inst/application/old/app6.R
In mjmccoy/REVAvis: What the Package Does (One Line, Title Case)
#
# This is a Shiny web application that visualizes REVA output. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# Load libraries
library(shiny)
library(ggplot2)
library(dplyr)
library(plotly)
# library(DT) # overwrites shiny's renderDataTable and dataTableOutput to work properly
# Load functions
source(file = "../../R/read.input.files.R")
# Load REVA output data. Currently, there are issues with the input file.
# For example, some of the columns have extra quotation marks that throw that row out of wack.
# data.df <- read.table(file = "../extdata/FeatureSummary_REVA_CATALLNavy_allHuman38_unOrdered_random_EBV_NsReplacedWithA_D_12_02_19_T_13_55_12.unix.tdv", sep = c("\t"), skip = 53, header = T, fill = T, quote = c(""))
# names(data.df) <- gsub(pattern = "\\__.*", "", names(data.df))
# data.df <- data.df[,!(names(data.df) %in% "GFF_ColumnUnlabeled")]
# Define UI for application
ui <- navbarPage("REVA Visualization",
tabPanel("Data Input",
fluidRow(
# Input Condition 1 files:
column(4, wellPanel(
fileInput(
inputId = "condition1_files",
label = "Choose REVA Output Files for Condition 1",
multiple = TRUE,
accept = c(".tdv")
),
textInput(
inputId = "condition1_name",
label = "Name for Condition 1",
value = "Condition1"),
checkboxInput("condition1_norm", "RPM normalize", TRUE)
)),
# Output Condition 1 file names:
column(4,
tableOutput("condition1_files")
)
),
fluidRow(
# Input Condition 2 files:
column(4, wellPanel(
fileInput(
inputId = "condition2_files",
label = "Choose REVA Output Files for Condition 2",
multiple = TRUE,
accept = c(".tdv")
),
textInput(
inputId = "condition2_name",
label = "Name for Condition 2",
value = "Condition2"),
checkboxInput("condition2_norm", "RPM normalize", TRUE)
)),
# Outout Condition 2 file names:
column(4,
tableOutput("condition2_files")
)
)
),
tabPanel("Individual Plots",
fluidRow(
column(2,
uiOutput("chr_ui_1"),
uiOutput("feature_ui_1"),
submitButton("Submit")
),
column(10,
plotlyOutput("condition1_chromPlot")
)
),
fluidRow(
column(2),
column(10,
plotlyOutput("condition2_chromPlot")
)
)
),
tabPanel("Aggregate Plots",
fluidRow(
column(2,
uiOutput("chr_ui_2"),
uiOutput("feature_ui_2"),
submitButton("Submit")
),
column(10,
plotlyOutput("agg_chromPlot")
)
)
),
tabPanel("Multi-Chromosomal Plots",
fluidRow(
column(2,
uiOutput("chr_ui_3"),
uiOutput("feature_ui_3"),
submitButton("Submit")
),
column(10,
plotlyOutput("condition1_multi_chromPlot")
)
),
fluidRow(
column(2),
column(10,
plotlyOutput("condition2_multi_chromPlot")
)
),
fluidRow(
column(2),
column(10,
plotlyOutput("multi_chromPlot_ratio")
)
)
)
)
# Define server logic
server <- function(input, output) {
# Limit file size
options(shiny.maxRequestSize=10000*1024^2) # ~10 Gb
###############################
## Define reactive variables ##
###############################
# condition files
condition1_data <- reactive({
req(input$condition1_files)
read.input.files(
file.list = input$condition1_files,
condition = input$condition1_name,
normalized = input$condition1_norm
)
})
condition2_data <- reactive({
req(input$condition2_files)
read.input.files(
file.list = input$condition2_files,
condition = input$condition2_name,
normalized = input$condition2_norm)
})
# chr
chr_1 <- reactive({
as.factor(input$chr_1)
})
chr_2 <- reactive({
as.factor(input$chr_2)
})
chr_3 <- reactive({
as.factor(input$chr_3)
})
# feature
feature_1 <- reactive({
input$feature_1
})
feature_2 <- reactive({
input$feature_2
})
feature_3 <- reactive({
input$feature_3
})
####################
## Define outputs ##
####################
# condition file names
output$condition1_files <- renderTable({
out.table <- input$condition1_files[['name']]
if(is.null(out.table)){
return(NULL)
}
out.table <- as.data.frame(out.table)
names(out.table) <- input$condition1_name
return(out.table)
})
output$condition2_files <- renderTable({
out.table <- input$condition2_files[['name']]
if(is.null(out.table)){
return(NULL)
}
out.table <- as.data.frame(out.table)
names(out.table) <- input$condition2_name
return(out.table)
})
# chr
output$chr_ui_1 <- renderUI({
selectInput("chr_1", "Chromosome name:",
choices = unique(as.character(condition1_data()$Chr)))
})
output$chr_ui_2 <- renderUI({
selectInput("chr_2", "Chromosome name:",
choices = unique(as.character(condition1_data()$Chr)))
})
output$chr_ui_3 <- renderUI({
selectInput("chr_3", "Chromosome name:",
multiple = T,
choices = unique(as.character(condition1_data()$Chr)))
})
# feature
output$feature_ui_1 <- renderUI({
selectInput('feature_1', 'Feature', names(condition1_data())[5:(length(names(condition1_data())) - 1)])
})
output$feature_ui_2 <- renderUI({
selectInput('feature_2', 'Feature', names(condition1_data())[5:(length(names(condition1_data())) - 1)])
})
output$feature_ui_3 <- renderUI({
selectInput('feature_3', 'Feature', names(condition1_data())[5:(length(names(condition1_data())) - 1)])
})
###########
## Plots ##
###########
# condition1_chromPlot
output$condition1_chromPlot <- renderPlotly({
if (is.null(condition1_data())){
return(NULL)
}
g1 <- subset(condition1_data(), Chr %in% chr_1()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) %>% # removes the first instance of duplicated values (summary of each chr)
ggplot(aes_string(x = "BinStart", y = feature_1(), col = "Condition")) +
geom_point() +
ylab(feature_1()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g1)
})
# condition2_chromPlot
output$condition2_chromPlot <- renderPlotly({
if (is.null(condition2_data())){
return(NULL)
}
g2 <- subset(condition2_data(), Chr %in% chr_1()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) %>% # removes the first instance of duplicated values (summary of each chr)
ggplot(aes_string(x = "BinStart", y = feature_1(), col = "Condition")) +
geom_point() +
ylab(feature_1()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g2)
})
# agg_chromPlot
output$agg_chromPlot <- renderPlotly({
if(is.null(condition1_data())){
return(NULL)
}
if(is.null(condition2_data())){
return(NULL)
}
g3 <- rbind(condition1_data(), condition2_data()) %>%
subset(Chr %in% chr_2()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) %>%
mutate(group = gsub("\\_.*", "", Condition)) %>%
ggplot(aes_string(x = "BinStart", y = feature_2(), col = "group")) +
geom_point() +
ylab(feature_2()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g3)
})
# condition1_multi_chromPlot
output$condition1_multi_chromPlot <- renderPlotly({
if (is.null(condition1_data())){
return(NULL)
}
data.df <- subset(condition1_data(), Chr %in% chr_3()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data.df <- data.df[with(data.df, order(Condition, Chr, BinStart)), ]
data.df$Chr <- factor(data.df$Chr, levels = chr_3()) # force chr order by selection
g4 <- data.df %>% ggplot(aes_string(x = "Chr", y = feature_3(), col = "Condition")) +
geom_point() +
ylab(feature_3()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g4)
})
# condition2_multi_chromPlot
output$condition2_multi_chromPlot <- renderPlotly({
if (is.null(condition2_data())){
return(NULL)
}
data.df <- subset(condition2_data(), Chr %in% chr_3()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data.df <- data.df[with(data.df, order(Condition, Chr, BinStart)), ]
data.df$Chr <- factor(data.df$Chr, levels = chr_3()) # force chr order by selection
g5 <- data.df %>% ggplot(aes_string(x = "Chr", y = feature_3(), col = "Condition")) +
geom_point() +
ylab(feature_3()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g5)
})
# multi_chromPlot_ratio
output$multi_chromPlot_ratio <- renderPlotly({
if (is.null(condition1_data())){
return(NULL)
}
if (is.null(condition2_data())){
return(NULL)
}
data.df <- condition1_data() %>% subset(Chr %in% chr_3()) %>%
select(c("Condition", "Chr", feature_3())) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data2.df <- condition2_data() %>% subset(Chr %in% chr_3()) %>%
select(c("Condition", "Chr", feature_3())) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data.df[,3] <- data.df[,3]/data2.df[,3] # calculate ratio condition1/condition2
# data.df <- data.df[with(data.df, order(Condition, Chr)), ]
data.df$Chr <- factor(data.df$Chr, levels = chr_3()) # force chr order by selection
g6 <- data.df %>% ggplot(aes_string(x = "Chr", y = feature_3(), col = "Condition")) +
stat_summary() +
ylab(paste(feature_3(), "\n", input$condition1_name, "/", input$condition2_name, sep = "")) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g6)
})
}
# Run the application
shinyApp(ui = ui, server = server)
mjmccoy/REVAvis documentation built on April 8, 2021, 8:11 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#
# This is a Shiny web application that visualizes REVA output. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# Load libraries
library(shiny)
library(ggplot2)
library(dplyr)
library(plotly)
# library(DT) # overwrites shiny's renderDataTable and dataTableOutput to work properly
# Load functions
source(file = "../../R/read.input.files.R")
# Load REVA output data. Currently, there are issues with the input file.
# For example, some of the columns have extra quotation marks that throw that row out of wack.
# data.df <- read.table(file = "../extdata/FeatureSummary_REVA_CATALLNavy_allHuman38_unOrdered_random_EBV_NsReplacedWithA_D_12_02_19_T_13_55_12.unix.tdv", sep = c("\t"), skip = 53, header = T, fill = T, quote = c(""))
# names(data.df) <- gsub(pattern = "\\__.*", "", names(data.df))
# data.df <- data.df[,!(names(data.df) %in% "GFF_ColumnUnlabeled")]
# Define UI for application
ui <- navbarPage("REVA Visualization",
tabPanel("Data Input",
fluidRow(
# Input Condition 1 files:
column(4, wellPanel(
fileInput(
inputId = "condition1_files",
label = "Choose REVA Output Files for Condition 1",
multiple = TRUE,
accept = c(".tdv")
),
textInput(
inputId = "condition1_name",
label = "Name for Condition 1",
value = "Condition1"),
checkboxInput("condition1_norm", "RPM normalize", TRUE)
)),
# Output Condition 1 file names:
column(4,
tableOutput("condition1_files")
)
),
fluidRow(
# Input Condition 2 files:
column(4, wellPanel(
fileInput(
inputId = "condition2_files",
label = "Choose REVA Output Files for Condition 2",
multiple = TRUE,
accept = c(".tdv")
),
textInput(
inputId = "condition2_name",
label = "Name for Condition 2",
value = "Condition2"),
checkboxInput("condition2_norm", "RPM normalize", TRUE)
)),
# Outout Condition 2 file names:
column(4,
tableOutput("condition2_files")
)
)
),
tabPanel("Individual Plots",
fluidRow(
column(2,
uiOutput("chr_ui_1"),
uiOutput("feature_ui_1"),
submitButton("Submit")
),
column(10,
plotlyOutput("condition1_chromPlot")
)
),
fluidRow(
column(2),
column(10,
plotlyOutput("condition2_chromPlot")
)
)
),
tabPanel("Aggregate Plots",
fluidRow(
column(2,
uiOutput("chr_ui_2"),
uiOutput("feature_ui_2"),
submitButton("Submit")
),
column(10,
plotlyOutput("agg_chromPlot")
)
)
),
tabPanel("Multi-Chromosomal Plots",
fluidRow(
column(2,
uiOutput("chr_ui_3"),
uiOutput("feature_ui_3"),
submitButton("Submit")
),
column(10,
plotlyOutput("condition1_multi_chromPlot")
)
),
fluidRow(
column(2),
column(10,
plotlyOutput("condition2_multi_chromPlot")
)
),
fluidRow(
column(2),
column(10,
plotlyOutput("multi_chromPlot_ratio")
)
)
)
)
# Define server logic
server <- function(input, output) {
# Limit file size
options(shiny.maxRequestSize=10000*1024^2) # ~10 Gb
###############################
## Define reactive variables ##
###############################
# condition files
condition1_data <- reactive({
req(input$condition1_files)
read.input.files(
file.list = input$condition1_files,
condition = input$condition1_name,
normalized = input$condition1_norm
)
})
condition2_data <- reactive({
req(input$condition2_files)
read.input.files(
file.list = input$condition2_files,
condition = input$condition2_name,
normalized = input$condition2_norm)
})
# chr
chr_1 <- reactive({
as.factor(input$chr_1)
})
chr_2 <- reactive({
as.factor(input$chr_2)
})
chr_3 <- reactive({
as.factor(input$chr_3)
})
# feature
feature_1 <- reactive({
input$feature_1
})
feature_2 <- reactive({
input$feature_2
})
feature_3 <- reactive({
input$feature_3
})
####################
## Define outputs ##
####################
# condition file names
output$condition1_files <- renderTable({
out.table <- input$condition1_files[['name']]
if(is.null(out.table)){
return(NULL)
}
out.table <- as.data.frame(out.table)
names(out.table) <- input$condition1_name
return(out.table)
})
output$condition2_files <- renderTable({
out.table <- input$condition2_files[['name']]
if(is.null(out.table)){
return(NULL)
}
out.table <- as.data.frame(out.table)
names(out.table) <- input$condition2_name
return(out.table)
})
# chr
output$chr_ui_1 <- renderUI({
selectInput("chr_1", "Chromosome name:",
choices = unique(as.character(condition1_data()$Chr)))
})
output$chr_ui_2 <- renderUI({
selectInput("chr_2", "Chromosome name:",
choices = unique(as.character(condition1_data()$Chr)))
})
output$chr_ui_3 <- renderUI({
selectInput("chr_3", "Chromosome name:",
multiple = T,
choices = unique(as.character(condition1_data()$Chr)))
})
# feature
output$feature_ui_1 <- renderUI({
selectInput('feature_1', 'Feature', names(condition1_data())[5:(length(names(condition1_data())) - 1)])
})
output$feature_ui_2 <- renderUI({
selectInput('feature_2', 'Feature', names(condition1_data())[5:(length(names(condition1_data())) - 1)])
})
output$feature_ui_3 <- renderUI({
selectInput('feature_3', 'Feature', names(condition1_data())[5:(length(names(condition1_data())) - 1)])
})
###########
## Plots ##
###########
# condition1_chromPlot
output$condition1_chromPlot <- renderPlotly({
if (is.null(condition1_data())){
return(NULL)
}
g1 <- subset(condition1_data(), Chr %in% chr_1()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) %>% # removes the first instance of duplicated values (summary of each chr)
ggplot(aes_string(x = "BinStart", y = feature_1(), col = "Condition")) +
geom_point() +
ylab(feature_1()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g1)
})
# condition2_chromPlot
output$condition2_chromPlot <- renderPlotly({
if (is.null(condition2_data())){
return(NULL)
}
g2 <- subset(condition2_data(), Chr %in% chr_1()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) %>% # removes the first instance of duplicated values (summary of each chr)
ggplot(aes_string(x = "BinStart", y = feature_1(), col = "Condition")) +
geom_point() +
ylab(feature_1()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g2)
})
# agg_chromPlot
output$agg_chromPlot <- renderPlotly({
if(is.null(condition1_data())){
return(NULL)
}
if(is.null(condition2_data())){
return(NULL)
}
g3 <- rbind(condition1_data(), condition2_data()) %>%
subset(Chr %in% chr_2()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) %>%
mutate(group = gsub("\\_.*", "", Condition)) %>%
ggplot(aes_string(x = "BinStart", y = feature_2(), col = "group")) +
geom_point() +
ylab(feature_2()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g3)
})
# condition1_multi_chromPlot
output$condition1_multi_chromPlot <- renderPlotly({
if (is.null(condition1_data())){
return(NULL)
}
data.df <- subset(condition1_data(), Chr %in% chr_3()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data.df <- data.df[with(data.df, order(Condition, Chr, BinStart)), ]
data.df$Chr <- factor(data.df$Chr, levels = chr_3()) # force chr order by selection
g4 <- data.df %>% ggplot(aes_string(x = "Chr", y = feature_3(), col = "Condition")) +
geom_point() +
ylab(feature_3()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g4)
})
# condition2_multi_chromPlot
output$condition2_multi_chromPlot <- renderPlotly({
if (is.null(condition2_data())){
return(NULL)
}
data.df <- subset(condition2_data(), Chr %in% chr_3()) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data.df <- data.df[with(data.df, order(Condition, Chr, BinStart)), ]
data.df$Chr <- factor(data.df$Chr, levels = chr_3()) # force chr order by selection
g5 <- data.df %>% ggplot(aes_string(x = "Chr", y = feature_3(), col = "Condition")) +
geom_point() +
ylab(feature_3()) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g5)
})
# multi_chromPlot_ratio
output$multi_chromPlot_ratio <- renderPlotly({
if (is.null(condition1_data())){
return(NULL)
}
if (is.null(condition2_data())){
return(NULL)
}
data.df <- condition1_data() %>% subset(Chr %in% chr_3()) %>%
select(c("Condition", "Chr", feature_3())) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data2.df <- condition2_data() %>% subset(Chr %in% chr_3()) %>%
select(c("Condition", "Chr", feature_3())) %>%
group_by(Condition, Chr) %>%
filter(duplicated(Chr) | n()==1) # removes the first instance of duplicated values (summary of each chr)
data.df[,3] <- data.df[,3]/data2.df[,3] # calculate ratio condition1/condition2
# data.df <- data.df[with(data.df, order(Condition, Chr)), ]
data.df$Chr <- factor(data.df$Chr, levels = chr_3()) # force chr order by selection
g6 <- data.df %>% ggplot(aes_string(x = "Chr", y = feature_3(), col = "Condition")) +
stat_summary() +
ylab(paste(feature_3(), "\n", input$condition1_name, "/", input$condition2_name, sep = "")) +
theme(panel.border = element_rect(colour = "black", fill = NA, size = 1)) +
geom_hline(yintercept = 0)
ggplotly(g6)
})
}
# Run the application
shinyApp(ui = ui, server = server)
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