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ui.R
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ui.R
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library(shiny)
library(shinyWidgets)
library(shinyjs)
library(bslib)
library(markdown)
ui <- shinyUI(navbarPage(theme = bs_theme(version = 4, bootswatch = "flatly"), #Set a theme. More can be selected at bootswatch.com
"IMPACC Proteomics Analysis", #Set the Title
#make first Tab. Select the dataset (DDA, DIA, Targeted)
tabPanel("Select-Dataset", #####
#Sidebar Items
sidebarLayout(
sidebarPanel(
#Select the Dataset
radioButtons("DataSelect", "Select Dataset",
c("DDA"= "DDA_Data",
"DIA"= "DIA_Data",
"Targeted" = "Targeted_Data")),
#Select If URS samples should be removed
radioButtons("URSFilter", "Remove URS Samples?",
c("Yes" = TRUE,
"No" = FALSE)
),
#select for Emory
radioButtons("EmoryFilter", "Remove Emory Samples?",
c("Yes" = TRUE,
"No" = FALSE)
),
#select for Non COVID samples
radioButtons("NonPositive", "Remove SARS-CoV-2 Negative?",
c("Yes" = TRUE,
"No" = FALSE)
),
#select normalisation (only for DDA its nice)
selectInput("Normalization", "Choose Normalisation (DDA)",
choices = list("None" = 1, "Median" = 2, "VSN" = 3),
selected = 1),
#cut off filter (50% is standard)
sliderInput("CutOffFilter", "Cut Off Filter (% NA)",
min = 2, max = 99, value = 50),
),
#Main Panel
mainPanel(
tabsetPanel(type = "tabs",
#Panel 1, Data selection info
tabPanel("Dataset Info",
plotOutput("DataSelectionPlot")
),
# #Panel 2, Plot XXXXXXX
# tabPanel("XXXXX",
# plotOutput("SampleBarPlot")
# ),
tabPanel("Venny (DDA vs. DIA vs. Targeted)",
plotOutput("VennyThreeMethods")
)
)
)
)
),
tabPanel("Clinical Info", #####
#Add a sidebar
sidebarLayout(
sidebarPanel(
sliderInput("HistBins", "Number of Bins for Histogram",
min = 5, max = 50, value = 25)
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Table Info",
htmlOutput("BasicClinicalInfo"),
),
tabPanel("Clinical group: day 0 & 14",
plotOutput("dayonetofourteen", height = "600px")
),
tabPanel("day 0 flow to day 14",
plotOutput("alluvianPlot")
),
tabPanel("Number of Samples per visit",
plotOutput("ClinicalPlot_Bar")
),
tabPanel("Age Distribution (Histogram)",
plotOutput("AgeHistogram")
),
tabPanel("Sex",
plotOutput("ClinicalSex")
),
tabPanel("Enrollment Sites",
plotOutput("SampleLocation")
),
tabPanel("Ethnicity",
plotOutput("ClinicalRace"))
)
)
)
),
tabPanel("Clustering", #####
sidebarLayout(
sidebarPanel(
selectInput("Scaler", "Should Data be Scaled?",
choices = list("Yes" = TRUE, "No" = FALSE),
selected = TRUE),
selectInput("Imputation", "Choose an imputation Method",
choices = list("Half Min Value" = 1, "Replace with 0" = 2),
selected = 1),
selectInput("Colouring", "What Clinical Factor Should be Coloured?",
choices = list("Visit Numbers" = "event_type",
"Respiratory Status (day 0)" = "respiratory_status",
"Respiratory Status (day 14)" = "respiratory_status_day14",
"Enrollment Site" = "enrollment_site",
"Age" = "admit_age",
"Sex" = "sex",
"Race" = "race",
"Clinical Group (day 0)" = "EndpointDay0",
"Clinical Group (day 14)" = "EndpointDay14",
"Death" = "death")),
selectInput("ClusterElipse", "Add Elipse on Plot?",
choices = list("Yes" = TRUE, "No" = FALSE),
selected = "No"),
sliderInput("Perplexity", "t-SNE Perplexity",
min = 1, max = 200, value = 15),
pickerInput("pvcavariables","Select Variables for PVCA",
choices= colnames(clinical_data %>% dplyr::select(- samples_collected,
- sample_id,
- event_type,
- event_id,
- event_location,
- participant_type,
-sample_type
)),
options = list(`actions-box` = TRUE),multiple = T,
selected = colnames(clinical_data %>% dplyr::select(- samples_collected,
- sample_id,
- event_type,
- event_id,
- event_location,
- participant_type,
- sample_type,
- sex,
- pcr_date,
- EndpointDay0,
- event_date,
- ethnicity,
- symptom_date,
- EndpointDay14
)))
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("PCA",
plotOutput("PCAPlot")),
tabPanel("t-SNE",
plotOutput("tSNE")),
tabPanel("UMAP",
plotOutput("UMAP")),
tabPanel("PVCA",
plotOutput("PVCA")),
useShinyjs()
)
)
)
),
tabPanel("Longitudinal Projectories", #####
sidebarLayout(
sidebarPanel(
pickerInput("LongFilter","Select visits to include in Analysis",
choices= c("Visit 1", "Visit 2", "Visit 3", "Visit 4", "Visit 5", "Visit 6"),
options = list(`actions-box` = TRUE),multiple = T,
selected =c("Visit 1", "Visit 2", "Visit 3")),
radioButtons("AnovaType", "Type of ANOVA",
c("ANOVA" = 1,
"within-subjects ANOVA (only parametric)" = 2)),
radioButtons("AnovaParametric", "Parametric / non-parametric",
c("parametric" = 1,
"Non-Parametric" = 2)),
pickerInput("SpaghettiFacet","Select Variable for Split Spaghetti Plot",
choices= colnames(clinical_data %>% dplyr::select(- samples_collected,
- sample_id,
- event_type,
- event_id,
- event_location,
- participant_type,
-sample_type,
-pcr_date,
-admit_age,
-event_date,
-symptom_date,
-participant_id,
-enrollment_site,
-race
)), options = list(`actions-box` = TRUE),multiple = F, selected = "sex"),
pickerInput("BoxPlotProtein", "Select Protein",
choices = c(0))
#NOTE TO SELF. Find a way to easily search all from a reactive(dynamic DF).... Based on that
#We will build boxplots with post hoc tests.
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Number of Samples per Visit",
plotOutput("Longit_SamplePerVisit")
),
tabPanel("Projectories",
plotlyOutput("SpaghettiLong_all"),
plotlyOutput("Longit_volcano")
),
tabPanel("BoxPlot & Statistics",
plotOutput("BoxplotComparisons"),
DT::dataTableOutput("Longitudinal_Table")
),
tabPanel("Projectories (Split)",
plotlyOutput("SpaghettiLong_Facet", height = "750px")),
tabPanel("Enrichment Analysis",
plotOutput("EnrichmentPlot"),
DT::dataTableOutput("EnrichmentLongitudinal")),
tabPanel("GO Similarity",
plotOutput("GoPlotEnrich" , height = "600px"))
)
)
)
),
tabPanel("VolcanoPlotter", ######
sidebarLayout(
sidebarPanel(
sliderInput("Volcano_eventDate", "Filter for Event date (days)",
min = min(clinical_data$event_date), max = max(clinical_data$event_date),
value = c(min(clinical_data$event_date),max(clinical_data$event_date))),
pickerInput("Volcano_EventType","Select visits to include in Volcanoplot",
choices= c("Escalation 1", "Visit 1", "Visit 2", "Visit 3", "Visit 4", "Visit 5", "Visit 6"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Visit 1", "Visit 2", "Visit 3")),
pickerInput("Volcano_Endpoint0","Select Endpoint day 0 to include in Volcanoplot",
choices= c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)")),
pickerInput("Volcano_Endpoint14","Select Endpoint day 14 to include in Volcanoplot",
choices= c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)")),
pickerInput("Volcano_Sex","Select sex to include in Volcanoplot",
choices= c("Female", "Male"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Female", "Male")),
pickerInput("Volcano_death","Select sex to include in Volcanoplot",
choices= c("Alive" = FALSE, "Dead" = TRUE),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Alive" = FALSE, "Dead" = TRUE)),
sliderInput("Volcano_Age", "Filter for age of Participants in Volcanoplot",
min = min(clinical_data$admit_age), max = max(clinical_data$admit_age),
value = c(min(clinical_data$admit_age),max(clinical_data$admit_age))),
pickerInput("GroupCompare","Which variable do you want to Compare?",
choices= c("Visit Number" = "event_type",
"Endpoint day 0" = "EndpointDay0", "Endpoint day 14" = "EndpointDay14",
"Sex" = "sex", "Death" = "death", "Age" = "age"),
options = list(`actions-box` = TRUE),multiple = F),
pickerInput("GroupA","Select which for group A (DONT OVERLAP WITH B)",
choices= c(0),
options = list(`actions-box` = TRUE),multiple = T,
selected =c(0)),
pickerInput("GroupB","Select which for group B (DONT OVERLAP WITH A)",
choices= c(0),
options = list(`actions-box` = TRUE),multiple = T,
selected =c(0)),
sliderInput("Split_Age", "Filter for age of Participants in Volcanoplot if age is selected. Group A will include selected number",
min = min(clinical_data$admit_age), max = max(clinical_data$admit_age),
value = 65),
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Selected Number of Samples",
plotOutput("SelectedSamples_Volcano"),
),
tabPanel("VolcanoPlot",
plotlyOutput("VolcanoPlotly"),
DT::dataTableOutput("VolcanoTable")),
tabPanel("Enrichment Analysis",
plotOutput("EnrichmentPlot_Volcano"),
DT::dataTableOutput("EnrichmentVolcano")),
tabPanel("GO Similarity",
plotOutput("GoPlotEnrich_Volcano" , height = "600px"))
)
)
)
),
tabPanel("Heatmap", ######
sidebarLayout(
sidebarPanel(
sliderInput("Heatmap_eventDate", "Filter for Event date (days)",
min = min(clinical_data$event_date), max = max(clinical_data$event_date),
value = c(min(clinical_data$event_date),max(clinical_data$event_date))),
pickerInput("Heatmap_EventType","Select visits to include in Heatmap",
choices= c("Escalation 1", "Visit 1", "Visit 2", "Visit 3", "Visit 4", "Visit 5", "Visit 6"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Visit 1", "Visit 2", "Visit 3")),
pickerInput("Heatmap_Endpoint0","Select Endpoint day 0 to include in Heatmap",
choices= c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)")),
pickerInput("Heatmap_Endpoint14","Select Endpoint day 14 to include in Heatmap",
choices= c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Discharged(1-2)", "Hospitalized(3-4)", "Hospitalized(5-6)", "Dead(7)")),
pickerInput("Heatmap_Sex","Select sex to include in Heatmap",
choices= c("Female", "Male"),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Female", "Male")),
pickerInput("Heatmap_death","Select sex to include in Heatmap",
choices= c("Alive" = FALSE, "Dead" = TRUE),
options = list(`actions-box` = TRUE),multiple = T,
selected = c("Alive" = FALSE, "Dead" = TRUE)),
sliderInput("Heatmap_Age", "Filter for age of Participants in Heatmap",
min = min(clinical_data$admit_age), max = max(clinical_data$admit_age),
value = c(min(clinical_data$admit_age),max(clinical_data$admit_age))),
pickerInput("Heatmap_Grouping","Do you want to group (mean) some factors?",
choices= c("Yes" = T, "No" = F),
options = list(`actions-box` = TRUE),multiple = F,
selected = c("Yes" = TRUE)),
pickerInput("Heatmap_groupby","Which Grouping?",
choices= c("Visit Number" = "event_type",
"Endpoint day 0" = "EndpointDay0", "Endpoint day 14" = "EndpointDay14",
"Sex" = "sex", "Death" = "death"),
options = list(`actions-box` = TRUE),multiple = T)
,
pickerInput("Heatmap_Protein","Select Proteins to include in Heatmap (significant selected automatically)",
choices= c(0),
options = list(`actions-box` = TRUE),multiple = T,
selected =c(0)),
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Selected Number of Samples",
plotOutput("SelectedSamples")
),
tabPanel("HeatMap",
plotOutput("HMT", height = "750px")),
tabPanel("ValueTable",
#DT::dataTableOutput("HeatMapSuper")
)
)
)
)
),
tabPanel("", ######
sidebarLayout(
sidebarPanel(),
mainPanel(
tabsetPanel()
)
)
)
) #navbarpage
)#shinyUI