shiny_app/server.R
In alb202/PACER:
#server.R
library(shiny)
library(shinyFiles)
library(shinyjs)
library(DT)
library(R.utils)
library(tidyverse)
library(GenomicAlignments)
library(biomaRt)
library(Rcpp)
library(GenomicAlignments)
library(GenomicFeatures)
library(biomaRt)
library(tidyverse)
library(data.table)
library(scales)
library(svglite)
library(quantreg)
library(ggseqlogo)
library(geometry)
library(grid)
library(gridExtra)
source("shiny_helper.R")
source("modals.R")
Rcpp::sourceCpp(file = "../cpp/cpp_functions.cpp")
source("../R/load.R")
source("../R/alignments.R")
source("../R/other.R")
source("../R/filters.R")
source("../R/make_figures.R")
source("../R/calculations.R")
source("../R/figures.R")
source("../R/sequences.R")
server <- function(input, output, session){
values <- reactiveValues()
plots <- reactiveValues()
begin <- reactiveVal()
observeEvent(eventExpr = values, once = TRUE, {
# Set the initial value of the input directory to the first entry in the volumes list
values$input_volumes <- c("Input"="../data/input","Home"="~/", "Root"="/")
values$output_volumes <- c("Output"="../data/output","Home"="~/", "Root"="/")
values$genome_volumes <- c("Genomes"="../data/genomes","Home"="~/", "Root"="/")
values$index_volumes <- c("Indexes"="../data/indexes","Home"="~/", "Root"="/")
# values$genomes <- read.delim(file = "../data/genomes/genomes_new.txt", stringsAsFactors = FALSE, header = FALSE, sep = ",", row.names= NULL, col.names = c("Description", "Version", "Dataset", "Status", "Interval.Status", "Genome.FASTA", "Bowtie.Index", "Gene.Sets"))
values$genomes_dir <- "../data/genomes/"
values$adapters_dir <- "../data/adapters/"
values$genomes_file <- "genomes.txt"
values$adapters_file <- "adapters.txt"
values$adapters <- read.delim(file = paste(values$adapters_dir, values$adapters_file, sep = ""), stringsAsFactors = FALSE, header = FALSE, sep = "#", col.names = c("Adapter", "Description"))
values$genomes <- read.delim(file = paste(values$genomes_dir, values$genomes_file, sep = ""), stringsAsFactors = FALSE, header = TRUE, sep = "\t", row.names= NULL, col.names = c("Dataset", "Description", "Version", "Status", "Interval.Status", "Genome.FASTA", "Bowtie.Index", "Gene.Sets"))
# Output directories
values$input_dir <- values$input_volumes[[1]]
values$output_dir <- values$output_volumes[[1]]
values$trim_cmd <- "cutadapt --max-n=0 -m"
values$align_cmd <- "bowtie -n2 -e1000 -l22 -k4 --best --strata -S --threads"
#values$bt_options <- c("-n2", "-e1000", "-l22", "-k4", "--best", "--strata", "-S")
})
observe({
# Only show the "Align dataset" button if a dataset has been selected
toggleState(id = "use_dataset", condition = (!is.null(input$input_file) & !is.null(values$selected_genome)))
})
observe({
# Only show the "View dataset" button if a dataset has been selected
toggleState(id = "view_dataset", condition = !is.null(input$processed_file))
})
output$input_dir <- renderText({
# Print the dataset directory
print("render directory name")
return(getAbsolutePath(values$input_dir))
})
output$output_dir <- renderText({
# Print the Output directory
print("render output directory name")
return(values$output_dir)
})
observe({
print("getting input directory")
# When the directory button is pressed, these run
shinyDirChoose(input = input,
id = "find_input_dir",
roots=isolate(values$input_volumes),
session=session
)
# Parse the result and save the output directory
if(!is.null(input$find_input_dir))
values$input_dir <- parseDirPath(roots = values$input_volumes,
selection = input$find_input_dir)
# Print the output directories
print(values$input_dir)
})
observe({
print("getting output directory")
# When the directory button is pressed, these run
shinyDirChoose(input = input,
id = "find_output_dir",
roots=isolate(values$output_volumes),
session=session
)
# Parse the result and save the output directory
if(!is.null(input$find_output_dir))
values$output_dir <- parseDirPath(roots = values$output_volumes,
selection = input$find_output_dir)
# Print the output directories
print(isolate(values$output_dir))
})
observeEvent(values$input_dir, {
# Update the file select box with the list of fastq files from the directory
print("Update the file input box")
updateSelectInput(session = session,
inputId = "input_file",
label = NULL,
choices = load_directory(dir = values$input_dir,
filetypes = c("fastq", "fq")),
selected = NULL)
})
observeEvent(values$output_dir, once = TRUE,{
# Update the file select box with the list of processed datasets
print("Update the output box")
updateSelectInput(session = session,
inputId = "processed_file",
label = NULL,
choices = load_directory(dir = values$output_dir,
filetypes = "",
include_dirs = TRUE),
selected = NULL)
# print("Sleep before making plots")
# Sys.sleep(40)
# print("Outputing the plots ...")
# for(i in plots[["plot_list"]]){
# print(i)
# output[[i]] <- renderPlot({plots[[i]]})
# }
})
observeEvent(input$use_dataset, {
print("Open the start modal")
input_file <- input$input_file
showModal(start_modal)
output$start_modal_title <- renderText({paste("File: ", input$input_file, sep = "")})
})
### Genome selector modal observers
observeEvent(label = "Select genome modal",
eventExpr = input$select_genome,
priority = 100, {
print("Select genome")
showModal(genomes_modal)
output$genome_table <- renderTable(width = "100%" ,
rownames = TRUE,
spacing = "s",
expr = {
new_table <- values$genomes
if(nrow(new_table)>0){
print("Changing values of new_table to OK")
new_table[,5:8][new_table[,5:8]!="None"] <- "OK"
}
return(new_table)
})
print("Update the genome index")
update_genome_index(session = session,
genomes = values$genomes)
})
observeEvent(eventExpr = input$view_genome,
label = "View genome details",
priority = 0, {
print("View the genome details")
toggleElement(id = "genome_details")
})
observeEvent(eventExpr = c(values$genomes[input$genome_index, 5:8],
input$view_genome),
label = "Update genome details view",
priority = -5, {
print("Update the genome view")
if(isolate(nrow(values$genomes) > 0)){
# Get the genome row
genome_row <- values$genomes[input$genome_index, ]
# Indicate if the genome intervals are ready
output$genome_interval_status <- renderUI({
if(as.character(genome_row[5])=="None"){
return(HTML("<font color='red'>Incomplete</font>"))
} else {
return(HTML("<font color='green'>Ready</font>"))
}
})
# Check if gene sets are loaded
if(nrow(genome_row)>0){
gene_list_choices <- make_choices(choices = strsplit(x = genome_row[["Gene.Sets"]],
split = ";",
fixed = TRUE)[[1]],
numbered = FALSE)
}
else{
gene_list_choices <- c(" "= 0)}
updateSelectInput(session = session,
inputId = "gene_list_status",
choices = gene_list_choices)
# Check if the genome fasta location is set
output$genome_fasta_location <- renderUI({
if(as.character(genome_row[["Genome.FASTA"]])=="None"){
return(HTML("<font color='red'>Incomplete</font>"))
} else {
return(HTML("<font color='green'>",genome_row[["Genome.FASTA"]],"</font>"))
}
})
# Check if the genome index location is set
output$genome_index_location <- renderUI({
if(as.character(genome_row[["Bowtie.Index"]])=="None"){
return(HTML("<font color='red'>Incomplete</font>"))
} else {
return(HTML("<font color='green'>",genome_row[["Bowtie.Index"]],"</font>"))
}
})
# Find the genome fasta
shinyFileChoose(input = input,
session = session,
id = "genome_fasta_finder",
roots = isolate(values$genome_volumes),
filetypes = c("fasta", "fa"))
# Find the genome index
shinyDirChoose(input = input,
session = session,
id = "genome_index_finder",
roots = isolate(values$index_volumes))
# Find gene lists
shinyFileChoose(input = input,
session = session,
id = "gene_list_finder",
roots = isolate(values$genome_volumes),
filetypes = c("txt", "tsv"))
}
})
observeEvent(eventExpr = input$genome_fasta_finder,
label = "Fasta finder", {
print("Get the genome fasta file location")
if(!is.null(input$genome_fasta_finder)){
fasta_location <- parseFilePaths(roots = values$genome_volumes, selection = input$genome_fasta_finder)
values$genomes[isolate(input$genome_index), "Genome.FASTA"] <- getAbsolutePath(as.character(fasta_location[["datapath"]]))
}
})
observeEvent(eventExpr = input$genome_index_finder,
label = "Index finder", {
print("Get the genome index file location")
if(!is.null(input$genome_index_finder)){
index_location <- parseDirPath(roots = values$index_volumes, selection = input$genome_index_finder)
#updateTextInput(session = session, inputId = "genome_index_finder", value = NULL)
values$genomes[isolate(input$genome_index), "Bowtie.Index"] <- getAbsolutePath(as.character(index_location))
}
})
observeEvent(eventExpr = values$genomes,
label = "Checking complete genome",
priority = -10, {
print("Check if genome is complete")
if(nrow(values$genomes)>0){
values$genomes[,"Status"] <- check_for_complete_genome(genome = values$genomes)
}
})
observeEvent(eventExpr = input$gene_list_finder,
label = "Gene list finder", {
print("Get list of genes")
if(!is.null(input$gene_list_finder)){
gene_list_location <- parseFilePaths(roots = values$genome_volumes, selection = input$gene_list_finder)
isolate(
if(values$genomes[input$genome_index, "Gene.Sets"]=="None"){
values$genomes[[input$genome_index, "Gene.Sets"]] <- getAbsolutePath(gene_list_location[["datapath"]])
} else {
if(getAbsolutePath(gene_list_location[["datapath"]]) %in% strsplit(x = values$genomes[input$genome_index, "Gene.Sets"],
split = ";",
fixed = TRUE)[[1]]) return(NULL)
values$genomes[[input$genome_index, "Gene.Sets"]] <- paste(values$genomes[input$genome_index, "Gene.Sets"],
getAbsolutePath(gene_list_location[["datapath"]]),
sep = ";")
}
)
}
})
observeEvent(eventExpr = input$remove_gene_list,
label = "Remove gene list", {
print("Remove a gene list")
isolate(genome_row <- values$genomes[input$genome_index, ])
isolate(new_list <- paste(strsplit(x = genome_row[["Gene.Sets"]],
split = ";",
fixed = TRUE)[[1]][-(as.integer(input$gene_list_status))], collapse = ";"))
if(new_list=="") new_list <- "None"
isolate(values$genomes[input$genome_index, "Gene.Sets"] <- new_list)
})
observeEvent(eventExpr = input$get_ensembl_genomes,
label = "Get ensembl genomes", {
print("Get the list of ensembl datasets")
# Create a Progress object
values$progress <- shiny::Progress$new()
values$progress$set(message = "Getting datasets: ", value = 0)
on.exit(values$progress$close())
# Get the list of ensembl datasets
values$ensembl_genome_index <- get_datasets_from_biomart(updateProgress = updateProgress)
update_ensembl_genome_index(session = session, genomes = values$ensembl_genome_index)
})
observeEvent(eventExpr = input$add_genome,
label = "Add genome", {
if(values$ensembl_genome_index[as.integer(input$ensembl_genome_index), "dataset"] %in% values$genomes[, "Dataset"])
return(NULL)
values$genomes <- add_genome(genomes = values$genomes, new_genome = values$ensembl_genome_index[as.integer(input$ensembl_genome_index), ])
update_genome_index(session = session, genomes = values$genomes)
})
observeEvent(eventExpr = input$remove_genome,
label = "Remove genome", {
print("Remove a genome")
if(!is.na(input$genome_index)){
values$genomes <- values$genomes[-as.integer(input$genome_index), ]
if(nrow(values$genomes)>0)
row.names(values$genomes) <- 1:nrow(values$genomes)
}
update_genome_index(session = session, genomes = values$genomes)
})
observeEvent(eventExpr = input$get_intervals,
label = "Get intervals", {
print("Get gene intervals from ensembl")
genome <- values$genomes[isolate(input$genome_index), 3]
if(!check_for_intervals(path = values$genomes_dir, genome = genome)){
# Create a Progress object
values$progress <- shiny::Progress$new()
values$progress$set(message = "Getting Genome: ", value = 0)
on.exit(values$progress$close())
# Getting ensembl intervals
get_ensembl_intervals(path = values$genomes_dir, genome = genome, updateProgress = updateProgress)
}
values$genomes[isolate(input$genome_index), 5] <- "OK"
})
observeEvent(eventExpr = input$save_genomes,
label = "Save genomes", {
print("Save the genomes list")
success <- save_info(x = values$genomes,
path = paste(values$genomes_dir,
values$genomes_file,
sep = ""),
delimeter = "\t",
col_names = TRUE)
if(isTRUE(success)){
output$genome_changes_saved <- renderText(expr = {return("Saved")})
}
})
# Clear the indicator after saving
observeEvent(values$genomes, {output$genome_changes_saved <- renderText({" "})})
observeEvent(eventExpr = input$load_genome,
label = "Load genome", {
print("Load the selected genome")
selected_genome <- values$genomes[input$genome_index, ]
if(selected_genome["Status"] == "Ready"){
# If the genome is ready
values$selected_genome <- selected_genome
output$selected_genome <- renderText({return(selected_genome[["Description"]])})
} else {
# If the genome isn't ready
values$selected_genome <- NULL
output$selected_genome <- renderText({"Select a complete genome!"})
}
})
### Adapter selector modal observers
observeEvent(eventExpr = input$view_adapters,
label = "View adapters", {
print("Viewing adapters modal")
showModal(adapter_modal)
output$adapter_table <- renderTable(expr = values$adapters,
rownames = TRUE)
update_adapter_index(session = session,
adapters = values$adapters)
})
observeEvent(eventExpr = input$add_adapter,
label = "Add adapter", {
print("Add an adapter")
values$adapters <- rbind(values$adapters, data.frame("Adapter"=input$add_adapter_sequence_input, "Description"=input$add_adapter_description_input))
update_adapter_index(session = session, adapters = values$adapters)
})
observeEvent(eventExpr = input$remove_adapter,
label = "Remove adapter", {
print("Remove an adapter")
values$adapters <- values$adapters[-c(as.numeric(input$adapter_index)), ]
if(nrow(values$adapters)>0)
row.names(values$adapters) <- 1:nrow(values$adapters)
update_adapter_index(session = session, adapters = values$adapters)
})
observeEvent(eventExpr = input$save_adapters,
label = "Save adapters", {
success <- save_info(x = values$adapters,
path = paste(values$adapters_dir,
values$adapters_file,
sep = ""),
delimeter = "#",
col_names = FALSE)
if(isTRUE(success)){
output$adapter_changes_saved <- renderText(expr = {return("Saved")})
}
})
observeEvent(values$adapters, {output$adapter_changes_saved <- renderText({" "})})
### Common functions
# Close the modal
observeEvent(eventExpr = input$close_modal, {removeModal()})
### Run the workflow on a dataset
# show_plots <- function(){
# results <- eventReactive(input$show_plots,#input$begin_processing,
# #label = "show_plts", #priority = 0,
# {
# print("Sleep before making plots")
# Sys.sleep(40)
# print("Outputing the plots ...")
# for(i in plots[["plot_list"]]){
# print(i)
# output[[i]] <- renderPlot({plots[[i]]})
# }
# })
# }
# eventReactive(values$new_tabset, {
# appendTab(inputId = 'main',
# tab = values$new_tabset,
# select = FALSE)
# })
# makeNewTabs <- function(gene_set){
# eventReactive({
# create_new_tabset(name = gene_set)
# })}
observeEvent(eventExpr = input$begin_processing, once = TRUE, label = "Make Tabs", priority = 5, handlerExpr = {
values$gene_sets <- load_gene_sets(gene_sets = values$selected_genome[["Gene.Sets"]])
print("Making the new tabsets")
print(names(values$gene_sets))
#Sys.sleep(15)
for(gene_set_ in names(values$gene_sets)){
appendTab(inputId = 'main',
tab = create_new_tabset(name = gene_set_),
select = FALSE)
}
values$begin <- TRUE
removeModal()
})
observeEvent(eventExpr = input$show_plots, #values$begin, #input$begin_processing,
once = TRUE,
label = "Show the plots",
# priority = 0, ignoreInit = TRUE,
handlerExpr = {
print("Outputing the plots ...")
output[["test_plot"]] <- renderImage({
list(
# src = "/PACER/data/output/SRR5023999_100K_sample-_2018-06-03_01-20-39/Full/five_prime/five_prime_plot__Full__no_mm__both__positions.svg",
src = "/media/ab/HD4/PACER/data/output/SRR5023999_100K_sample-_2018-06-04_21-07-38/Full/five_prime/five_prime_plot__Full__shuffled_alignment__both__reads.png",
filetype = "image/png",
width = 'auto',
height = 'auto',
alt = "sample plot"
)}
)
# for(i in plots[["plot_list"]]){
# print(i)
# output[[i]] <- renderPlot({plots[[i]]})
# }
})
observeEvent(eventExpr = input$make_plots, #values$begin, #input$begin_processing,
#once = TRUE,
label = "Make the plots",
priority = 0,
ignoreInit = TRUE,
handlerExpr = {
removeModal()
#### Toggle everything off!!
# Create a Progress object
# values$progress <- shiny::Progress$new()
# values$progress$set(message = "Progress: ", value = 0)
# on.exit(values$progress$close())
print(input$get_range)
print(input$read_cutoff)
# Get the current time
values$timestamp <- get_timestamp()
values$dataset_ID <- make_filename(filename = input$input_file,
dataset_name = input$dataset_name)
print(values$dataset_ID)
print(values$output_dir)
values$output_dir <- getAbsolutePath(create_output_dir(out_dir = values$output_dir,
name = paste(values$dataset_ID,
values$timestamp[[1]][1],
gsub(pattern = ":",
replacement = "-",
x = values$timestamp[[1]][2]),
sep = "_")))
print(values$output_dir)
#withProgress(value = 0, message = "Generating alignments ...", {
# shiny::setProgress(value = .25, detail = "Working")
values$bam_path <- align_reads(trim_cmd = values$trim_cmd,
align_cmd = values$align_cmd,
input_dir = getAbsolutePath(values$input_dir),
input_file = input$input_file,
output_dir = values$output_dir,
adapters_file = paste(values$adapters_dir,
values$adapters_file,
sep = "/"),
index_dir = values$selected_genome[["Bowtie.Index"]],
genome = values$selected_genome[["Version"]],
cores = input$cores,
dataset_ID = values$dataset_ID,
min_length = input$get_range[[1]])
## Print the trimming and alignment logs to the UI
output$trim_log <- renderUI({
pre(includeText(paste(values$output_dir, '/trim.log', sep = '')))
})
#output$trim_cmd <- renderUI({values$trim_cmd})
output$align_log <- renderUI({
pre(includeText(paste(values$output_dir, '/align.log', sep = '')))
})
output$align_cmd <- renderUI({values$align_cmd})
#shiny::setProgress(value = 1, detail = "Done")
#})
print('print(values$bam_path)')
print(values$bam_path)
### Load genome information
#withProgress(min = 0, max = 1, message = "Filtering Alignments", expr = {
#main_workflow()
### Loading the genome data
#incProgress(amount = .2, detail = "Loading intervals")
values$genome_data <- load_genome_data(path = values$genomes_dir,
genome = values$selected_genome[["Version"]])
print(values$genome_data)
print(values$selected_genome[["Gene.Sets"]])
#incProgress(amount = .2, detail = "Loading gene sets")
### Ths is the gene sets loader - be sure to uncomment it if needed
#values$gene_sets <- load_gene_sets(gene_sets = values$selected_genome[["Gene.Sets"]])
print(values$gene_sets)
### Load alignment
#incProgress(amount = .2, detail = "Loading alignments")
values$alignments <- load_alignments(path = values$bam_path)
### Filter alignments by size
#incProgress(amount = .1, detail = "Filtering alignment sizes")
values$alignments <- filter_alignments_by_size(alignments = values$alignments,
minimum = input$get_range[[1]],
maximum = input$get_range[[2]])
### Filter overrepresented reads
#incProgress(amount = .1, detail = "Filtering overrpreseented reads")
values$alignments <- remove_overrepresented_sequences(alignments = values$alignments,
cutoff = input$read_cutoff)
### Get the read and genome sequence
#incProgress(amount = .1, detail = "Get the read sequences")
values$alignments <- get_genome_sequence(gr = values$alignments,
genome_sequence = load_fasta_genome(
path = values$selected_genome[['Genome.FASTA']]
))
### Create 3 sets of alignments based on the mismatch fields
#incProgress(amount = .1, detail = "Filtering mismatches")
mismatch_indexes <- filter_BAM_tags(values$alignments)
values$two_mm <- values$alignments[mismatch_indexes$two_mm]
values$no_mm <- values$alignments[mismatch_indexes$no_mm]
values$no_mm_in_seed <- values$alignments[mismatch_indexes$no_mm_seed]
#})
###### Start the loops to make each plot here
strand_filter <- c("+", "-")
position_filter <- c(FALSE, TRUE)
length_filter <- c(input$get_range[[1]]:input$get_range[[2]])
alignments_sets <- c("two_mm", "no_mm", "no_mm_in_seed", "shuffled_alignment")
region_filter <- c("sense", "antisense", "both")
### Create a shuffled alignment and get the new read sequence
values$shuffled_alignment <- shuffle_alignments(alignments = values$no_mm,
intervals = values$genome_data[["gene_intervals"]],
antisense = TRUE)
values$shuffled_alignment <- get_genome_sequence(gr = values$shuffled_alignment,
genome_sequence = load_fasta_genome(
path = values$selected_genome[['Genome.FASTA']]
))
plots[["plot_list"]] <- c()
for (gene_set_ in names(values$gene_sets[1])){
filtered_genes <- filter_by_gene(gr = values$genome_data[["gene_intervals"]],
gene_list = values$gene_sets[[gene_set_]],
invert = FALSE)
values$five_prime_dir <- create_output_dir(out_dir = paste0(values$output_dir,
"/",
gene_set_,
"/"),
name = 'five_prime')
# appendTab(inputId = 'main',
# tab = create_new_tabset(name = gene_set_),
# select = FALSE)
# values[["new_tabset"]] <- create_new_tabset(name = gene_set_)
# makeNewTabs(gene_set_)
# insertUI(selector = paste0("#","row1_", input$add),
# where = "afterEnd",
# ui =
# )
print("filtered_genes")
print(filtered_genes)
for (alignment_set_ in alignments_sets){
gr_alignment_set_ <- values[[alignment_set_]]
print("gr_alignment_set_")
print(gr_alignment_set_)
for (regions_ in region_filter){
gr_regions <- filter_by_regions(gr = gr_alignment_set_,
type = regions_,
regions = filtered_genes)
print("gr_regions")
print(gr_regions)
for (position_ in position_filter){
if(position_ == TRUE){
gr_position <- filter_unique_positions(gr = gr_regions)
position_name <- "positions"
}
if(position_ == FALSE){
gr_position <- gr_regions
position_name <- "reads"}
print("gr_position")
print(gr_position)
print("make the plot name")
print(gene_set_)
print(alignment_set_)
print(regions_)
print(position_name)
plot_name <- paste(gene_set_, alignment_set_, regions_, position_name, sep="__")
print("make the plot")
# ### This is the main plot function - be sure to uncomment it
plots[[paste0("five_prime_plot__", plot_name)]] <- five_prime_plot(gr = gr_position)
# plots[[paste0("five_prime_plot__", plot_name)]] <- ggplot2::ggplot()
# ###
print("save the plot")
# save('gr_regions',
# file = 'gr_regions.RData',
# ascii = FALSE)
#a <- plots[[paste0("five_prime_plot__", plot_name)]]
#save(a, file = 'plots.RData', ascii = FALSE)
# ### Uncomment this to save files
save_plot(p = plots[[paste0("five_prime_plot__", plot_name)]],
path = values$five_prime_dir,
label = paste0("five_prime_plot__", plot_name))
insertUI(selector = paste0("#","five_prime_plot__",
gene_set_),
where = "beforeEnd",
ui = create_new_output(ID = paste0("five_prime_plot__",
plot_name)),
immediate = FALSE)
plots[["plot_list"]] <- append(x = plots[["plot_list"]],
values = paste0("five_prime_plot__", plot_name))
# a <- paste0("five_prime_plot__", plot_name)
# output[[a]] <- renderPlot({
# plots[[a]]
# })
# output$five_prime_plot__two_mm__both__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__both__22nt_5prime})
}
}
}
}
print(length(plots$plot_list))
print("go to sleep")
# for(i in plots[["plot_list"]][[1]]){
# print(i)
# output$test_plot <- renderPlot({plots[[i]]})
# #Sys.sleep(50)
# }
#a <- plots[["plot_list"]][[1]]
#output$test_plot <- renderPlot({plots[[a]]})
print("wake up")
# show_plots()
# values$make_plots <- TRUE
### Start of the good figures
#
# incProgress(amount = .2, detail = "Filtering regions")
# values$two_mm__both <- filter_by_regions(alignments = values$two_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "both")
# values$two_mm__sense <- filter_by_regions(alignments = values$two_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "sense")
# values$two_mm__antisense <- filter_by_regions(alignments = values$two_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "antisense")
#
#
# print('print(values$alignments)')
# print(values$alignments)
# print('The length of values$two_mm is: ')
# print(length(x = print(values$two_mm)))
# #print(values$two_mm)
# #print(values$no_mm)
# #print(values$no_mm_in_seed)
# print('making the plots')
# print('making the plots two_mm')
# print(width(values$two_mm))
# print(strand(values$two_mm))
# print(mcols(values$two_mm))
# print("shuffled")
# print(values$shuffled)
#
#
# ## Create the output directory for the 5' plots
# values$five_prime_dir <- create_output_dir(out_dir = values$output_dir,
# name = 'five_prime')
# print(values$five_prime_dir)
# plots$five_prime_plot__two_mm__both <- five_prime_plot(
# gr = values$two_mm__both)
# plots$five_prime_plot__two_mm__sense <- five_prime_plot(
# gr = values$two_mm__sense)
# plots$five_prime_plot__two_mm__antisense <- five_prime_plot(
# gr = values$two_mm__antisense)
#
# save_plot(p = plots$five_prime_plot__two_mm__both,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__both')
# save_plot(p = plots$five_prime_plot__two_mm__sense,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__sense')
# save_plot(p = plots$five_prime_plot__two_mm__antisense,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__antisense')
#
# output$five_prime_plot__two_mm__both <- renderPlot({plots$five_prime_plot__two_mm__both})
# output$five_prime_plot__two_mm__sense <- renderPlot({plots$five_prime_plot__two_mm__sense})
# output$five_prime_plot__two_mm__antisense <- renderPlot({plots$five_prime_plot__two_mm__antisense})
#
# ### Create datasets with 5' ends assigned to 22nt reads
# values$two_mm__both__22nt_5prime <- assign_5prime_to_a_length(
# gr = values$two_mm__both,
# primary_length = 22)
# values$two_mm__antisense__22nt_5prime <- assign_5prime_to_a_length(
# gr = values$two_mm__antisense,
# primary_length = 22)
# values$two_mm__sense__22nt_5prime <- assign_5prime_to_a_length(
# gr = values$two_mm__sense,
# primary_length = 22)
#
# plots$five_prime_plot__two_mm__both__22nt_5prime <- five_prime_plot(
# gr = values$two_mm__both__22nt_5prime)
# plots$five_prime_plot__two_mm__sense__22nt_5prime <- five_prime_plot(
# gr = values$two_mm__sense__22nt_5prime)
# plots$five_prime_plot__two_mm__antisense__22nt_5prime <- five_prime_plot(
# gr = values$two_mm__antisense__22nt_5prime)
#
# save_plot(p = plots$five_prime_plot__two_mm__both__22nt_5prime,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__both__22nt_5prime')
# save_plot(p = plots$five_prime_plot__two_mm__sense__22nt_5prime,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__sense__22nt_5prime')
# save_plot(p = plots$five_prime_plot__two_mm__antisense__22nt_5prime,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__antisense__22nt_5prime')
#
# output$five_prime_plot__two_mm__both__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__both__22nt_5prime})
# output$five_prime_plot__two_mm__sense__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__sense__22nt_5prime})
# output$five_prime_plot__two_mm__antisense__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__antisense__22nt_5prime})
#
# ### Create the dataset with the gene overlap counts
# print("create the dataframe with gene overlap counts")
# values$two_mm__antisense__22nt_5prime__overlap_counts <- count_overlaps_by_width(
# gr = values$two_mm__antisense__22nt_5prime,
# regions = values$genome_data[["gene_intervals"]],
# overlap = "antisense",
# normalized = FALSE)
# values$two_mm__antisense__22nt_5prime__overlap_counts_base_22 <- count_overlaps_by_width_and_base(
# gr = values$two_mm__antisense__22nt_5prime,
# regions = values$genome_data[["gene_intervals"]],
# overlap = "antisense",
# normalized = FALSE,
# alignment_width = 22,
# base_col = 'five')
# #print(values$two_mm__antisense__22nt_5prime__overlap_counts_base_22)
# ## Create the output directory for the scatter plots
# values$scatter_dir <- create_output_dir(out_dir = values$output_dir,
# name = 'scatter')
# print(values$scatter_dir)
#
# plots$scatter__two_mm__antisense__22nt_5prime__22_15 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 15)
# plots$scatter__two_mm__antisense__22nt_5prime__22_16 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 16)
# plots$scatter__two_mm__antisense__22nt_5prime__22_17 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 17)
# plots$scatter__two_mm__antisense__22nt_5prime__22_18 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 18)
# plots$scatter__two_mm__antisense__22nt_5prime__22_19 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 19)
# plots$scatter__two_mm__antisense__22nt_5prime__22_20 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 20)
# plots$scatter__two_mm__antisense__22nt_5prime__22_21 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 21)
# plots$scatter__two_mm__antisense__22nt_5prime__22_23 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 23)
# plots$scatter__two_mm__antisense__22nt_5prime__22_24 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 24)
# plots$scatter__two_mm__antisense__22nt_5prime__22_25 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 25)
# plots$scatter__two_mm__antisense__22nt_5prime__22_26 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 26)
# plots$scatter__two_mm__antisense__22nt_5prime__22_27 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 27)
# plots$scatter__two_mm__antisense__22nt_5prime__22_28 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 28)
# plots$scatter__two_mm__antisense__22nt_5prime__22_29 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 29)
# plots$scatter__two_mm__antisense__22nt_5prime__22_30 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 30)
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22A <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts_base_22,
# x = 'G',
# y = 'A')
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22C <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts_base_22,
# x = 'G',
# y = 'C')
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22T <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts_base_22,
# x = 'G',
# y = 'T')
#
# ### Get sequences for alignments with no mismatches
#
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_15,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_15')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_16,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_16')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_17,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_17')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_18,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_18')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_19,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_19')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_20,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_20')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_21,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_21')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_23,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_23')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_24,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_24')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_25,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_25')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_26,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_26')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_27,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_27')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_28,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_28')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_29,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_29')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_30,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_30')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22G_22A,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22G_22A')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22G_22C,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22G_22C')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22G_22T,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22G_22T')
# output$scatter__two_mm__antisense__22nt_5prime__22_15 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_15})
# output$scatter__two_mm__antisense__22nt_5prime__22_16 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_16})
# output$scatter__two_mm__antisense__22nt_5prime__22_17 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_17})
# output$scatter__two_mm__antisense__22nt_5prime__22_18 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_18})
# output$scatter__two_mm__antisense__22nt_5prime__22_19 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_19})
# output$scatter__two_mm__antisense__22nt_5prime__22_20 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_20})
# output$scatter__two_mm__antisense__22nt_5prime__22_21 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_21})
# output$scatter__two_mm__antisense__22nt_5prime__22_23 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_23})
# output$scatter__two_mm__antisense__22nt_5prime__22_24 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_24})
# output$scatter__two_mm__antisense__22nt_5prime__22_25 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_25})
# output$scatter__two_mm__antisense__22nt_5prime__22_26 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_26})
# output$scatter__two_mm__antisense__22nt_5prime__22_27 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_27})
# output$scatter__two_mm__antisense__22nt_5prime__22_28 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_28})
# output$scatter__two_mm__antisense__22nt_5prime__22_29 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_29})
# output$scatter__two_mm__antisense__22nt_5prime__22_30 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_30})
# output$scatter__two_mm__antisense__22nt_5prime__22G_22A <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22A})
# output$scatter__two_mm__antisense__22nt_5prime__22G_22C <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22C})
# output$scatter__two_mm__antisense__22nt_5prime__22G_22T <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22T})
#
#
# ### Create alignments with no mismatches in the seed region
# values$seq_logo_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'seq_logos')
#
# values$no_mm_in_seed__shuffled <- shuffle_alignments(
# alignments = values$no_mm_in_seed,
# intervals = values$genome_data[["gene_intervals"]],
# antisense = TRUE)
# values$no_mm_in_seed__shuffled <- get_genome_sequence(
# gr = values$no_mm_in_seed__shuffled,
# genome_sequence = load_fasta_genome(
# path = values$selected_genome[['Genome.FASTA']]))
# values$no_mm_in_seed <- filter_by_regions(
# alignments = values$no_mm_in_seed,
# regions = values$genome_data[["gene_intervals"]],
# type = "antisense")
#
# #print(values$no_mm_in_seed)
# #print(values$no_mm_in_seed__shuffled)
#
# plots$seq_logo__no_mm_in_seed__antisense__20nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 20,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__21nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 21,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__22nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 22,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__23nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 23,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__24nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 24,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__25nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 25,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__26nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 26,
# five_prime_base = NULL)
#
# output$seq_logo__no_mm_in_seed__antisense__20nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__20nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__21nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__21nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__22nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__22nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__23nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__23nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__24nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__24nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__25nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__25nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__26nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__26nt)
# })
#
# ### Create alignments with no mismatches
# values$phasing_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'phasing')
#
# values$no_mm__overlapping_genes <- filter_by_regions(
# alignments = values$no_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "both")
#
# plots$phasing__no_mm__22nt <- phasing_plot(gr = values$no_mm__overlapping_genes,
# length = 22,
# start_base = 'G')
# plots$phasing__no_mm__26nt <- phasing_plot(gr = values$no_mm__overlapping_genes,
# length = 26,
# start_base = 'G')
# output$phasing__no_mm__22nt <- renderPlot({
# grid.draw(x = plots$phasing__no_mm__22nt)
# })
# output$phasing__no_mm__26nt <- renderPlot({
# grid.draw(x = plots$phasing__no_mm__26nt)
# })
#
#
# ### Create alignments with no mismatches
# values$offsets_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'offsets')
#
# values$no_mm__5prime_22nt <- assign_5prime_to_a_length(gr = values$no_mm__overlapping_genes,
# primary_length = 22)
# TEMP <- values$no_mm__5prime_22nt
# save('TEMP',
# file = 'no_mm__5prime_22nt.RData',
# ascii = FALSE)
# plots$offsets__no_mm__5prime_22nt__sense <- offset_plot(gr = values$no_mm__overlapping_genes,
# primary_length = 22,
# maximum_offset = 10,
# overlap_type = 'sense')
# plots$offsets__no_mm__5prime_22nt__antisense <- offset_plot(gr = values$no_mm__overlapping_genes,
# primary_length = 22,
# maximum_offset = 10,
# overlap_type = 'antisense')
# output$offsets__no_mm__5prime_22nt__sense <- renderPlot({
# grid.draw(x = plots$offsets__no_mm__5prime_22nt__sense)
# })
# output$offsets__no_mm__5prime_22nt__antisense <- renderPlot({
# grid.draw(x = plots$offsets__no_mm__5prime_22nt__antisense)
# })
#
# ### Create alignments with no mismatches
# values$heatmaps_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'heatmaps')
#
# values$no_mm__5prime_longest <- assign_5prime_to_longer(
# gr = values$no_mm)
# values$no_mm__shuffled__5prime_longest <- assign_5prime_to_longer(
# gr = values$no_mm__shuffled)
#
# TEMP <- values$no_mm__5prime_longest
# save('TEMP',
# file = 'no_mm__5prime_longest.RData',
# ascii = FALSE)
#
# values$no_mm__shuffled__5prime_longest__hm__22nt__pos <- calculate_heatmaps(
# gr = values$no_mm__shuffled__5prime_longest,
# length = 22,
# strand = NULL)
# values$no_mm__5prime_longest__hm__22nt__pos <- calculate_heatmaps(
# gr = values$no_mm__5prime_longest,
# length = 22,
# strand = "+")
# values$no_mm__5prime_longest__hm__22nt__neg <- calculate_heatmaps(
# gr = values$no_mm__5prime_longest,
# length = 22,
# strand = "-")
# values$no_mm__5prime_longest__hm__22nt__pos <- subtract_heatmap_background(
# gr = values$no_mm__5prime_longest__hm__22nt__pos,
# bg = values$no_mm__shuffled__5prime_longest__hm__22nt__pos)
# values$no_mm__5prime_longest__hm__22nt__neg <- subtract_heatmap_background(
# gr = values$no_mm__5prime_longest__hm__22nt__neg,
# bg = values$no_mm__shuffled__5prime_longest__hm__22nt__pos)
#
# plots$heatmaps__no_mm__22nt__pos <- heatmap_plot(
# heatmap_data = values$no_mm__5prime_longest__hm__22nt__pos)
# plots$heatmaps__no_mm__22nt__neg <- heatmap_plot(
# heatmap_data = values$no_mm__5prime_longest__hm__22nt__neg)
#
# output$heatmaps__no_mm__22nt__pos <- renderPlot({
# grid.draw(x = plots$heatmaps__no_mm__22nt__pos)
# })
# output$heatmaps__no_mm__22nt__neg <- renderPlot({
# grid.draw(x = plots$heatmaps__no_mm__22nt__neg)
# })
#
#### End of the good figures
# save_plot(p = plots$seq_logo__no_mm_in_seed__antisense__23nt,
# path = values$seq_logo_dir,
# label = 'seq_logo__no_mm_in_seed__antisense__23nt')
# plots$no_mm_in_seed__23 <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = no_mm_in_seed__shuffled,
# length = 23,
# five_prime_base = NULL)
# two_mm_five_prime <- make_length_plots(gr = values$two_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "two_mm__five_prime")
# output$two_mm_five_prime <- renderPlot({two_mm_five_prime})
#
# no_mm_five_prime <- make_length_plots(gr = values$no_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm__five_prime")
# output$no_mm_five_prime <- renderPlot({no_mm_five_prime})
#
# no_mm_in_seed_five_prime <- make_length_plots(gr = values$no_mm_in_seed,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm_in_seed__five_prime")
# output$no_mm_in_seed_five_prime <- renderPlot({no_mm_in_seed_five_prime})
#
# shuffled_five_prime <- make_length_plots(gr = values$shuffled,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "shuffled__five_prime")
# output$shuffled_five_prime <- renderPlot({shuffled_five_prime})
# print('making the plots no_mm')
# make_length_plots(gr = values$no_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm__all_genes")
# print('making the plots no_mm_in_seed')
# make_length_plots(gr = values$no_mm_in_seed,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm_in_seed__all_genes")
# output$fivepp_all <- renderPlot({
# print('making the plot')
# p = make_length_plots(gr = values$two_mm,
# path = paste(values$output_dir,
# '/five_prime/',
# sep = ''),
# label = 'two_mm__all_genes')
# print('printing the plot')
# print(p)
# return(NULL)
# })
#make_length_plots(gr = values$two_mm, path = values$output_dir, label = "two_mm")
#make_length_plots(gr = values$no_mm, path = values$output_dir, label = "no_mm")
#make_length_plots(gr = values$no_mm_in_seed, path = values$output_dir, label = "no_mm_in_seed")
# strvalues$selected_genome
#print(values$selected_genome)
# for(i in values$selected_genome)
# {
#
# }
})
}
# alignments <- filter_alignments(alignments = alignments,
# regions = genome_data[["gene_intervals"]],
# regions_filter = "both",
# minimum = length_range["minimum"],
# maximum = length_range["maximum"])
#
#
#gene_lists <- load_gene_lists(path = paste(getwd(),"genomes",genome, sep = "/"), gene_lists_files = genome_files[[genome]]$gene_list_files)
# genome_data <- load_genome_data(path = "data/genomes", genome = "WBcel235")
# #genome_data <- load_genome_data(path = paste(getwd(),"data/genomes", sep="/"), genome = genome)
# genome_sequence <- load_fasta_genome(path = paste(getwd(),"genomes",genome,as.character(genome_files[[genome]]$genome_file), sep="/"))
# gene_lists <- load_gene_lists(path = paste(getwd(),"genomes",genome, sep = "/"), gene_lists_files = genome_files[[genome]]$gene_list_files)
# ## Load and filter the main alignment file
# #alignments <- load_alignments(path = alignment_file)
# alignments <- load_alignments(path = "data/output/WT_early_rep1/two_seed_mm_SRR5023999.bam")
# alignments <- filter_alignments(alignments = alignments,
# regions = genome_data[["gene_intervals"]],
# regions_filter = "both",
# minimum = length_range["minimum"],
# maximum = length_range["maximum"],
#
#
# trim_cmd <- make_trim_command(input_dir = getAbsolutePath(values$input_dir),
# output_dir = values$output_dir,
# dataset_ID = values$dataset_ID,
# input_file = input$input_file,
# adapter_file = paste(values$adapters_dir,
# values$adapters_file,
# sep = "/"),
# min_length = input$get_range[[1]])
# print(trim_cmd)
# print(input$cores)
# values$trim_output <- run_trimmer(output_dir = values$output_dir,
# dataset_ID = values$dataset_ID,
# trim_cmd = trim_cmd)
# write_data_to_TSV(data = trim_log,
# path = values$output_dir,
# filename = paste(values$dataset_ID,
# ".trim.log",
# sep = ""))
### Listeners
#align_dataset_listener <- reactive({input$align_dataset})
#view_results_listener <- reactive({input$view_results})
#begin_processing_listener <- reactive({input$begin_processing})
#select_genome_listener <- reactive({input$select_genome})
#load_genome_listener <- reactive({input$load_genome})
#view_genome_listener <- reactive({input$view_genome})
#get_ensembl_genomes_listener <- reactive({input$get_ensembl_genomes})
#add_genome_listener <- reactive({input$add_genome})
#remove_genome_listener <- reactive({input$remove_genome})
#get_intervals_listener <- reactive({input$get_intervals})
#check_complete_genome_listener <- reactive({values$genomes})
#remove_gene_list_listener <- reactive({input$remove_gene_list})
#update_genome_view_listener <- reactive({values$update_genome_view})
#genome_fasta_finder_listener <- reactive({ input$genome_fasta_finder})
#genome_index_finder_listener <- reactive({ input$genome_index_finder})
#gene_list_finder_listener <- reactive({input$gene_list_finder})
#save_genomes_listener <- reactive({input$save_genomes})
#load_genome_listener <- reactive({input$load_genome})
#nrow_genomes_listener <- reactive({values$genomes})
#view_adapters_listener <- reactive({input$view_adapters})
#add_adapter_listener <- reactive({input$add_adapter})
#remove_adapter_listener <- reactive({input$remove_adapter})
#save_adapters_listener <- reactive({input$save_adapters})
#output$adapter_changes_saved <- renderText({" "})
# output$input_dir <- renderText({
# # If a save directory is selected, display the name of the save directory
# return(values$input_dir)
# })
# # Get the current time
# timestamp <- strsplit(x = as.character(Sys.time()), split = " ")
#
# # Make the full output directory with timestamp
# values$output_dir <- paste(values$save_dir, "/", timestamp[[1]][1], "_", timestamp[[1]][2], sep = "")
#
alb202/PACER documentation built on May 16, 2019, 10:05 a.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.
#server.R
library(shiny)
library(shinyFiles)
library(shinyjs)
library(DT)
library(R.utils)
library(tidyverse)
library(GenomicAlignments)
library(biomaRt)
library(Rcpp)
library(GenomicAlignments)
library(GenomicFeatures)
library(biomaRt)
library(tidyverse)
library(data.table)
library(scales)
library(svglite)
library(quantreg)
library(ggseqlogo)
library(geometry)
library(grid)
library(gridExtra)
source("shiny_helper.R")
source("modals.R")
Rcpp::sourceCpp(file = "../cpp/cpp_functions.cpp")
source("../R/load.R")
source("../R/alignments.R")
source("../R/other.R")
source("../R/filters.R")
source("../R/make_figures.R")
source("../R/calculations.R")
source("../R/figures.R")
source("../R/sequences.R")
server <- function(input, output, session){
values <- reactiveValues()
plots <- reactiveValues()
begin <- reactiveVal()
observeEvent(eventExpr = values, once = TRUE, {
# Set the initial value of the input directory to the first entry in the volumes list
values$input_volumes <- c("Input"="../data/input","Home"="~/", "Root"="/")
values$output_volumes <- c("Output"="../data/output","Home"="~/", "Root"="/")
values$genome_volumes <- c("Genomes"="../data/genomes","Home"="~/", "Root"="/")
values$index_volumes <- c("Indexes"="../data/indexes","Home"="~/", "Root"="/")
# values$genomes <- read.delim(file = "../data/genomes/genomes_new.txt", stringsAsFactors = FALSE, header = FALSE, sep = ",", row.names= NULL, col.names = c("Description", "Version", "Dataset", "Status", "Interval.Status", "Genome.FASTA", "Bowtie.Index", "Gene.Sets"))
values$genomes_dir <- "../data/genomes/"
values$adapters_dir <- "../data/adapters/"
values$genomes_file <- "genomes.txt"
values$adapters_file <- "adapters.txt"
values$adapters <- read.delim(file = paste(values$adapters_dir, values$adapters_file, sep = ""), stringsAsFactors = FALSE, header = FALSE, sep = "#", col.names = c("Adapter", "Description"))
values$genomes <- read.delim(file = paste(values$genomes_dir, values$genomes_file, sep = ""), stringsAsFactors = FALSE, header = TRUE, sep = "\t", row.names= NULL, col.names = c("Dataset", "Description", "Version", "Status", "Interval.Status", "Genome.FASTA", "Bowtie.Index", "Gene.Sets"))
# Output directories
values$input_dir <- values$input_volumes[[1]]
values$output_dir <- values$output_volumes[[1]]
values$trim_cmd <- "cutadapt --max-n=0 -m"
values$align_cmd <- "bowtie -n2 -e1000 -l22 -k4 --best --strata -S --threads"
#values$bt_options <- c("-n2", "-e1000", "-l22", "-k4", "--best", "--strata", "-S")
})
observe({
# Only show the "Align dataset" button if a dataset has been selected
toggleState(id = "use_dataset", condition = (!is.null(input$input_file) & !is.null(values$selected_genome)))
})
observe({
# Only show the "View dataset" button if a dataset has been selected
toggleState(id = "view_dataset", condition = !is.null(input$processed_file))
})
output$input_dir <- renderText({
# Print the dataset directory
print("render directory name")
return(getAbsolutePath(values$input_dir))
})
output$output_dir <- renderText({
# Print the Output directory
print("render output directory name")
return(values$output_dir)
})
observe({
print("getting input directory")
# When the directory button is pressed, these run
shinyDirChoose(input = input,
id = "find_input_dir",
roots=isolate(values$input_volumes),
session=session
)
# Parse the result and save the output directory
if(!is.null(input$find_input_dir))
values$input_dir <- parseDirPath(roots = values$input_volumes,
selection = input$find_input_dir)
# Print the output directories
print(values$input_dir)
})
observe({
print("getting output directory")
# When the directory button is pressed, these run
shinyDirChoose(input = input,
id = "find_output_dir",
roots=isolate(values$output_volumes),
session=session
)
# Parse the result and save the output directory
if(!is.null(input$find_output_dir))
values$output_dir <- parseDirPath(roots = values$output_volumes,
selection = input$find_output_dir)
# Print the output directories
print(isolate(values$output_dir))
})
observeEvent(values$input_dir, {
# Update the file select box with the list of fastq files from the directory
print("Update the file input box")
updateSelectInput(session = session,
inputId = "input_file",
label = NULL,
choices = load_directory(dir = values$input_dir,
filetypes = c("fastq", "fq")),
selected = NULL)
})
observeEvent(values$output_dir, once = TRUE,{
# Update the file select box with the list of processed datasets
print("Update the output box")
updateSelectInput(session = session,
inputId = "processed_file",
label = NULL,
choices = load_directory(dir = values$output_dir,
filetypes = "",
include_dirs = TRUE),
selected = NULL)
# print("Sleep before making plots")
# Sys.sleep(40)
# print("Outputing the plots ...")
# for(i in plots[["plot_list"]]){
# print(i)
# output[[i]] <- renderPlot({plots[[i]]})
# }
})
observeEvent(input$use_dataset, {
print("Open the start modal")
input_file <- input$input_file
showModal(start_modal)
output$start_modal_title <- renderText({paste("File: ", input$input_file, sep = "")})
})
### Genome selector modal observers
observeEvent(label = "Select genome modal",
eventExpr = input$select_genome,
priority = 100, {
print("Select genome")
showModal(genomes_modal)
output$genome_table <- renderTable(width = "100%" ,
rownames = TRUE,
spacing = "s",
expr = {
new_table <- values$genomes
if(nrow(new_table)>0){
print("Changing values of new_table to OK")
new_table[,5:8][new_table[,5:8]!="None"] <- "OK"
}
return(new_table)
})
print("Update the genome index")
update_genome_index(session = session,
genomes = values$genomes)
})
observeEvent(eventExpr = input$view_genome,
label = "View genome details",
priority = 0, {
print("View the genome details")
toggleElement(id = "genome_details")
})
observeEvent(eventExpr = c(values$genomes[input$genome_index, 5:8],
input$view_genome),
label = "Update genome details view",
priority = -5, {
print("Update the genome view")
if(isolate(nrow(values$genomes) > 0)){
# Get the genome row
genome_row <- values$genomes[input$genome_index, ]
# Indicate if the genome intervals are ready
output$genome_interval_status <- renderUI({
if(as.character(genome_row[5])=="None"){
return(HTML("<font color='red'>Incomplete</font>"))
} else {
return(HTML("<font color='green'>Ready</font>"))
}
})
# Check if gene sets are loaded
if(nrow(genome_row)>0){
gene_list_choices <- make_choices(choices = strsplit(x = genome_row[["Gene.Sets"]],
split = ";",
fixed = TRUE)[[1]],
numbered = FALSE)
}
else{
gene_list_choices <- c(" "= 0)}
updateSelectInput(session = session,
inputId = "gene_list_status",
choices = gene_list_choices)
# Check if the genome fasta location is set
output$genome_fasta_location <- renderUI({
if(as.character(genome_row[["Genome.FASTA"]])=="None"){
return(HTML("<font color='red'>Incomplete</font>"))
} else {
return(HTML("<font color='green'>",genome_row[["Genome.FASTA"]],"</font>"))
}
})
# Check if the genome index location is set
output$genome_index_location <- renderUI({
if(as.character(genome_row[["Bowtie.Index"]])=="None"){
return(HTML("<font color='red'>Incomplete</font>"))
} else {
return(HTML("<font color='green'>",genome_row[["Bowtie.Index"]],"</font>"))
}
})
# Find the genome fasta
shinyFileChoose(input = input,
session = session,
id = "genome_fasta_finder",
roots = isolate(values$genome_volumes),
filetypes = c("fasta", "fa"))
# Find the genome index
shinyDirChoose(input = input,
session = session,
id = "genome_index_finder",
roots = isolate(values$index_volumes))
# Find gene lists
shinyFileChoose(input = input,
session = session,
id = "gene_list_finder",
roots = isolate(values$genome_volumes),
filetypes = c("txt", "tsv"))
}
})
observeEvent(eventExpr = input$genome_fasta_finder,
label = "Fasta finder", {
print("Get the genome fasta file location")
if(!is.null(input$genome_fasta_finder)){
fasta_location <- parseFilePaths(roots = values$genome_volumes, selection = input$genome_fasta_finder)
values$genomes[isolate(input$genome_index), "Genome.FASTA"] <- getAbsolutePath(as.character(fasta_location[["datapath"]]))
}
})
observeEvent(eventExpr = input$genome_index_finder,
label = "Index finder", {
print("Get the genome index file location")
if(!is.null(input$genome_index_finder)){
index_location <- parseDirPath(roots = values$index_volumes, selection = input$genome_index_finder)
#updateTextInput(session = session, inputId = "genome_index_finder", value = NULL)
values$genomes[isolate(input$genome_index), "Bowtie.Index"] <- getAbsolutePath(as.character(index_location))
}
})
observeEvent(eventExpr = values$genomes,
label = "Checking complete genome",
priority = -10, {
print("Check if genome is complete")
if(nrow(values$genomes)>0){
values$genomes[,"Status"] <- check_for_complete_genome(genome = values$genomes)
}
})
observeEvent(eventExpr = input$gene_list_finder,
label = "Gene list finder", {
print("Get list of genes")
if(!is.null(input$gene_list_finder)){
gene_list_location <- parseFilePaths(roots = values$genome_volumes, selection = input$gene_list_finder)
isolate(
if(values$genomes[input$genome_index, "Gene.Sets"]=="None"){
values$genomes[[input$genome_index, "Gene.Sets"]] <- getAbsolutePath(gene_list_location[["datapath"]])
} else {
if(getAbsolutePath(gene_list_location[["datapath"]]) %in% strsplit(x = values$genomes[input$genome_index, "Gene.Sets"],
split = ";",
fixed = TRUE)[[1]]) return(NULL)
values$genomes[[input$genome_index, "Gene.Sets"]] <- paste(values$genomes[input$genome_index, "Gene.Sets"],
getAbsolutePath(gene_list_location[["datapath"]]),
sep = ";")
}
)
}
})
observeEvent(eventExpr = input$remove_gene_list,
label = "Remove gene list", {
print("Remove a gene list")
isolate(genome_row <- values$genomes[input$genome_index, ])
isolate(new_list <- paste(strsplit(x = genome_row[["Gene.Sets"]],
split = ";",
fixed = TRUE)[[1]][-(as.integer(input$gene_list_status))], collapse = ";"))
if(new_list=="") new_list <- "None"
isolate(values$genomes[input$genome_index, "Gene.Sets"] <- new_list)
})
observeEvent(eventExpr = input$get_ensembl_genomes,
label = "Get ensembl genomes", {
print("Get the list of ensembl datasets")
# Create a Progress object
values$progress <- shiny::Progress$new()
values$progress$set(message = "Getting datasets: ", value = 0)
on.exit(values$progress$close())
# Get the list of ensembl datasets
values$ensembl_genome_index <- get_datasets_from_biomart(updateProgress = updateProgress)
update_ensembl_genome_index(session = session, genomes = values$ensembl_genome_index)
})
observeEvent(eventExpr = input$add_genome,
label = "Add genome", {
if(values$ensembl_genome_index[as.integer(input$ensembl_genome_index), "dataset"] %in% values$genomes[, "Dataset"])
return(NULL)
values$genomes <- add_genome(genomes = values$genomes, new_genome = values$ensembl_genome_index[as.integer(input$ensembl_genome_index), ])
update_genome_index(session = session, genomes = values$genomes)
})
observeEvent(eventExpr = input$remove_genome,
label = "Remove genome", {
print("Remove a genome")
if(!is.na(input$genome_index)){
values$genomes <- values$genomes[-as.integer(input$genome_index), ]
if(nrow(values$genomes)>0)
row.names(values$genomes) <- 1:nrow(values$genomes)
}
update_genome_index(session = session, genomes = values$genomes)
})
observeEvent(eventExpr = input$get_intervals,
label = "Get intervals", {
print("Get gene intervals from ensembl")
genome <- values$genomes[isolate(input$genome_index), 3]
if(!check_for_intervals(path = values$genomes_dir, genome = genome)){
# Create a Progress object
values$progress <- shiny::Progress$new()
values$progress$set(message = "Getting Genome: ", value = 0)
on.exit(values$progress$close())
# Getting ensembl intervals
get_ensembl_intervals(path = values$genomes_dir, genome = genome, updateProgress = updateProgress)
}
values$genomes[isolate(input$genome_index), 5] <- "OK"
})
observeEvent(eventExpr = input$save_genomes,
label = "Save genomes", {
print("Save the genomes list")
success <- save_info(x = values$genomes,
path = paste(values$genomes_dir,
values$genomes_file,
sep = ""),
delimeter = "\t",
col_names = TRUE)
if(isTRUE(success)){
output$genome_changes_saved <- renderText(expr = {return("Saved")})
}
})
# Clear the indicator after saving
observeEvent(values$genomes, {output$genome_changes_saved <- renderText({" "})})
observeEvent(eventExpr = input$load_genome,
label = "Load genome", {
print("Load the selected genome")
selected_genome <- values$genomes[input$genome_index, ]
if(selected_genome["Status"] == "Ready"){
# If the genome is ready
values$selected_genome <- selected_genome
output$selected_genome <- renderText({return(selected_genome[["Description"]])})
} else {
# If the genome isn't ready
values$selected_genome <- NULL
output$selected_genome <- renderText({"Select a complete genome!"})
}
})
### Adapter selector modal observers
observeEvent(eventExpr = input$view_adapters,
label = "View adapters", {
print("Viewing adapters modal")
showModal(adapter_modal)
output$adapter_table <- renderTable(expr = values$adapters,
rownames = TRUE)
update_adapter_index(session = session,
adapters = values$adapters)
})
observeEvent(eventExpr = input$add_adapter,
label = "Add adapter", {
print("Add an adapter")
values$adapters <- rbind(values$adapters, data.frame("Adapter"=input$add_adapter_sequence_input, "Description"=input$add_adapter_description_input))
update_adapter_index(session = session, adapters = values$adapters)
})
observeEvent(eventExpr = input$remove_adapter,
label = "Remove adapter", {
print("Remove an adapter")
values$adapters <- values$adapters[-c(as.numeric(input$adapter_index)), ]
if(nrow(values$adapters)>0)
row.names(values$adapters) <- 1:nrow(values$adapters)
update_adapter_index(session = session, adapters = values$adapters)
})
observeEvent(eventExpr = input$save_adapters,
label = "Save adapters", {
success <- save_info(x = values$adapters,
path = paste(values$adapters_dir,
values$adapters_file,
sep = ""),
delimeter = "#",
col_names = FALSE)
if(isTRUE(success)){
output$adapter_changes_saved <- renderText(expr = {return("Saved")})
}
})
observeEvent(values$adapters, {output$adapter_changes_saved <- renderText({" "})})
### Common functions
# Close the modal
observeEvent(eventExpr = input$close_modal, {removeModal()})
### Run the workflow on a dataset
# show_plots <- function(){
# results <- eventReactive(input$show_plots,#input$begin_processing,
# #label = "show_plts", #priority = 0,
# {
# print("Sleep before making plots")
# Sys.sleep(40)
# print("Outputing the plots ...")
# for(i in plots[["plot_list"]]){
# print(i)
# output[[i]] <- renderPlot({plots[[i]]})
# }
# })
# }
# eventReactive(values$new_tabset, {
# appendTab(inputId = 'main',
# tab = values$new_tabset,
# select = FALSE)
# })
# makeNewTabs <- function(gene_set){
# eventReactive({
# create_new_tabset(name = gene_set)
# })}
observeEvent(eventExpr = input$begin_processing, once = TRUE, label = "Make Tabs", priority = 5, handlerExpr = {
values$gene_sets <- load_gene_sets(gene_sets = values$selected_genome[["Gene.Sets"]])
print("Making the new tabsets")
print(names(values$gene_sets))
#Sys.sleep(15)
for(gene_set_ in names(values$gene_sets)){
appendTab(inputId = 'main',
tab = create_new_tabset(name = gene_set_),
select = FALSE)
}
values$begin <- TRUE
removeModal()
})
observeEvent(eventExpr = input$show_plots, #values$begin, #input$begin_processing,
once = TRUE,
label = "Show the plots",
# priority = 0, ignoreInit = TRUE,
handlerExpr = {
print("Outputing the plots ...")
output[["test_plot"]] <- renderImage({
list(
# src = "/PACER/data/output/SRR5023999_100K_sample-_2018-06-03_01-20-39/Full/five_prime/five_prime_plot__Full__no_mm__both__positions.svg",
src = "/media/ab/HD4/PACER/data/output/SRR5023999_100K_sample-_2018-06-04_21-07-38/Full/five_prime/five_prime_plot__Full__shuffled_alignment__both__reads.png",
filetype = "image/png",
width = 'auto',
height = 'auto',
alt = "sample plot"
)}
)
# for(i in plots[["plot_list"]]){
# print(i)
# output[[i]] <- renderPlot({plots[[i]]})
# }
})
observeEvent(eventExpr = input$make_plots, #values$begin, #input$begin_processing,
#once = TRUE,
label = "Make the plots",
priority = 0,
ignoreInit = TRUE,
handlerExpr = {
removeModal()
#### Toggle everything off!!
# Create a Progress object
# values$progress <- shiny::Progress$new()
# values$progress$set(message = "Progress: ", value = 0)
# on.exit(values$progress$close())
print(input$get_range)
print(input$read_cutoff)
# Get the current time
values$timestamp <- get_timestamp()
values$dataset_ID <- make_filename(filename = input$input_file,
dataset_name = input$dataset_name)
print(values$dataset_ID)
print(values$output_dir)
values$output_dir <- getAbsolutePath(create_output_dir(out_dir = values$output_dir,
name = paste(values$dataset_ID,
values$timestamp[[1]][1],
gsub(pattern = ":",
replacement = "-",
x = values$timestamp[[1]][2]),
sep = "_")))
print(values$output_dir)
#withProgress(value = 0, message = "Generating alignments ...", {
# shiny::setProgress(value = .25, detail = "Working")
values$bam_path <- align_reads(trim_cmd = values$trim_cmd,
align_cmd = values$align_cmd,
input_dir = getAbsolutePath(values$input_dir),
input_file = input$input_file,
output_dir = values$output_dir,
adapters_file = paste(values$adapters_dir,
values$adapters_file,
sep = "/"),
index_dir = values$selected_genome[["Bowtie.Index"]],
genome = values$selected_genome[["Version"]],
cores = input$cores,
dataset_ID = values$dataset_ID,
min_length = input$get_range[[1]])
## Print the trimming and alignment logs to the UI
output$trim_log <- renderUI({
pre(includeText(paste(values$output_dir, '/trim.log', sep = '')))
})
#output$trim_cmd <- renderUI({values$trim_cmd})
output$align_log <- renderUI({
pre(includeText(paste(values$output_dir, '/align.log', sep = '')))
})
output$align_cmd <- renderUI({values$align_cmd})
#shiny::setProgress(value = 1, detail = "Done")
#})
print('print(values$bam_path)')
print(values$bam_path)
### Load genome information
#withProgress(min = 0, max = 1, message = "Filtering Alignments", expr = {
#main_workflow()
### Loading the genome data
#incProgress(amount = .2, detail = "Loading intervals")
values$genome_data <- load_genome_data(path = values$genomes_dir,
genome = values$selected_genome[["Version"]])
print(values$genome_data)
print(values$selected_genome[["Gene.Sets"]])
#incProgress(amount = .2, detail = "Loading gene sets")
### Ths is the gene sets loader - be sure to uncomment it if needed
#values$gene_sets <- load_gene_sets(gene_sets = values$selected_genome[["Gene.Sets"]])
print(values$gene_sets)
### Load alignment
#incProgress(amount = .2, detail = "Loading alignments")
values$alignments <- load_alignments(path = values$bam_path)
### Filter alignments by size
#incProgress(amount = .1, detail = "Filtering alignment sizes")
values$alignments <- filter_alignments_by_size(alignments = values$alignments,
minimum = input$get_range[[1]],
maximum = input$get_range[[2]])
### Filter overrepresented reads
#incProgress(amount = .1, detail = "Filtering overrpreseented reads")
values$alignments <- remove_overrepresented_sequences(alignments = values$alignments,
cutoff = input$read_cutoff)
### Get the read and genome sequence
#incProgress(amount = .1, detail = "Get the read sequences")
values$alignments <- get_genome_sequence(gr = values$alignments,
genome_sequence = load_fasta_genome(
path = values$selected_genome[['Genome.FASTA']]
))
### Create 3 sets of alignments based on the mismatch fields
#incProgress(amount = .1, detail = "Filtering mismatches")
mismatch_indexes <- filter_BAM_tags(values$alignments)
values$two_mm <- values$alignments[mismatch_indexes$two_mm]
values$no_mm <- values$alignments[mismatch_indexes$no_mm]
values$no_mm_in_seed <- values$alignments[mismatch_indexes$no_mm_seed]
#})
###### Start the loops to make each plot here
strand_filter <- c("+", "-")
position_filter <- c(FALSE, TRUE)
length_filter <- c(input$get_range[[1]]:input$get_range[[2]])
alignments_sets <- c("two_mm", "no_mm", "no_mm_in_seed", "shuffled_alignment")
region_filter <- c("sense", "antisense", "both")
### Create a shuffled alignment and get the new read sequence
values$shuffled_alignment <- shuffle_alignments(alignments = values$no_mm,
intervals = values$genome_data[["gene_intervals"]],
antisense = TRUE)
values$shuffled_alignment <- get_genome_sequence(gr = values$shuffled_alignment,
genome_sequence = load_fasta_genome(
path = values$selected_genome[['Genome.FASTA']]
))
plots[["plot_list"]] <- c()
for (gene_set_ in names(values$gene_sets[1])){
filtered_genes <- filter_by_gene(gr = values$genome_data[["gene_intervals"]],
gene_list = values$gene_sets[[gene_set_]],
invert = FALSE)
values$five_prime_dir <- create_output_dir(out_dir = paste0(values$output_dir,
"/",
gene_set_,
"/"),
name = 'five_prime')
# appendTab(inputId = 'main',
# tab = create_new_tabset(name = gene_set_),
# select = FALSE)
# values[["new_tabset"]] <- create_new_tabset(name = gene_set_)
# makeNewTabs(gene_set_)
# insertUI(selector = paste0("#","row1_", input$add),
# where = "afterEnd",
# ui =
# )
print("filtered_genes")
print(filtered_genes)
for (alignment_set_ in alignments_sets){
gr_alignment_set_ <- values[[alignment_set_]]
print("gr_alignment_set_")
print(gr_alignment_set_)
for (regions_ in region_filter){
gr_regions <- filter_by_regions(gr = gr_alignment_set_,
type = regions_,
regions = filtered_genes)
print("gr_regions")
print(gr_regions)
for (position_ in position_filter){
if(position_ == TRUE){
gr_position <- filter_unique_positions(gr = gr_regions)
position_name <- "positions"
}
if(position_ == FALSE){
gr_position <- gr_regions
position_name <- "reads"}
print("gr_position")
print(gr_position)
print("make the plot name")
print(gene_set_)
print(alignment_set_)
print(regions_)
print(position_name)
plot_name <- paste(gene_set_, alignment_set_, regions_, position_name, sep="__")
print("make the plot")
# ### This is the main plot function - be sure to uncomment it
plots[[paste0("five_prime_plot__", plot_name)]] <- five_prime_plot(gr = gr_position)
# plots[[paste0("five_prime_plot__", plot_name)]] <- ggplot2::ggplot()
# ###
print("save the plot")
# save('gr_regions',
# file = 'gr_regions.RData',
# ascii = FALSE)
#a <- plots[[paste0("five_prime_plot__", plot_name)]]
#save(a, file = 'plots.RData', ascii = FALSE)
# ### Uncomment this to save files
save_plot(p = plots[[paste0("five_prime_plot__", plot_name)]],
path = values$five_prime_dir,
label = paste0("five_prime_plot__", plot_name))
insertUI(selector = paste0("#","five_prime_plot__",
gene_set_),
where = "beforeEnd",
ui = create_new_output(ID = paste0("five_prime_plot__",
plot_name)),
immediate = FALSE)
plots[["plot_list"]] <- append(x = plots[["plot_list"]],
values = paste0("five_prime_plot__", plot_name))
# a <- paste0("five_prime_plot__", plot_name)
# output[[a]] <- renderPlot({
# plots[[a]]
# })
# output$five_prime_plot__two_mm__both__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__both__22nt_5prime})
}
}
}
}
print(length(plots$plot_list))
print("go to sleep")
# for(i in plots[["plot_list"]][[1]]){
# print(i)
# output$test_plot <- renderPlot({plots[[i]]})
# #Sys.sleep(50)
# }
#a <- plots[["plot_list"]][[1]]
#output$test_plot <- renderPlot({plots[[a]]})
print("wake up")
# show_plots()
# values$make_plots <- TRUE
### Start of the good figures
#
# incProgress(amount = .2, detail = "Filtering regions")
# values$two_mm__both <- filter_by_regions(alignments = values$two_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "both")
# values$two_mm__sense <- filter_by_regions(alignments = values$two_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "sense")
# values$two_mm__antisense <- filter_by_regions(alignments = values$two_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "antisense")
#
#
# print('print(values$alignments)')
# print(values$alignments)
# print('The length of values$two_mm is: ')
# print(length(x = print(values$two_mm)))
# #print(values$two_mm)
# #print(values$no_mm)
# #print(values$no_mm_in_seed)
# print('making the plots')
# print('making the plots two_mm')
# print(width(values$two_mm))
# print(strand(values$two_mm))
# print(mcols(values$two_mm))
# print("shuffled")
# print(values$shuffled)
#
#
# ## Create the output directory for the 5' plots
# values$five_prime_dir <- create_output_dir(out_dir = values$output_dir,
# name = 'five_prime')
# print(values$five_prime_dir)
# plots$five_prime_plot__two_mm__both <- five_prime_plot(
# gr = values$two_mm__both)
# plots$five_prime_plot__two_mm__sense <- five_prime_plot(
# gr = values$two_mm__sense)
# plots$five_prime_plot__two_mm__antisense <- five_prime_plot(
# gr = values$two_mm__antisense)
#
# save_plot(p = plots$five_prime_plot__two_mm__both,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__both')
# save_plot(p = plots$five_prime_plot__two_mm__sense,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__sense')
# save_plot(p = plots$five_prime_plot__two_mm__antisense,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__antisense')
#
# output$five_prime_plot__two_mm__both <- renderPlot({plots$five_prime_plot__two_mm__both})
# output$five_prime_plot__two_mm__sense <- renderPlot({plots$five_prime_plot__two_mm__sense})
# output$five_prime_plot__two_mm__antisense <- renderPlot({plots$five_prime_plot__two_mm__antisense})
#
# ### Create datasets with 5' ends assigned to 22nt reads
# values$two_mm__both__22nt_5prime <- assign_5prime_to_a_length(
# gr = values$two_mm__both,
# primary_length = 22)
# values$two_mm__antisense__22nt_5prime <- assign_5prime_to_a_length(
# gr = values$two_mm__antisense,
# primary_length = 22)
# values$two_mm__sense__22nt_5prime <- assign_5prime_to_a_length(
# gr = values$two_mm__sense,
# primary_length = 22)
#
# plots$five_prime_plot__two_mm__both__22nt_5prime <- five_prime_plot(
# gr = values$two_mm__both__22nt_5prime)
# plots$five_prime_plot__two_mm__sense__22nt_5prime <- five_prime_plot(
# gr = values$two_mm__sense__22nt_5prime)
# plots$five_prime_plot__two_mm__antisense__22nt_5prime <- five_prime_plot(
# gr = values$two_mm__antisense__22nt_5prime)
#
# save_plot(p = plots$five_prime_plot__two_mm__both__22nt_5prime,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__both__22nt_5prime')
# save_plot(p = plots$five_prime_plot__two_mm__sense__22nt_5prime,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__sense__22nt_5prime')
# save_plot(p = plots$five_prime_plot__two_mm__antisense__22nt_5prime,
# path = values$five_prime_dir,
# label = 'five_prime_plot__two_mm__antisense__22nt_5prime')
#
# output$five_prime_plot__two_mm__both__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__both__22nt_5prime})
# output$five_prime_plot__two_mm__sense__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__sense__22nt_5prime})
# output$five_prime_plot__two_mm__antisense__22nt_5prime <- renderPlot({
# plots$five_prime_plot__two_mm__antisense__22nt_5prime})
#
# ### Create the dataset with the gene overlap counts
# print("create the dataframe with gene overlap counts")
# values$two_mm__antisense__22nt_5prime__overlap_counts <- count_overlaps_by_width(
# gr = values$two_mm__antisense__22nt_5prime,
# regions = values$genome_data[["gene_intervals"]],
# overlap = "antisense",
# normalized = FALSE)
# values$two_mm__antisense__22nt_5prime__overlap_counts_base_22 <- count_overlaps_by_width_and_base(
# gr = values$two_mm__antisense__22nt_5prime,
# regions = values$genome_data[["gene_intervals"]],
# overlap = "antisense",
# normalized = FALSE,
# alignment_width = 22,
# base_col = 'five')
# #print(values$two_mm__antisense__22nt_5prime__overlap_counts_base_22)
# ## Create the output directory for the scatter plots
# values$scatter_dir <- create_output_dir(out_dir = values$output_dir,
# name = 'scatter')
# print(values$scatter_dir)
#
# plots$scatter__two_mm__antisense__22nt_5prime__22_15 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 15)
# plots$scatter__two_mm__antisense__22nt_5prime__22_16 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 16)
# plots$scatter__two_mm__antisense__22nt_5prime__22_17 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 17)
# plots$scatter__two_mm__antisense__22nt_5prime__22_18 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 18)
# plots$scatter__two_mm__antisense__22nt_5prime__22_19 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 19)
# plots$scatter__two_mm__antisense__22nt_5prime__22_20 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 20)
# plots$scatter__two_mm__antisense__22nt_5prime__22_21 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 21)
# plots$scatter__two_mm__antisense__22nt_5prime__22_23 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 23)
# plots$scatter__two_mm__antisense__22nt_5prime__22_24 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 24)
# plots$scatter__two_mm__antisense__22nt_5prime__22_25 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 25)
# plots$scatter__two_mm__antisense__22nt_5prime__22_26 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 26)
# plots$scatter__two_mm__antisense__22nt_5prime__22_27 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 27)
# plots$scatter__two_mm__antisense__22nt_5prime__22_28 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 28)
# plots$scatter__two_mm__antisense__22nt_5prime__22_29 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 29)
# plots$scatter__two_mm__antisense__22nt_5prime__22_30 <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts,
# x = 22,
# y = 30)
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22A <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts_base_22,
# x = 'G',
# y = 'A')
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22C <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts_base_22,
# x = 'G',
# y = 'C')
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22T <- scatter_plot(
# df = values$two_mm__antisense__22nt_5prime__overlap_counts_base_22,
# x = 'G',
# y = 'T')
#
# ### Get sequences for alignments with no mismatches
#
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_15,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_15')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_16,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_16')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_17,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_17')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_18,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_18')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_19,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_19')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_20,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_20')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_21,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_21')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_23,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_23')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_24,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_24')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_25,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_25')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_26,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_26')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_27,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_27')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_28,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_28')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_29,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_29')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22_30,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22_30')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22G_22A,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22G_22A')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22G_22C,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22G_22C')
# # save_plot(p = plots$scatter__two_mm__antisense__22nt_5prime__22G_22T,
# # path = values$scatter_dir,
# # label = 'scatter__two_mm__antisense__22nt_5prime__22G_22T')
# output$scatter__two_mm__antisense__22nt_5prime__22_15 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_15})
# output$scatter__two_mm__antisense__22nt_5prime__22_16 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_16})
# output$scatter__two_mm__antisense__22nt_5prime__22_17 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_17})
# output$scatter__two_mm__antisense__22nt_5prime__22_18 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_18})
# output$scatter__two_mm__antisense__22nt_5prime__22_19 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_19})
# output$scatter__two_mm__antisense__22nt_5prime__22_20 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_20})
# output$scatter__two_mm__antisense__22nt_5prime__22_21 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_21})
# output$scatter__two_mm__antisense__22nt_5prime__22_23 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_23})
# output$scatter__two_mm__antisense__22nt_5prime__22_24 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_24})
# output$scatter__two_mm__antisense__22nt_5prime__22_25 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_25})
# output$scatter__two_mm__antisense__22nt_5prime__22_26 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_26})
# output$scatter__two_mm__antisense__22nt_5prime__22_27 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_27})
# output$scatter__two_mm__antisense__22nt_5prime__22_28 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_28})
# output$scatter__two_mm__antisense__22nt_5prime__22_29 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_29})
# output$scatter__two_mm__antisense__22nt_5prime__22_30 <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22_30})
# output$scatter__two_mm__antisense__22nt_5prime__22G_22A <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22A})
# output$scatter__two_mm__antisense__22nt_5prime__22G_22C <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22C})
# output$scatter__two_mm__antisense__22nt_5prime__22G_22T <- renderPlot({
# plots$scatter__two_mm__antisense__22nt_5prime__22G_22T})
#
#
# ### Create alignments with no mismatches in the seed region
# values$seq_logo_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'seq_logos')
#
# values$no_mm_in_seed__shuffled <- shuffle_alignments(
# alignments = values$no_mm_in_seed,
# intervals = values$genome_data[["gene_intervals"]],
# antisense = TRUE)
# values$no_mm_in_seed__shuffled <- get_genome_sequence(
# gr = values$no_mm_in_seed__shuffled,
# genome_sequence = load_fasta_genome(
# path = values$selected_genome[['Genome.FASTA']]))
# values$no_mm_in_seed <- filter_by_regions(
# alignments = values$no_mm_in_seed,
# regions = values$genome_data[["gene_intervals"]],
# type = "antisense")
#
# #print(values$no_mm_in_seed)
# #print(values$no_mm_in_seed__shuffled)
#
# plots$seq_logo__no_mm_in_seed__antisense__20nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 20,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__21nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 21,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__22nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 22,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__23nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 23,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__24nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 24,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__25nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 25,
# five_prime_base = NULL)
#
# plots$seq_logo__no_mm_in_seed__antisense__26nt <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = values$no_mm_in_seed__shuffled,
# length = 26,
# five_prime_base = NULL)
#
# output$seq_logo__no_mm_in_seed__antisense__20nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__20nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__21nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__21nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__22nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__22nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__23nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__23nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__24nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__24nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__25nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__25nt)
# })
# output$seq_logo__no_mm_in_seed__antisense__26nt <- renderPlot({
# grid.draw(x = plots$seq_logo__no_mm_in_seed__antisense__26nt)
# })
#
# ### Create alignments with no mismatches
# values$phasing_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'phasing')
#
# values$no_mm__overlapping_genes <- filter_by_regions(
# alignments = values$no_mm,
# regions = values$genome_data[["gene_intervals"]],
# type = "both")
#
# plots$phasing__no_mm__22nt <- phasing_plot(gr = values$no_mm__overlapping_genes,
# length = 22,
# start_base = 'G')
# plots$phasing__no_mm__26nt <- phasing_plot(gr = values$no_mm__overlapping_genes,
# length = 26,
# start_base = 'G')
# output$phasing__no_mm__22nt <- renderPlot({
# grid.draw(x = plots$phasing__no_mm__22nt)
# })
# output$phasing__no_mm__26nt <- renderPlot({
# grid.draw(x = plots$phasing__no_mm__26nt)
# })
#
#
# ### Create alignments with no mismatches
# values$offsets_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'offsets')
#
# values$no_mm__5prime_22nt <- assign_5prime_to_a_length(gr = values$no_mm__overlapping_genes,
# primary_length = 22)
# TEMP <- values$no_mm__5prime_22nt
# save('TEMP',
# file = 'no_mm__5prime_22nt.RData',
# ascii = FALSE)
# plots$offsets__no_mm__5prime_22nt__sense <- offset_plot(gr = values$no_mm__overlapping_genes,
# primary_length = 22,
# maximum_offset = 10,
# overlap_type = 'sense')
# plots$offsets__no_mm__5prime_22nt__antisense <- offset_plot(gr = values$no_mm__overlapping_genes,
# primary_length = 22,
# maximum_offset = 10,
# overlap_type = 'antisense')
# output$offsets__no_mm__5prime_22nt__sense <- renderPlot({
# grid.draw(x = plots$offsets__no_mm__5prime_22nt__sense)
# })
# output$offsets__no_mm__5prime_22nt__antisense <- renderPlot({
# grid.draw(x = plots$offsets__no_mm__5prime_22nt__antisense)
# })
#
# ### Create alignments with no mismatches
# values$heatmaps_dir <- create_output_dir(
# out_dir = values$output_dir,
# name = 'heatmaps')
#
# values$no_mm__5prime_longest <- assign_5prime_to_longer(
# gr = values$no_mm)
# values$no_mm__shuffled__5prime_longest <- assign_5prime_to_longer(
# gr = values$no_mm__shuffled)
#
# TEMP <- values$no_mm__5prime_longest
# save('TEMP',
# file = 'no_mm__5prime_longest.RData',
# ascii = FALSE)
#
# values$no_mm__shuffled__5prime_longest__hm__22nt__pos <- calculate_heatmaps(
# gr = values$no_mm__shuffled__5prime_longest,
# length = 22,
# strand = NULL)
# values$no_mm__5prime_longest__hm__22nt__pos <- calculate_heatmaps(
# gr = values$no_mm__5prime_longest,
# length = 22,
# strand = "+")
# values$no_mm__5prime_longest__hm__22nt__neg <- calculate_heatmaps(
# gr = values$no_mm__5prime_longest,
# length = 22,
# strand = "-")
# values$no_mm__5prime_longest__hm__22nt__pos <- subtract_heatmap_background(
# gr = values$no_mm__5prime_longest__hm__22nt__pos,
# bg = values$no_mm__shuffled__5prime_longest__hm__22nt__pos)
# values$no_mm__5prime_longest__hm__22nt__neg <- subtract_heatmap_background(
# gr = values$no_mm__5prime_longest__hm__22nt__neg,
# bg = values$no_mm__shuffled__5prime_longest__hm__22nt__pos)
#
# plots$heatmaps__no_mm__22nt__pos <- heatmap_plot(
# heatmap_data = values$no_mm__5prime_longest__hm__22nt__pos)
# plots$heatmaps__no_mm__22nt__neg <- heatmap_plot(
# heatmap_data = values$no_mm__5prime_longest__hm__22nt__neg)
#
# output$heatmaps__no_mm__22nt__pos <- renderPlot({
# grid.draw(x = plots$heatmaps__no_mm__22nt__pos)
# })
# output$heatmaps__no_mm__22nt__neg <- renderPlot({
# grid.draw(x = plots$heatmaps__no_mm__22nt__neg)
# })
#
#### End of the good figures
# save_plot(p = plots$seq_logo__no_mm_in_seed__antisense__23nt,
# path = values$seq_logo_dir,
# label = 'seq_logo__no_mm_in_seed__antisense__23nt')
# plots$no_mm_in_seed__23 <- seq_logo_comparisons(
# gr = values$no_mm_in_seed,
# shuffled_gr = no_mm_in_seed__shuffled,
# length = 23,
# five_prime_base = NULL)
# two_mm_five_prime <- make_length_plots(gr = values$two_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "two_mm__five_prime")
# output$two_mm_five_prime <- renderPlot({two_mm_five_prime})
#
# no_mm_five_prime <- make_length_plots(gr = values$no_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm__five_prime")
# output$no_mm_five_prime <- renderPlot({no_mm_five_prime})
#
# no_mm_in_seed_five_prime <- make_length_plots(gr = values$no_mm_in_seed,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm_in_seed__five_prime")
# output$no_mm_in_seed_five_prime <- renderPlot({no_mm_in_seed_five_prime})
#
# shuffled_five_prime <- make_length_plots(gr = values$shuffled,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "shuffled__five_prime")
# output$shuffled_five_prime <- renderPlot({shuffled_five_prime})
# print('making the plots no_mm')
# make_length_plots(gr = values$no_mm,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm__all_genes")
# print('making the plots no_mm_in_seed')
# make_length_plots(gr = values$no_mm_in_seed,
# path = paste(values$output_dir,
# '/five_prime',
# sep = ''),
# label = "no_mm_in_seed__all_genes")
# output$fivepp_all <- renderPlot({
# print('making the plot')
# p = make_length_plots(gr = values$two_mm,
# path = paste(values$output_dir,
# '/five_prime/',
# sep = ''),
# label = 'two_mm__all_genes')
# print('printing the plot')
# print(p)
# return(NULL)
# })
#make_length_plots(gr = values$two_mm, path = values$output_dir, label = "two_mm")
#make_length_plots(gr = values$no_mm, path = values$output_dir, label = "no_mm")
#make_length_plots(gr = values$no_mm_in_seed, path = values$output_dir, label = "no_mm_in_seed")
# strvalues$selected_genome
#print(values$selected_genome)
# for(i in values$selected_genome)
# {
#
# }
})
}
# alignments <- filter_alignments(alignments = alignments,
# regions = genome_data[["gene_intervals"]],
# regions_filter = "both",
# minimum = length_range["minimum"],
# maximum = length_range["maximum"])
#
#
#gene_lists <- load_gene_lists(path = paste(getwd(),"genomes",genome, sep = "/"), gene_lists_files = genome_files[[genome]]$gene_list_files)
# genome_data <- load_genome_data(path = "data/genomes", genome = "WBcel235")
# #genome_data <- load_genome_data(path = paste(getwd(),"data/genomes", sep="/"), genome = genome)
# genome_sequence <- load_fasta_genome(path = paste(getwd(),"genomes",genome,as.character(genome_files[[genome]]$genome_file), sep="/"))
# gene_lists <- load_gene_lists(path = paste(getwd(),"genomes",genome, sep = "/"), gene_lists_files = genome_files[[genome]]$gene_list_files)
# ## Load and filter the main alignment file
# #alignments <- load_alignments(path = alignment_file)
# alignments <- load_alignments(path = "data/output/WT_early_rep1/two_seed_mm_SRR5023999.bam")
# alignments <- filter_alignments(alignments = alignments,
# regions = genome_data[["gene_intervals"]],
# regions_filter = "both",
# minimum = length_range["minimum"],
# maximum = length_range["maximum"],
#
#
# trim_cmd <- make_trim_command(input_dir = getAbsolutePath(values$input_dir),
# output_dir = values$output_dir,
# dataset_ID = values$dataset_ID,
# input_file = input$input_file,
# adapter_file = paste(values$adapters_dir,
# values$adapters_file,
# sep = "/"),
# min_length = input$get_range[[1]])
# print(trim_cmd)
# print(input$cores)
# values$trim_output <- run_trimmer(output_dir = values$output_dir,
# dataset_ID = values$dataset_ID,
# trim_cmd = trim_cmd)
# write_data_to_TSV(data = trim_log,
# path = values$output_dir,
# filename = paste(values$dataset_ID,
# ".trim.log",
# sep = ""))
### Listeners
#align_dataset_listener <- reactive({input$align_dataset})
#view_results_listener <- reactive({input$view_results})
#begin_processing_listener <- reactive({input$begin_processing})
#select_genome_listener <- reactive({input$select_genome})
#load_genome_listener <- reactive({input$load_genome})
#view_genome_listener <- reactive({input$view_genome})
#get_ensembl_genomes_listener <- reactive({input$get_ensembl_genomes})
#add_genome_listener <- reactive({input$add_genome})
#remove_genome_listener <- reactive({input$remove_genome})
#get_intervals_listener <- reactive({input$get_intervals})
#check_complete_genome_listener <- reactive({values$genomes})
#remove_gene_list_listener <- reactive({input$remove_gene_list})
#update_genome_view_listener <- reactive({values$update_genome_view})
#genome_fasta_finder_listener <- reactive({ input$genome_fasta_finder})
#genome_index_finder_listener <- reactive({ input$genome_index_finder})
#gene_list_finder_listener <- reactive({input$gene_list_finder})
#save_genomes_listener <- reactive({input$save_genomes})
#load_genome_listener <- reactive({input$load_genome})
#nrow_genomes_listener <- reactive({values$genomes})
#view_adapters_listener <- reactive({input$view_adapters})
#add_adapter_listener <- reactive({input$add_adapter})
#remove_adapter_listener <- reactive({input$remove_adapter})
#save_adapters_listener <- reactive({input$save_adapters})
#output$adapter_changes_saved <- renderText({" "})
# output$input_dir <- renderText({
# # If a save directory is selected, display the name of the save directory
# return(values$input_dir)
# })
# # Get the current time
# timestamp <- strsplit(x = as.character(Sys.time()), split = " ")
#
# # Make the full output directory with timestamp
# values$output_dir <- paste(values$save_dir, "/", timestamp[[1]][1], "_", timestamp[[1]][2], sep = "")
#
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.