
title = 'BIF',
splitLayout(
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Table) Networks impact factor of the perturbations.'))
),
tags$br(),
dataTableOutput(outputId = 'bif'),
tags$br()
),
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Figure) Networks impact factor of the perturbations.')),
tags$br(),
actionButton(
inputId = 'bif_back',
label = 'Back'
),
bsTooltip(
id = 'bif_back',
title = 'Click to go back to previous family of networks.'
),
actionButton(
inputId = 'bif_next',
label = 'Next'
),
bsTooltip(
id = 'bif_next',
title = 'Click to go to next family of networks.'
),
tags$br(),
tags$br(),
textOutput(outputId = 'bif_family')
),
tags$br(),
plotlyOutput(outputId = 'bif_plot'),
tags$br()
)
)
),
#### NPA ----
tabPanel(
title = 'NPA',
splitLayout(
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Table) Networks perturbation amplitudes.'))
),
tags$br(),
dataTableOutput(outputId = 'npa'),
tags$br()
),
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Figure) Networks perturbation amplitudes.')),
tags$br(),
actionButton(
inputId = 'npa_back',
label = 'Back'
),
bsTooltip(
id = 'npa_back',
title = 'Click to go back to previous network.'
),
actionButton(
inputId = 'npa_next',
label = 'Next'
),
bsTooltip(
id = 'npa_next',
title = 'Click to go to next network.'
),
tags$br(),
tags$br(),
textOutput(outputId = 'npa_network')
),
tags$br(),
plotlyOutput(outputId = 'npa_plot'),
tags$br()
)
)
),
#### NODES ----
tabPanel(
title = 'NODES',
splitLayout(
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Table) Contributions of leading nodes to the perturbations.'))
),
tags$br(),
dataTableOutput(outputId = 'nodes'),
tags$br()
),
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Figure) Contributions of leading nodes to the perturbations.')),
tags$br(),
actionButton(
inputId = 'nodes_back',
label = 'Back'
),
bsTooltip(
id = 'nodes_back',
title = 'Click to go back to previous cell line.'
),
actionButton(
inputId = 'nodes_next',
label = 'Next'
),
bsTooltip(
id = 'nodes_next',
title = 'Click to go to next cell line.'
),
tags$br(),
tags$br(),
textOutput(outputId = 'nodes_cell')
),
tags$br(),
plotlyOutput(outputId = 'nodes_plot'),
tags$br()
)
)
),
#### GRAPH ----
tabPanel(
title = 'GRAPH',
sidebarLayout(
sidebarPanel(
selectizeInput(
inputId = 'overlay_pert_type',
label = 'Overlay Type',
choices = NULL,
selected = NULL
),
bsTooltip(
id = 'overlay_pert_type',
title = 'Select one perturbation type to overlay on the network.'
),
selectizeInput(
inputId = 'overlay_pert_iname',
label = 'Overlay Perturbation',
choices = NULL,
selected = NULL
),
bsTooltip(
id = 'overlay_pert_iname',
title = 'Select one perturbation to overlay on the network.'
),
selectizeInput(
inputId = 'overlay_cell_id',
label = 'Cell Line',
choices = NULL,
selected = NULL
),
bsTooltip(
id = 'overlay_cell_id',
title = 'Select one cell line to overlay on the network.'
),
checkboxInput(
inputId = 'overlay_color',
label = 'Change node colors',
value = FALSE
),
bsTooltip(
id = 'overlay_signif',
title = 'Check to show perturbed nodes in different colors.'
),
checkboxInput(
inputId = 'overlay_scale',
label = 'Change node size',
value = FALSE
),
bsTooltip(
id = 'overlay_scale',
title = 'Check to scale the node sizes by how strong they are perturbed.'
),
tags$hr(),
checkboxInput(
inputId = 'highlight_nearest',
label = 'Highlight nearest nodes'
),
bsTooltip(
id = 'highlight_nearest',
title = 'Check to highlight the nearst nodes when selecting a node.'
),
checkboxInput(
inputId = 'select_node',
label = 'Select nodes',
value = FALSE
),
bsTooltip(
id = 'select_node',
title = 'Check to add the option to select individual nodes by name.'
),
checkboxInput(
inputId = 'select_cluster',
label = 'Select modules',
value = FALSE
),
bsTooltip(
id = 'select_cluster',
title = 'Check to add the option to select modules.'
)
),
mainPanel(
verticalLayout(
tags$br(),
fluidRow(
align = 'center',
actionButton(
inputId = 'graph_back',
label = 'Back'
),
bsTooltip(
id = 'graph_back',
title = 'Click to go to next network'
),
actionButton(
inputId = 'graph_next',
label = 'Next'
),
bsTooltip(
id = 'graph_ext',
title = 'Click to go to next network.'
),
tags$br(),
tags$br(),
textOutput(outputId = 'graph_name'),
),
tags$br(),
visNetworkOutput('graph'),
tags$br()
)
)
)
),
#### SIMILARITY ----
tabPanel(
title = 'SIMILARITY',
sidebarLayout(
sidebarPanel(
tags$h4('Perturbation Similarity'),
selectInput(
inputId = 'select_pert_type',
label = 'Type',
choices = NULL,
selected = NULL
),
bsTooltip(placement = 'top', id = 'select_pert_type',
title = 'Select one type of perturbations to which perturbations are available.'),
selectizeInput(
inputId = 'select_pert_iname',
label = 'Name',
choices = NULL,
selected = NULL
),
bsTooltip(placement = 'top', id = 'select_pert_iname',
title = 'Select one perturbation to see its similarity with others.'),
numericInput(
inputId = 'select_pert_number',
label = 'Number',
value = 5,
min = 2,
max = 50,
step = 1
),
bsTooltip(placement = 'top', id = 'select_pert_number',
title = 'Select the number of perturbations to show.'),
selectInput(
inputId = 'sim_type',
'Similarity',
choices = 'SCC',
selected = 'SCC'
),
bsTooltip(placement = 'top', id = 'sim_type',
title = 'Select a measure of similarity.')
),
mainPanel(
verticalLayout(
fluidRow(
align = 'center',
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Figure) Similarity between the pertubrations.'))
),
actionButton(
inputId = 'select_pert_back',
label = 'Back'
),
bsTooltip(
id = 'select_pert_back',
title = 'Click to go to previous cell line'
),
actionButton(
inputId = 'select_pert_next',
label = 'Next'
),
bsTooltip(
id = 'select_pert_next',
title = 'Click to go to next cell_line'
),
tags$br(),
tags$br(),
textOutput(outputId = 'select_pert_text'),
tags$br(),
plotlyOutput('select_pert'),
tags$br(),
fluidRow(
align = 'center',
tags$p(strong('(Table) Similarity between the pertubrations.'))
),
tags$br(),
dataTableOutput('select_pert_tab')
)
)
)
)
# TODO: add cell line selection (similarity)
# ,
# sidebarLayout(
# sidebarPanel(
# tags$h4('Selecting Cell Lines')
# ),
# mainPanel()
# )
),
#### DOWNLAOD ----
tabPanel(
title = 'DOWNLAOD',
verticalLayout(
tags$br(),
tags$h2('Download data for selected items'),
htmlOutput('selected_info'),
tags$br(),
downloadLink(
outputId = 'down_rbif',
label = 'Relative Biological Impact Factor (RBIF)'
),
downloadLink(
outputId = 'down_bif',
label = 'Biological Impact Factor (BIF)'
),
downloadLink(
outputId = 'down_npa',
label = 'Network Perturbation Amplitudes (NPA)'
),
downloadLink(
outputId = 'down_nodes',
label = 'Node perturbations'
),
downloadLink(
outputId = 'down_graph',
label = 'Network Graph'
),
downloadLink(
outputId = 'down_sim',
label = 'Perturbations Similarity'
),
tags$hr(),
tags$h2('Download the database files'),
htmlOutput('database_info'),
tags$br(),
downloadLink(
outputId = 'down_database',
label = 'SQLite full database file'
),
downloadLink(
outputId = 'down_perturbations',
label = 'LINCS perturbations file'
),
downloadLink(
outputId = 'down_networks',
label = 'Causal Biological Networks (CBN)'
)
)
)
)
)
),
## Glossary ----
tabPanel(
title = 'Glossary',
includeMarkdown('www/pages/Glossary.md')
),
## FAQ ----
tabPanel(
title = 'FAQ',
includeMarkdown('www/pages/FAQ.md')
),
## Tutorial ----
tabPanel(
title = 'Tutorial',
includeMarkdown('www/pages/Tutorial.md')
),
## Contact ----
tabPanel(
title = 'Contact',
includeMarkdown('www/pages/Contact.md')
)
)
# server ----
server <- function(input, output, session) {
# Input panels ----
## CELL ----
tissue_choices <- reactive({
# update the tissue
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
vec <- tbl(con, 'perturbations') %>%
pull(tissue) %>%
unique()
dbDisconnect(con)
vec
})
observe({
updateSelectizeInput(
session,
inputId = 'tissue',
choices = tissue_choices(),
selected = head(tissue_choices(), 3),
server = TRUE
)
})
cell_id_choices <- reactive({
# update the cell_id based on tissue selection
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
vec <- tbl(con, 'perturbations') %>%
filter(tissue %in% local(input$tissue)) %>%
pull(cell_id) %>%
unique()
dbDisconnect(con)
vec
})
observe({
updateSelectizeInput(
session,
inputId = 'cell_id',
choices = cell_id_choices(),
selected = head(cell_id_choices(), 5),
server = TRUE
)
})
observeEvent(input$reset_cells, {
# reset tissue selection
updateSelectizeInput(
session,
inputId = 'tissue',
choices = tissue_choices(),
selected = head(tissue_choices(), 3),
server = TRUE
)
# reset cell selection
updateSelectizeInput(
session,
inputId = 'cell_id',
choices = cell_id_choices(),
selected = head(cell_id_choices(), 5),
server = TRUE
)
})
observeEvent(input$lucky_cells, {
# select 3 cells at random
updateSelectizeInput(
session,
inputId = 'cell_id',
choices = cell_id_choices(),
selected = sample(cell_id_choices(), 3),
server = TRUE
)
})
## PERTURBATION ----
pert_type_choices <- reactive({
# update the pert_type based on cell_id
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
vec <- tbl(con, 'rbif') %>%
filter(cell_id %in% local(input$cell_id)) %>%
pull(pert_type) %>%
unique()
dbDisconnect(con)
vec
})
observe({
updateSelectizeInput(
session,
inputId = 'pert_type',
choices = pert_type_choices(),
selected = head(pert_type_choices(), 3),
server = TRUE
)
})
pert_iname_choices <- reactive({
# update the pert_iname based on pert_type and cell_id
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
# TODO: use rbif to extract significant perturbations
vec <- tbl(con, 'rbif') %>%
filter(pert_type %in% local(input$pert_type),
cell_id %in% local(input$cell_id)) %>%
pull(pert_iname) %>%
unique()
dbDisconnect(con)
vec
})
observe({
updateSelectizeInput(
session,
inputId = 'pert_iname',
choices = pert_iname_choices(),
selected = head(pert_iname_choices(), 10),
server = TRUE
)
})
observeEvent(input$reset_perturb, {
# reset pert_type selection
updateSelectizeInput(
session,
inputId = 'pert_type',
choices = pert_type_choices(),
selected = head(pert_type_choices(), 3),
server = TRUE
)
# reset pert_iname selection
updateSelectizeInput(
session,
inputId = 'pert_iname',
choices = pert_iname_choices(),
selected = head(pert_iname_choices(), 10),
server = TRUE
)
})
observeEvent(input$lucky_perturb, {
# select 3 perturbations at random
updateSelectizeInput(
session,
inputId = 'pert_iname',
choices = pert_iname_choices(),
selected = sample(pert_iname_choices(), 3),
server = TRUE
)
})
## NETWORK ----
family_choices <- reactive({
# update the pert_type based on cell_id
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
vec <- tbl(con, 'models') %>%
pull(family) %>%
unique()
dbDisconnect(con)
vec
})
observe({
updateSelectizeInput(
session,
inputId = 'family',
choices = family_choices(),
selected = head(family_choices(), 3),
server = TRUE
)
})
network_choices <- reactive({
# update the pert_type based on cell_id
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
vec <- tbl(con, 'models') %>%
filter(family %in% local(input$family)) %>%
pull(model) %>%
unique()
dbDisconnect(con)
vec
})
observe({
# update the network based on family
updateSelectizeInput(
session,
inputId = 'network',
choices = network_choices(),
selected = head(network_choices(), 3),
server = TRUE
)
})
observeEvent(input$reset_network, {
# reset family selection
updateSelectInput(
session,
inputId = 'family',
choices = family_choices(),
selected = head(family_choices(), 3)
)
# reset network selection
updateSelectInput(
session,
inputId = 'network',
choices = network_choices(),
selected = head(network_choices(), 3),
)
})
observeEvent(input$lucky_network, {
# select 1 network at random
updateSelectInput(
session,
inputId = 'network',
choices = network_choices(),
selected = sample(network_choices(), 1),
)
})
# Output tabs ----
## RBIF ----
rbif <- reactive({
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
df <- tbl(con, 'rbif') %>%
filter(
cell_id %in% local(input$cell_id),
pert_type %in% local(input$pert_type),
pert_iname %in% local(input$pert_iname)
) %>%
collect() %>%
mutate_if(is.numeric, ~ round(.x, 2))
dbDisconnect(con)
df
})
output$rbif <- renderDataTable({
datatable({
rbif()
},
extensions = 'Buttons',
options = list(
paging = TRUE,
searching = TRUE,
fixedColumns = TRUE,
autoWidth = TRUE,
ordering = TRUE,
dom = 'tB',
buttons = c('copy', 'csv', 'excel')
),
class = "display")
})
wh <- reactiveValues(network = 1,
cell = 1,
cell_sim = 1,
family = 1)
observeEvent(input$rbif_next, {
wh$cell <- counter(wh$cell, length(input$cell_id))
})
observeEvent(input$rbif_back, {
wh$cell <- counter(wh$cell, direction = 'back')
})
output$rbif_cell <- renderText({
input$cell_id[wh$cell]
})
output$rbif_plot <- renderPlotly({
df <- rbif() %>%
filter(cell_id == input$cell_id[wh$cell]) %>%
mutate(pert_iname = fct_reorder(pert_iname, desc(RBIF)))
validate(need(
nrow(df) > 0,
'No significant perturbations were found for this cell line.'
))
ggplotly(
df %>%
ggplot(aes(x = pert_iname, y = RBIF)) +
geom_col() +
theme(axis.text.x = element_text(
angle = 90, hjust = 1
)) +
labs(x = '', y = 'RBIF')
)
})
## BIF ----
bif <- reactive({
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
df <- tbl(con, 'bif') %>%
filter(
cell_id %in% local(input$cell_id),
pert_type %in% local(input$pert_type),
pert_iname %in% local(input$pert_iname)
) %>%
collect() %>%
mutate_if(is.numeric, round, digits = 2)
dbDisconnect(con)
df
})
output$bif <- renderDataTable({
datatable({
bif()
},
extensions = 'Buttons',
options = list(
paging = TRUE,
searching = TRUE,
fixedColumns = TRUE,
autoWidth = TRUE,
ordering = TRUE,
dom = 'tB',
buttons = c('copy', 'csv', 'excel')
),
class = "display")
})
observeEvent(input$bif_next, {
wh$family <- counter(wh$family, length(input$family))
})
observeEvent(input$bif_back, {
wh$family <- counter(wh$family, direction = 'back')
})
output$bif_family <- renderText({
input$family[wh$family]
})
output$bif_plot <- renderPlotly({
df <- bif() %>%
pivot_longer(cols = which(sapply(., is.numeric)),
names_to = 'family',
values_to = 'BIF') %>%
filter(family == input$family[wh$family]) %>%
mutate(pert_iname = fct_reorder(pert_iname, desc(BIF)))
validate(
need(
nrow(df) > 0,
'No significant perturbations to this family were found for these cell lines.'
)
)
ggplotly(
df %>%
ggplot(aes(x = pert_iname, y = BIF)) +
geom_col(position = position_dodge()) +
theme(axis.text.x = element_text(
angle = 90, hjust = 1
)) +
facet_grid(. ~ cell_id,
scales = 'free_x',
space = 'free_x') +
labs(x = '', y = 'BIF')
)
})
## NPA ----
npa <- reactive({
con <- dbConnect(RSQLite::SQLite(), dbname = db_fl)
df <- tbl(con, 'networks') %>%
filter(
cell_id %in% local(input$cell_id),
pert_type %in% local(input$pert_type),
pert_iname %in% local(input$pert_iname),
family %in% local(input$family),
network %in% local(input$network)
) %>%
filter(coefficients > 0) %>%