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Bring in changes from RSE fork #1

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33,986 changes: 33,986 additions & 0 deletions data/csv/metaphewas_model1a_extracted.csv
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33,775 changes: 33,775 additions & 0 deletions data/csv/metaphewas_model1b_extracted.csv
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9 changes: 9 additions & 0 deletions rse_app/README.md
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## EPoCH results R Shiny app

To run the app launch an R shell from the top level repo directory and invoke the following commands:

```
> library(shiny)
> runApp("results_app")
```

183 changes: 183 additions & 0 deletions rse_app/app.R
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# Load required packages -------------------------------
library(shiny)
library(shinyjs)
library(shinycssloaders)
library(shinyTree)
library(tidyverse)
library(plotly)
library(bslib)

source("data.R")
source("plot.R")

addResourcePath(prefix="img", directoryPath = "./img")

ui <- function(request) {
fluidPage(title = "EPoCH data analysis tool", id = 'epoch',
theme = bs_theme(version = 4, bootswatch = "minty"),#, heading_font = font_google("Segoe UI")),
hr(),
# User Input Section -------------------------------
sidebarLayout(
sidebarPanel( width = 3,
# Java scriptlet to get window dimensions
tags$head(tags$script('
var dimension = [0, 0];
$(document).on("shiny:connected", function(e) {
dimension[0] = window.innerWidth;
dimension[1] = window.innerHeight;
Shiny.onInputChange("dimension", dimension);
});
$(window).resize(function(e) {
dimension[0] = window.innerWidth;
dimension[1] = window.innerHeight;
Shiny.onInputChange("dimension", dimension);
});
')),
# Logo
tags$img(src="img/app_logo.png",
width = 185, height = 60, label = tags$h4("logo")),
hr(),
actionButton("load_results","Click to load results"),
hr(),
selectizeInput(inputId = "exposure_choice",
label = tags$h4("Exposure type:"),
choices = NULL,
selected = NULL,
multiple = T,
options = list(placeholder = '----------', maxItems = 1)),
selectizeInput(inputId = "outcome_choice",
label = tags$h4("Outcome:"),
choices = NULL,
selected = NULL,
multiple = T,
width = "1200px",
options = list(placeholder = '----------', maxItems = 1)),
selectizeInput(inputId = "model_choice",
label = tags$h4("Model:"),
choices = NULL,
selected = NULL,
multiple = T,
options = list(placeholder = '----------', maxItems = 1)),
hr(),
actionButton("plot_data","Visualise data"),
hr(),
p(HTML("<p>The Exploring Prenatal influences on Childhood Health, or EPoCH, project investigates how parents’
lifestyles in the important prenatal period might affect the health of their children. Please visit
the <a href='https://epoch.blogs.bristol.ac.uk'>EPoCH website</a> for more information. </p>"),
style = "font-size: 85%")
),
# Output of Plot, Data, and Summary -------------------------------
mainPanel( width = 9,
tabsetPanel(id = 'main_tabs',
tabPanel("Plot by exposure", icon = icon("chart-simple"),
tabsetPanel(
tabPanel("Manhattan", icon = icon("chart-simple"),
textOutput('Text1'),
plotlyOutput("exposureManhattanPlot", height="100%")
),
tabPanel("Volcano",
plotlyOutput("exposureVolcanoPlot")
)
)
),
tabPanel("Plot by Outcome", icon = icon("chart-simple"),
tabsetPanel(
tabPanel("Manhattan",
textOutput('Text2'),
plotlyOutput("outcomeManhattanPlot")
),
tabPanel("Volcano",
plotlyOutput("outcomeVolcanoPlot")
)
)
),
tabPanel("Plot by exposure coefficient", icon = icon("chart-simple"),
tabsetPanel(
tabPanel("Select comparisons",
inputPanel(
selectizeInput(inputId = "coeff_person",
label = tags$h4("Person exposed:"),
choices = NULL,
selected = NULL,
options = list(placeholder = '----------', maxItems = 1)),
selectizeInput(inputId = "coeff_subclass",
label = tags$h4("Exposure level:"),
choices = NULL,
selected = NULL,
options = list(placeholder = '----------', maxItems = 1)),
selectizeInput(inputId = "coeff_exptime",
label = tags$h4("Exposure time:"),
choices = NULL,
selected = NULL,
options = list(placeholder = '----------', maxItems = 1))),
tags$div(
selectInput(width = "80%", inputId = "coeff_explink",
label = tags$h4("Exposure linker:"),
choices = NULL,
selected = NULL,
multiple = F)),
actionButton("add_comp","Add comparison"),
hr(),
uiOutput("showActiveLinkers"),
actionButton("clear_comps","Clear comparisons")),
tabPanel("Plots", icon = icon("chart-simple"),
plotlyOutput("exposureCoeffPlot"))
)),
tabPanel("Forest plots", icon = icon("chart-simple"),
tabsetPanel(
tabPanel("Select comparisons",
inputPanel(selectizeInput(inputId = "forest_person",
label = tags$h4("Person exposed:"),
choices = NULL,
selected = NULL,
options = list(placeholder = '----------', maxItems = 1)),
selectizeInput(inputId = "forest_subclass",
label = tags$h4("Exposure level:"),
choices = NULL,
selected = NULL,
options = list(placeholder = '----------', maxItems = 1)),
selectizeInput(inputId = "forest_exptime",
label = tags$h4("Exposure time:"),
choices = NULL,
selected = NULL,
options = list(placeholder = '----------', maxItems = 1)),
selectizeInput(inputId = "forest_outcometype",
label = tags$h4("Outcome type:"),
choices = NULL,
selected = NULL,
options = list(placeholder = '----------', maxItems = 1))
),
tags$div(
selectInput(width = "80%", inputId = "forest_explink",
label = tags$h4("Exposure linker:"),
choices = NULL,
selected = NULL,
multiple = F),
selectInput(width = "80%", inputId = "forest_outlink",
label = tags$h4("Outcome linker:"),
choices = NULL,
selected = NULL,
multiple = F))),
tabPanel("Plots", icon = icon("chart-simple"),plotlyOutput("forestPlot"),
hr(),
uiOutput("showForestLinkers")),
)
)
)
)
)
)
}

server <- function(input, output, session) {

global_data <- reactiveValues(data = NULL, data_is_loaded = FALSE,
coeff_linkers = NULL)

source("data_server.R",local=T)$value
source("plot_server.R",local=T)$value
}

# Load UI and server controls
shinyApp(ui = ui, server = server)
142 changes: 142 additions & 0 deletions rse_app/data.R
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library(shiny)
library(tidyverse)
library(plotly)
library(shinycssloaders)
library(shinyjs)
library(shinyTree)
library(RCurl)


format_loaded_data <- function(global_data, loaded_data){
# a bit of tidying will happen (stick it all together, make lists of unique exposure and outcome classes, make a tibble of unique combinations, put everything in a list and name the objects)
loaded_data <- bind_rows(loaded_data)
global_data$exp_classes <- unique(loaded_data$exposure_class)
global_data$out_classes <- unique(loaded_data$outcome_class)
all_res_tib <- as_tibble(unique(loaded_data[,c("exposure_class", "exposure_subclass", "person_exposed", "exposure_time", "exposure_type", "exposure_source", "exposure_dose", "model")]))
formatted_data <- list(loaded_data,global_data$exp_classes,global_data$out_classes,all_res_tib)
names(formatted_data) <- c("all_res","exp_classes","out_classes","all_res_tib")

formatted_data
}

import_data_dropbox <- function(global_data){
dropbox_links <- c("https://www.dropbox.com/s/amagzojd4xmj66l/metaphewas_model1a_extracted.RDS?dl=1",
"https://www.dropbox.com/s/3ey11hxpqlo6i2h/metaphewas_model1b_extracted.RDS?dl=1"
)
all_res <- lapply(dropbox_links,function(x) readRDS(url(x)))
imported_data <- format_loaded_data(global_data, all_res)

return(imported_data)
}

import_data_local <- function(global_data){
file_paths <- c("../data/rds/metaphewas_model1a_extracted.RDS",
"../data/rds/metaphewas_model1b_extracted.RDS"
)
all_res <- lapply(file_paths,function(x) readRDS(x))
imported_data <- format_loaded_data(global_data, all_res)

return(imported_data)
}

create_exposure_dfs <- function(exposureclass, dat){

if (exposureclass == "all") {
df <- dat[dat$person_exposed!="child",]
} else {
df <- dat[dat$exposure_class==exposureclass&dat$person_exposed!="child",]
}

df$exposure_dose_ordered <- factor(df$exposure_dose,ordered=T,levels=c("light","moderate","heavy"))
df <- df[order(df$exposure_subclass,df$exposure_time,df$exposure_dose_ordered),]
df$exposure_subclass_time_dose <- paste(df$exposure_subclass,df$exposure_time,df$exposure_dose)
df$exposure_subclass_time_dose<-factor(df$exposure_subclass_time_dose,ordered=T,levels=unique(df$exposure_subclass_time_dose))
# to convert ln(OR) to cohen's d (SDM), multiply ln(OR) by sqrt(3)/pi (which is 0.5513)
# to convert SDM to ln(OR), multiply SDM by pi/sqrt(3) (which is 1.814)
# https://www.meta-analysis.com/downloads/Meta-analysis%20Converting%20among%20effect%20sizes.pdf
# https://onlinelibrary.wiley.com/doi/abs/10.1002/1097-0258(20001130)19:22%3C3127::AID-SIM784%3E3.0.CO;2-M
df$est_SDM <- df$est
df$est_SDM[df$outcome_type=="binary"|df$outcome_type=="ordinal"]<-df$est[df$outcome_type=="binary"|df$outcome_type=="ordinal"]*0.5513
df$se_SDM <- df$se
df$se_SDM[df$outcome_type=="binary"|df$outcome_type=="ordinal"]<-df$se[df$outcome_type=="binary"|df$outcome_type=="ordinal"]*0.5513
df
}

create_outcome_dfs <- function(outcomeclass, dat){

if (outcomeclass == "all") {
df <- dat[dat$person_exposed!="child",]
} else {
df <- dat[dat$outcome_class==outcomeclass&dat$person_exposed!="child",]
}

df$outcome_time_ordered <- factor(df$outcome_time,ordered=T,levels=c("pregnancy","delivery","first year", "age 1-2","age 3-4","age 5-7","age 8-11","anytime in childhood"))
df <- df[order(df$outcome_subclass1,df$outcome_subclass2,df$outcome_time_ordered),]
df$outcome_subclass_time <- paste(df$outcome_subclass1,df$outcome_subclass2,df$outcome_time)
df$outcome_subclass_time<-factor(df$outcome_subclass_time,ordered=T,levels=unique(df$outcome_subclass_time))
# to convert ln(OR) to cohen's d (SDM), multiply ln(OR) by sqrt(3)/pi (which is 0.5513)
# to convert SDM to ln(OR), multiply SDM by pi/sqrt(3) (which is 1.814)
# https://www.meta-analysis.com/downloads/Meta-analysis%20Converting%20among%20effect%20sizes.pdf
# https://onlinelibrary.wiley.com/doi/abs/10.1002/1097-0258(20001130)19:22%3C3127::AID-SIM784%3E3.0.CO;2-M
df$est_SDM <- df$est
df$est_SDM[df$outcome_type=="binary"|df$outcome_type=="ordinal"]<-df$est[df$outcome_type=="binary"|df$outcome_type=="ordinal"]*0.5513
df$se_SDM <- df$se
df$se_SDM[df$outcome_type=="binary"|df$outcome_type=="ordinal"]<-df$se[df$outcome_type=="binary"|df$outcome_type=="ordinal"]*0.5513
df
}

create_exposure_dfs <- function(exposureclass, dat){

if (exposureclass == "all") {
df <- dat[dat$person_exposed!="child",]
} else {
df <- dat[dat$exposure_class==exposureclass&dat$person_exposed!="child",]
}

df$exposure_dose_ordered <- factor(df$exposure_dose,ordered=T,levels=c("light","moderate","heavy"))
df <- df[order(df$exposure_subclass,df$exposure_time,df$exposure_dose_ordered),]
df$exposure_subclass_time_dose <- paste(df$exposure_subclass,df$exposure_time,df$exposure_dose)
df$exposure_subclass_time_dose<-factor(df$exposure_subclass_time_dose,ordered=T,levels=unique(df$exposure_subclass_time_dose))
# to convert ln(OR) to cohen's d (SDM), multiply ln(OR) by sqrt(3)/pi (which is 0.5513)
# to convert SDM to ln(OR), multiply SDM by pi/sqrt(3) (which is 1.814)
# https://www.meta-analysis.com/downloads/Meta-analysis%20Converting%20among%20effect%20sizes.pdf
# https://onlinelibrary.wiley.com/doi/abs/10.1002/1097-0258(20001130)19:22%3C3127::AID-SIM784%3E3.0.CO;2-M
df$est_SDM <- df$est
df$est_SDM[df$outcome_type=="binary"|df$outcome_type=="ordinal"]<-df$est[df$outcome_type=="binary"|df$outcome_type=="ordinal"]*0.5513
df$se_SDM <- df$se
df$se_SDM[df$outcome_type=="binary"|df$outcome_type=="ordinal"]<-df$se[df$outcome_type=="binary"|df$outcome_type=="ordinal"]*0.5513
df
}

create_forest_dfs <- function(dat, exp_linker, out_linker){
extracted_res <- dat[which(dat$exposure_linker==exp_linker &
dat$outcome_linker==out_linker),]

# which cohorts contributed to the meta-analysis
cohorts <- unlist(strsplit(extracted_res$cohorts,split=","))
cohorts <-str_remove(cohorts," ")

#prepare data:
df <- data.frame(exposure=rep(extracted_res$exposure_linker, length(cohorts)+1),
outcome=rep(extracted_res$outcome_linker, length(cohorts)+1),
cohort=c("meta",cohorts),
binary=c(extracted_res$outcome_type=="binary"),
est=c(extracted_res$est,#meta-analysis estimate
unlist(extracted_res[,paste0("est_",cohorts)])), #estimates for each cohort
se=c(extracted_res$se,#meta-analysis standard errror
unlist(extracted_res[,paste0("se_",cohorts)])), #standard errors for each cohort
n=c(extracted_res$total_n,#meta-analysis sample size
unlist(extracted_res[,paste0("n_",cohorts)])), #sample size for each cohort
P=c(extracted_res$p,#meta-analysis P-value
unlist(extracted_res[,paste0("p_",cohorts)])) #P-value for each cohort
)
df$lci <- df$est-(1.96*df$se) #lower 95% CI
df$uci <- df$est+(1.96*df$se) #upper 95% CI

df$cohort<-factor(df$cohort,ordered=T,levels=c("meta",sort(cohorts,decreasing = T))) #ordering so that meta-analysis statistic comes bottom
df$point_size <- c(1, #size 1 for meta-analysis results
sqrt(sqrt(1/df$se[-1]/100)) #then all other cohorts will have points sized smaller than 1, but proportionate to their contribution to the meta-analysis (i.e. the inverse of the variance)

)
df
}
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