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app masterfile.R
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app masterfile.R
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###################################################################################
#
# ViMO (Visualizer for multi-omics)
#
# An R Shiny Application for integrative multi-omics bioinformatics
#
# Inputs:
#
# 1. Masterfile of all genes with quantitative and functional annotation (tsv)
# 2. File with all contig, their bins, GC% and coverages (tsv)
#
# Aim: Provide interactive exploration of the data
#
#
# Date: 26.09.22
# Author: Magnus Arntzen
#
###################################################################################
#You want to increase this if uploading Masterfile and Contigfile > 200 Mb.
options(shiny.maxRequestSize=200*1024^2)
######################## - LIBRARIES AND GLOBALS - #####################################
options(stringsAsFactors=F)
library(ggplot2)
library(tidyverse)
library(gridExtra)
library(ggthemr)
library(RColorBrewer)
library(dichromat)
library(grid)
library(gridExtra)
library(DT)
library(KEGGREST)
library(shiny)
library(shinythemes)
library(shinyjs)
library(MetQy)
library(pheatmap)
library(magick)
wd <- getwd()
options(dplyr.summarise.inform = FALSE)
options(repos = BiocManager::repositories())
######################## - EXTERNAL FILES AND GLOBALS- #####################################
source("functions.R")
#No need to change anything here.
#ViMO allows for uploading your own files at run-time
KEGG_generic_file <- paste0(wd, "/ko00001.keg.txt")
KEGG_mapping_file <- paste0(wd, "/ko00001.keg.mapping.txt")
masterfile <- ''
contigfile <- ''
example_masterfile <- paste0(wd, "/www/All_genes_functional_quantitative_data.txt")
example_contigfile <- paste0(wd, "/www/Bin_taxonomy_coverages.txt")
######################## - LOADING AND PREPROCESSING - #####################################
# Load KEGG generic data for App functionality
ko_tbl <- read.table(KEGG_generic_file, header=T, sep="\t", quote="")
ko_mapping_tbl <- read.table(KEGG_mapping_file, header=T, sep="\t", quote="", na.strings = c(""))
k_numbers_connected_to_pathways <- ko_mapping_tbl %>%
drop_na(Pathway) %>%
select(KO) %>%
distinct() %>%
deframe()
######################## - APP UI - #####################################
#Define UIs
ui_dataset_details <- fluidRow(
wellPanel(
h3("Dataset overview"),
fileInput("masterfile", "Choose Masterfile", accept = ".txt"),
fileInput("contigfile", "Choose Contigfile", accept = ".txt"),
p("See the help section for an explanation of the file formats."),
p("Current file-size upload limits are set to 200 Mb."),
actionButton("loadButton", "Load files", class = "btn-success"),
actionButton("loadButton_example", "Use example files", class = "btn-success"),
hr(),
htmlOutput("filesSummary"),
#actionButton("STOPButton", "Close App", icon = icon("fa-solid fa-octagon-exclamation")),
)
)
ui_cazy_functional_analysis <- fluidPage(width = 12,
wellPanel(
tags$style(type='text/css', ".selectize-input { padding: 2px; min-height: 0;} .selectize-dropdown { line-height: 10px; }"),
selectInput(inputId = "cazyGroup", label = "Choose a CAZy functional group", choices = c("NA"))
),
fluidRow(
h3(textOutput("cazyCaption", container = span)),
fluidRow(
column(4, offset = 4,
selectInput(inputId = "sample_MT", label = "MetaT sample", choices = c("NA")),
),
column(4,
selectInput(inputId = "sample_MP", label = "MetaP sample", choices = c("NA")),
)
),
fluidRow(
column(4, offset = 8,
selectInput(inputId = "yaxis_MP", label = "y-axis", choices = c("Summed LFQ", "Protein count")),
)
),
fluidRow(
column(4,
h4("Meta-genomics"),
plotOutput(outputId = "cazyPlot_MG"),
downloadButton("download_cazyPlot_MG", label = "PDF")
),
column(4,
h4("Meta-transcriptomics"),
plotOutput(outputId = "cazyPlot_MT"),
downloadButton("download_cazyPlot_MT", label = "PDF")
),
column(4,
h4("Meta-proteomics"),
plotOutput(outputId = "cazyPlot_MP"),
downloadButton("download_cazyPlot_MP", label = "PDF")
)
),
fluidRow(
column(12,
plotOutput(outputId = "cazyPlot_legend", inline = TRUE),# height = "100px"),
hr(),
h5("The barplots show the number/abundance of genes/transcripts/proteins per bin and per sample."),
h5("The heatmaps below show the abundance of transcripts/proteins per bin but for all samples simultaneously to highlight e.g. changes over time.")
)
),
hr(),
fluidRow(
column(12,
h3("Functional abundance per sample per MAG"),
selectInput(inputId = "cazyPlot_heatmap_binSelector", label = "MAG:", choices = c("NA")),
plotOutput(outputId = "cazyPlot_heatmap_MT", inline = TRUE),
downloadButton("download_cazyPlot_MT_heatmap", label = "PDF"),
hr(),
plotOutput(outputId = "cazyPlot_heatmap_MP", inline = TRUE),
downloadButton("download_cazyPlot_MP_heatmap", label = "PDF")
)
)
)
)
ui_kegg_functional_analysis <- fluidPage(width = 12,
wellPanel(
selectInput(inputId = "keggGroup", label = "Choose a KEGG pathway", choices = c("NA"))
),
fluidRow(
h3(textOutput("keggCaption", container = span)),
fluidRow(
column(4, offset = 4,
selectInput(inputId = "kegg_sample_MT", label = "MetaT sample", choices = c("NA")),
),
column(4,
selectInput(inputId = "kegg_sample_MP", label = "MetaP sample", choices = c("NA")),
)
),
fluidRow(
column(4, offset = 8,
selectInput(inputId = "kegg_yaxis_MP", label = "y-axis", choices = c("Summed LFQ", "Protein count")),
)
)
),
fluidRow(
column(4,
h4("Meta-genomics"),
plotOutput(outputId = "keggPlot_MG"),
downloadButton("download_keggPlot_MG", label = "PDF")
),
column(4,
h4("Meta-transcriptomics"),
plotOutput(outputId = "keggPlot_MT"),
downloadButton("download_keggPlot_MT", label = "PDF")
),
column(4,
h4("Meta-proteomics"),
plotOutput(outputId = "keggPlot_MP"),
downloadButton("download_keggPlot_MP", label = "PDF")
)
),
fluidRow(
plotOutput(outputId = "keggPlot_legend", inline = TRUE)#, height = "100px")
),
fluidRow(
div(DT::dataTableOutput("keggTable"), style ="font-size: 90%; height: 75%"),
em("Click a pathway to download and highlight the corresponding KEGG pathway map"),
hr()
),
fluidRow(
column(7,
h4(htmlOutput("ERRORtext_KEGGmap")),
imageOutput(outputId = "keggPathway", inline = TRUE), #height = "600px"),
hr(),
em("NB: Note that the scale and colors used in the KEGG map can deviate from the heatmaps to the right as this depend on color swath chosen in settings and the number of bins in the data")
),
column(5,
checkboxInput(inputId = "keggplot_check_download_kegg", label = "Download and display KEGG pathway", value = TRUE),
radioButtons(inputId = "keggplot_annotation_radio", label = "Mark KEGG pathway with", choiceNames = c("Meta-genomics", "Meta-transcriptomics (average abundance across samples)", "Meta-proteomics (average abundance across samples)"), choiceValues = c("MetaG", "MetaT", "MetaP")),
selectInput(inputId = "keggPlot_heatmap_binSelector", label = "MAG:", choices = c("NA")),
plotOutput(outputId = "keggPlot_heatmap_MT", inline = TRUE),
downloadButton("download_keggPlot_MT_heatmap", label = "PDF"),
hr(),
plotOutput(outputId = "keggPlot_heatmap_MP", inline = TRUE),
downloadButton("download_keggPlot_MP_heatmap", label = "PDF")
)
)
)
ui_MAGs <- fluidPage(width = 12,
fluidRow(
h3("Metagenome-assembled genomes (MAGs)"),
h4(textOutput("MAGplot_info", container = span)),
fluidRow(
column(2,
sliderInput(inputId = "MAG_slider_completeness", label = "Filter to MAGs with Completeness above", min = 0, max = 100, value = 90, step = 1, post="%", ticks = FALSE),
),
column(2,
sliderInput(inputId = "MAG_slider_contamination", label = "Filter to MAGs with Contamination below", min = 0, max = 100, value = 5, step = 1, post="%", ticks = FALSE),
),
column(8)
),
plotOutput(outputId = "MAGPlot", height = "600px"),
downloadButton("downloadMAGPlot", label = "PDF"),
h5("The plot shows the GC% and coverage of all contigs in the metagenomics data. The size reflects the contig length and the color shows which MAG/bin the contig was assigned to."),
h5("The plot will never show more then 50 MAGs for visualization purposes. If the table below has >50 MAGs the plot will be limited to the 50 most abundant ones."),
br(),
br(),
div(DT::dataTableOutput("MAGTable"), style ="font-size: 90%; height: 75%"),
em("Click a bin to highlight it in the plot"),
hr(),
h3("MAG abundances"),
plotOutput(outputId = "MAGabundancePlot_heatmap_MT", inline = TRUE),
downloadButton("download_MAGabundancePlot_MT_heatmap", label = "PDF"),
hr(),
plotOutput(outputId = "MAGabundancePlot_heatmap_MP", inline = TRUE),
downloadButton("download_MAGabundancePlot_MP_heatmap", label = "PDF")
)
)
ui_MCF <- fluidPage(width = 12,
fluidRow(
column(1,
actionButton("calculate_mcf_button", "Calculate mcf", class = "btn-success")
),
column(11,
em("NB: Calculation of Module completion fraction (mcf) is time-consuming and more so for large datasets with many bins. For the example files (9 bins, 15.656 genes), this will take 3-4 minutes and ViMO will be unresponsive while calculating. The plot will be updated when the process is finished.")
)
),
br(),
br(),
wellPanel(
selectInput(inputId = "mcf_keggGroup", label = "Filter by a KEGG pathway", choices = c("NA")),
numericInput(inputId = "mcf_module_cutoff", label = "Only show modules where at least one mcf value is at/above", value = 0.5, min = 0.0, max = 1.0, step = 0.1)
),
fluidRow(
plotOutput(outputId = "mcfPlot", height = "1200px"),
h5("The plot shows the Module completion fraction (mcf) for all bins. This is indicative of the metabolic potential of each bin/MAG"),
column(1,
downloadButton("downloadMCFPlot", label = "PDF")
),
column(1,
downloadButton("downloadMCFPlot_data", label = "tsv")
)
)
)
ui_Settings <- fluidPage(width = 12,
wellPanel(
h3("Settings"),
selectInput(inputId = "gg_theme", label = "Theme for ggplots", choices = c('flat', 'flat dark', 'dust', 'light', 'earth', 'fresh', 'chalk', 'lilac', 'carrot', 'pale', 'copper', 'grape', 'greyscale', 'sky', 'solarized', 'grass', 'sea', 'camouflage'), selected = 'flat'),
textOutput("gg_themeCaption", container = span)
#shinythemes::themeSelector()
)
)
ui_Help <- fluidPage(width = 12,
fluidRow(
includeHTML(paste0(wd, "/helptext.html"))
)
)
ui_main <- navbarPage(title=div(img(src="ViMO_logo.png", width="150")), theme = shinytheme("cerulean"), windowTitle = "ViMO",
tags$head(
tags$style(
HTML("#shiny-notification-panel {
top: 0;
bottom: unset;
left: 0;
right: 0;
margin-left: auto;
margin-right: auto;
width: 100%;
max-width: 450px;
}
.navbar {
min-height:60px !important;
}
")
)
),
useShinyjs(),
extendShinyjs(text = jscode, functions = c("disableTab", "enableTab")),
inlineCSS(css),
tabPanel("Load data", ui_dataset_details),
tabPanel("MAGs", ui_MAGs),
tabPanel("CAZy", ui_cazy_functional_analysis),
tabPanel("KEGG", ui_kegg_functional_analysis),
tabPanel("KEGG-Modules", ui_MCF),
tabPanel("Settings", ui_Settings),
tabPanel("Help", ui_Help)
)
######################## - APP SERVER - #####################################
#Define server
server <- function(input, output, session) {
#Server config
session$onSessionEnded(function() {
stopApp()
})
#Some start-up settings
js$disableTab("MAGs")
js$disableTab("CAZy")
js$disableTab("KEGG")
js$disableTab("KEGG-Modules")
#Colors
global_swatch = NULL
#Runtime
mcf_running = FALSE
#App functionality, will be adjusted based on loaded files
do_metaP = TRUE
do_metaT = TRUE
do_lineage = TRUE
do_checkM = TRUE
#The main data object is reactive. When changed, it will trigger changes in everything
data <- reactiveValues(
masterfile = NULL,
contigfile = NULL,
master_tbl = NULL,
contig_tbl = NULL,
cazymes = NULL,
cazymes_expanded = NULL,
kegg_proteins = NULL,
kegg_proteins_expanded = NULL
)
#Create all the needed tables for the app once "data" changes (typically when loaded)
updateTables <- function() {
print("update tables")
#Sanity check of the files
required_columns_masterfile <- c('Accn', 'Bin', 'KEGG', 'KO', 'CAZy') %in% colnames(data$master_tbl)
required_columns_contigfile <- c('Contig', 'Bin', 'Length', 'Coverage', 'GC') %in% colnames(data$contig_tbl)
if (FALSE %in% required_columns_masterfile) {
showNotification("Masterfile missing required columns, see help section", type = "error", duration = NULL)
clearAllData()
return()
}
if (FALSE %in% required_columns_contigfile) {
showNotification("Contigfile missing required columns, see help section", type = "error", duration = NULL)
clearAllData()
return()
}
do_metaP <<- length(colnames(data$master_tbl %>% select(contains("metaP_LFQ")))) > 0
do_metaT <<- length(colnames(data$master_tbl %>% select(contains("metaT_TPM")))) > 0
do_lineage <<- 'Lineage' %in% colnames(data$contig_tbl)
do_checkM <<- !FALSE %in% (c('Completeness', 'Contamination', 'Strain.heterogeneity') %in% colnames(data$contig_tbl))
print(paste("MetaP:", do_metaP))
print(paste("MetaT:", do_metaT))
print(paste("Lineage:", do_lineage))
print(paste("CheckM:", do_checkM))
#Since 2022, InterProScan does no longer provide KO-pathways and EC annotations,
#hence, the KEGG and EC columns are often empty, which will affect the KEGG analysis
#in this tool.
if (nrow(data$master_tbl %>% drop_na(KEGG)) == 0) {
showNotification("No KEGG-Pathway data available from InterProScan. See the help section and use the script makeMasterFile.R to re-map KEGG and EC data from KoFamScan-data", type = "error", duration = NULL)
clearAllData()
return()
}
# Using the Masterfile-table and Contigfile-table, we will extract some sub-tables for easier analysis:
# The purpose of having these sub-tables is to avoid recalculating them many times - saves time
#
# 1. cazymes
# 2. cazymes_expanded
# 3. kegg_proteins
# 4. kegg_proteins_expanded
### Cazymes - a list of all cazymes and which bin they belong to + quantitative data
data$cazymes <- data$master_tbl %>%
select(Bin, CAZy, contains("metaT_TPM"), contains("metaP_LFQ")) %>%
filter(CAZy != "")
### Cazymes_expanded - open the semicolon separation and annotate more details
data$cazymes_expanded <- data$cazymes %>% separate_rows(CAZy, sep = ";")
data$cazymes_expanded$annot <- lapply(data$cazymes_expanded$CAZy, function(x) getCAZyDetails(x))
data$cazymes_expanded$CAZyClass <- unlist(lapply(data$cazymes_expanded$annot, "[[", 1))
data$cazymes_expanded$CAZyClassNumber <- unlist(lapply(data$cazymes_expanded$annot, "[[", 2))
data$cazymes_expanded$CAZyText <- unlist(lapply(data$cazymes_expanded$annot, "[[", 3))
data$cazymes_expanded <- data$cazymes_expanded %>% select(-annot)
### kegg_proteins - a list of all proteins with KEGG annotation and which bin they belong to + quantitative data
data$kegg_proteins <- data$master_tbl %>%
select(Bin, KEGG, EC, KO, contains("metaT_TPM"), contains("metaP_LFQ")) %>%
filter(KEGG != "")
### kegg_proteins_expanded - open the semicolon separation and annotate with more detailed KEGG info
data$kegg_proteins_expanded <- data$kegg_proteins %>% separate_rows(KEGG, sep = ";")
data$kegg_proteins_expanded <- data$kegg_proteins_expanded %>%
left_join(ko_tbl %>% select(ID, Name, Parent.term), by = c("KEGG" = "ID"))
#Some extra columns for counting purposes
data$master_tbl <- data$master_tbl %>%
mutate(metaP_expressed = if_any(contains('metaP_LFQ'), ~ !is.na(.))) %>%
mutate(metaT_expressed = if_any(contains('metaT_TPM'), ~ !is.na(.)))
#Update color swatches
setThemeAndCalculateColors('flat')
#Update UI based on data
#CAZy-page
updateSelectInput(session, "cazyGroup", choices = unique(data$cazymes_expanded$CAZyText))
updateSelectInput(session, "sample_MT", choices = unique(colnames(data$master_tbl %>% select(contains("metaT_TPM")))))
updateSelectInput(session, "sample_MP", choices = unique(colnames(data$master_tbl %>% select(contains("metaP_LFQ")))))
updateSelectInput(session, "cazyPlot_heatmap_binSelector", choices = unique(data$contig_tbl$Bin))
#KEGG-page
updateSelectInput(session, "keggGroup", choices = unique(data$kegg_proteins_expanded$Parent.term))
updateSelectInput(session, "kegg_sample_MT", choices = unique(colnames(data$master_tbl %>% select(contains("metaT_TPM")))))
updateSelectInput(session, "kegg_sample_MP", choices = unique(colnames(data$master_tbl %>% select(contains("metaP_LFQ")))))
updateSelectInput(session, "keggPlot_heatmap_binSelector", choices = unique(data$contig_tbl$Bin))
#MCF-page
#This will be updated after mcf calculation due to different content for modules
#MAG-Page
if (do_checkM)
updateSliderInput(session, "MAG_slider_contamination", max = ceiling(max(data$contig_tbl$Contamination)))
#Enable analysis
js$enableTab("MAGs")
js$enableTab("CAZy")
js$enableTab("KEGG")
if ('KO' %in% colnames(data$master_tbl))
js$enableTab("KEGG-Modules")
else
showNotification("No 'KO'-column detected. KEGG-Modules analysis disabled", type = "warning", duration = NULL)
}
#After data is loaded and tables are updated, we need to calculate a color swatch suitable for the
#number of bins in the data set.
#
# NB: Note the <<- to change the parent variable 'global_swatch'
#
setThemeAndCalculateColors <- function(theme_string) {
print("recalulate color swatch")
#Set base theme. This can be tweaked by some UI options if wanted
ggthemr(theme_string, type="outer", layout="scientific", spacing=2, text_size = 14)
n_cols <- length(unique(data$contig_tbl$Bin))
#Provides 8 usable bin-colors. Nine in swatch but first one is special
global_swatch <<- as.vector(swatch())
if (n_cols > 8 & n_cols < 20) {
#Provides 19 + 1 special
global_swatch <<- c(global_swatch, brewer.pal(11, "BrBG"))
}
if (n_cols > 19 & n_cols < 71) {
#Provides 70 + 1 special
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
global_swatch <<- c("#555555", unique(unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))))
}
if (n_cols > 70 & n_cols < 503) { #This is becoming a headache!
#Provides 502 + 1 special
global_swatch <<- c("#555555", colors(distinct = TRUE))
}
if (n_cols > 502) { #AAAhh!...What to do here? There may be datasets with >1500 bins
#Not possible to generate this many unique colors
#Not possible to use ggplot with not-unique colors
#Not possible to draw the plot at all
#Not possible to interpret the plot anyhow
#Can we filter to the most abundant MAGs?
#Can we say that ViMO do not support graphing this many MAGs? The users are referred to manual analysis in R.
print(">502 Bins in the data. R, ggplot and ViMO have difficultis with that any. R will likely crash...")
}
names(global_swatch) <<- c(NA, sort(unique(data$contig_tbl$Bin)))
set_swatch(global_swatch)
print(paste0("New theme set:", theme_string, ", Color count:", length(global_swatch)))
}
clearAllData <- function() {
print("Clearing all data...")
data$masterfile = NULL
data$contigfile = NULL
data$master_tbl = NULL
data$contig_tbl = NULL
data$cazymes = NULL
data$cazymes_expanded = NULL
data$kegg_proteins = NULL
data$kegg_proteins_expanded = NULL
global_swatch <<- NULL
do_metaP <<- TRUE
do_metaT <<- TRUE
do_lineage <<- TRUE
do_checkM <<- TRUE
js$disableTab("MAGs")
js$disableTab("CAZy")
js$disableTab("KEGG")
js$disableTab("KEGG-Modules")
}
# observeEvent(input$STOPButton, {
# stopApp()
# })
#The load buttons trigger a change in the main data object
observeEvent(input$loadButton, {
clearAllData()
print("load_button")
print(paste("masterfile:", input$masterfile$datapath))
print(paste("contigfile:", input$contigfile$datapath))
#check that the two files are set. Otherwise pop up message to user
if (is.null(input$masterfile$datapath)) {
showNotification("No Masterfile set", type = "error")
return()
}
if (is.null(input$contigfile$datapath)) {
showNotification("No Contigfile set", type = "error")
print("no contig")
return()
}
showNotification("Loading...", type = "message", duration = 2)
data$master_tbl <- read.table(input$masterfile$datapath, header=T, sep="\t", quote="", colClasses=c(Bin="character"))
data$contig_tbl <- read.table(input$contigfile$datapath, header=T, sep="\t", quote="", colClasses=c(Bin="character"))
data$masterfile <- paste(input$masterfile$name, '(uploaded)')
data$contigfile <- paste(input$contigfile$name, '(uploaded)')
showNotification("Files loaded. Calculating subsets...", type = "message", duration = 5)
updateTables()
})
observeEvent(input$loadButton_example, {
print("example_button")
data$master_tbl <- read.table(example_masterfile, header=T, sep="\t", quote="", colClasses=c(Bin="character"))
data$contig_tbl <- read.table(example_contigfile, header=T, sep="\t", quote="", colClasses=c(Bin="character"))
data$masterfile <- paste(example_masterfile, '(example)')
data$contigfile <- paste(example_contigfile, '(example)')
updateTables()
})
###################### Files summary ############################################
output$filesSummary <- renderUI({
print("files summary")
#Calculate some numbers
metaG_count <- 0
metaP_count <- 0
metaT_count <- 0
cazyme_count <- 0
kegg_count <- 0
mag_count <- 0
if (!is.null(data$masterfile)) {
metaG_count <- nrow(data$master_tbl)
if (do_metaP)
metaP_count <- nrow(data$master_tbl %>% filter(metaP_expressed == TRUE))
if (do_metaT)
metaT_count <- nrow(data$master_tbl %>% filter(metaT_expressed == TRUE))
cazyme_count <- nrow(data$cazymes)
kegg_count <- nrow(data$kegg_proteins)
mag_count <- nrow(data$contig_tbl %>% select(Bin) %>% distinct())
}
HTML(paste(
paste("<b>Masterfile:</b><code>", data$masterfile,"</code>"),
paste("<b>Contigfile:</b><code>", data$contigfile,"</code>"),
paste("<b>MAGs:</b><code>", mag_count,"</code>"),
paste("<b>Genes:</b><code>", metaG_count,"</code>"),
paste("<b>Transcripts quantified:</b><code>", metaT_count,"</code>"),
paste("<b>Proteins quantified:</b><code>", metaP_count,"</code>"),
paste("<b>CAZymes:</b><code>", cazyme_count,"</code>"),
paste("<b>KEGG-annotated genes:</b><code>", kegg_count,"</code>"),
paste("<br/>", ifelse(mag_count > 49 | metaG_count > 100000, "<h3><b>NB: You may experience slowness in the App with this many MAGs/genes. It will work, just slow.</b></h3>", "")),
sep = '<br/>'))
})
###################### CAZy Plots ###############################################
#Title
output$cazyCaption <- renderText({ input$cazyGroup })
#Legend
output$cazyPlot_legend <- renderPlot({
group <- input$cazyGroup
dataSubset <- data$cazymes_expanded[data$cazymes_expanded$CAZyText == group,]
colors_to_use <- global_swatch[unique(dataSubset$Bin)]
tmp_plot <- ggplot(dataSubset) + geom_bar(mapping = aes(x=CAZyClassNumber, fill = Bin)) + scale_fill_manual(values=colors_to_use) + theme(legend.position='bottom')
legend <- get_legend(tmp_plot)
grid.newpage()
grid.draw(legend)
}, height = function() {
round(length(data$cazymes_expanded %>%
filter(CAZyText == input$cazyGroup) %>%
select(Bin) %>% distinct() %>% deframe()) * 2.3 + 30)
})
#MetaG
output$cazyPlot_MG <- renderPlot({
showNotification("Loading...", type = "message", duration = 2)
group <- input$cazyGroup
dataSubset <- data$cazymes_expanded[data$cazymes_expanded$CAZyText == group,]
dataSubset <- dataSubset %>% mutate(CAZyClassNumber = factor(CAZyClassNumber, levels = c(1:300, 'cohesin', 'dockerin', 'SLH')))
plot <- ggplot(dataSubset) +
geom_bar(mapping = aes(x=CAZyClassNumber, fill = Bin)) +
scale_fill_manual(values=global_swatch) +
scale_y_continuous(breaks = function(x) pretty(x, n=3)) +
labs(title = "", y="Gene count", x="CAZy class number") +
theme(legend.position='none', axis.text.x = element_text(angle = 70, vjust = 1, hjust=1))
ggsave("cazyPlot_MG.pdf", plot)
plot
})
output$download_cazyPlot_MG <- downloadHandler(
filename = function() {"cazyPlot_MG.pdf"},
content = function(file) {file.copy("cazyPlot_MG.pdf", file, overwrite=TRUE)}
)
#MetaT
output$cazyPlot_MT <- renderPlot({
print("CAZyPlot_MT rendering")
group <- input$cazyGroup
if (!do_metaT) {
print("returning...")
return()
}
dataSubset <- data$cazymes_expanded %>%
select(-contains("metaP_LFQ")) %>%
filter(CAZyText == group) %>%
pivot_longer(cols = contains("metaT_TPM"), names_to="Sample", values_to="Value") %>%
filter(Sample == input$sample_MT)
dataSubset <- dataSubset %>%
group_by(CAZyClassNumber, Bin) %>%
summarise(y_axis = sum(Value, na.rm = TRUE))
dataSubset <- dataSubset %>% mutate(CAZyClassNumber = factor(CAZyClassNumber, levels = c(1:300, 'cohesin', 'dockerin', 'SLH')))
plot <- ggplot(dataSubset) +
geom_col(mapping = aes(x=CAZyClassNumber, fill = Bin, y=y_axis)) +
scale_fill_manual(values=global_swatch) +
scale_y_continuous(breaks = function(x) pretty(x, n=3)) +
labs(title = "", y="Transcripts per million (tpm)", x="CAZy class number") +
theme(legend.position='none', axis.text.x = element_text(angle = 70, vjust = 1, hjust=1))
ggsave("cazyPlot_MT.pdf", plot)
plot
})
output$download_cazyPlot_MT <- downloadHandler(
filename = function() {"cazyPlot_MT.pdf"},
content = function(file) {file.copy("cazyPlot_MT.pdf", file, overwrite=TRUE)}
)
#MetaP
output$cazyPlot_MP <- renderPlot({
print("CAZyPlot_MP rendering")
group <- input$cazyGroup
if (!do_metaP) {
print("returning...")
return()
}
dataSubset <- data$cazymes_expanded %>%
select(-contains("metaT_TPM")) %>%
filter(CAZyText == group) %>%
pivot_longer(cols = contains("metaP_LFQ"), names_to="Sample", values_to="Value") %>%
filter(Sample == input$sample_MP)
if (input$yaxis_MP == "Summed LFQ") {
dataSubset <- dataSubset %>%
group_by(CAZyClassNumber, Bin) %>%
summarise(y_axis = sum(Value, na.rm = TRUE))
}
if (input$yaxis_MP == "Protein count") {
dataSubset <- dataSubset %>%
group_by(CAZyClassNumber, Bin) %>%
summarise(y_axis = sum(!is.na(Value)))
}
dataSubset <- dataSubset %>% mutate(CAZyClassNumber = factor(CAZyClassNumber, levels = c(1:300, 'cohesin', 'dockerin', 'SLH')))
plot <- ggplot(dataSubset) +
geom_col(mapping = aes(x=CAZyClassNumber, fill = Bin, y=y_axis)) +
scale_fill_manual(values=global_swatch) +
scale_y_continuous(breaks = function(x) pretty(x, n=3)) +
labs(title = "", y=input$yaxis_MP, x="CAZy class number") +
theme(legend.position='none', axis.text.x = element_text(angle = 70, vjust = 1, hjust=1))
ggsave("cazyPlot_MP.pdf", plot)
plot
})
output$download_cazyPlot_MP <- downloadHandler(
filename = function() {"cazyPlot_MP.pdf"},
content = function(file) {file.copy("cazyPlot_MP.pdf", file, overwrite=TRUE)}
)
#CAZy-heatmaps, updates based on selected bin
output$cazyPlot_heatmap_MT <- renderPlot({
print("CAZyPlot_heatmap_MT rendering")
group <- input$cazyGroup
if (!do_metaT) {
print("returning...")
return()
}
selected_data <- data$cazymes_expanded %>%
select(-contains("metaP_LFQ")) %>%
filter(CAZyText == group, Bin == input$cazyPlot_heatmap_binSelector) %>%
pivot_longer(cols = contains("metaT_TPM"), names_to = "Sample", values_to = "Value") %>%
group_by(CAZyClassNumber, Sample) %>%
summarise(Value = sum(Value, na.rm = TRUE))
n_cc <- selected_data %>% select(CAZyClassNumber) %>% distinct() %>% deframe()
if (length(n_cc) == 0) {
ggplot() + annotate("text", x=4, y=25, size=8, label="Not enough CAZy data for the selected bin") + theme_void()
} else {
#Sort numbers alphabetically
selected_data <- selected_data %>% mutate(CAZyClassNumber = factor(CAZyClassNumber, levels = c(1:300, 'cohesin', 'dockerin', 'SLH')))
plot <- ggplot(selected_data) +
geom_tile(aes(y=CAZyClassNumber, x=Sample, fill=Value), color="white") +
labs(title = paste0("Abundance of ", group,", summed transcripts per million (tpm)")) +
theme(legend.position = 'top', axis.text.x = element_text(angle = 70, vjust = 1, hjust=1)) +
guides(fill = guide_colourbar(title="", barwidth = 20, barheight = 0.5)) +
scale_y_discrete(position = "right")
ggsave("cazyPlot_MT_heatmap.pdf", plot)
plot
}
},width = function() {
ncol(data$cazymes_expanded %>% select(contains("metaT_TPM"))) * 50 + 200
}, height = function() {
round(length(data$cazymes_expanded %>%
filter(CAZyText == input$cazyGroup, Bin == input$cazyPlot_heatmap_binSelector) %>%
select(CAZyClassNumber) %>% distinct() %>% deframe()) * 15 + 250)
})
output$download_cazyPlot_MT_heatmap <- downloadHandler(
filename = function() {"cazyPlot_MT_heatmap.pdf"},
content = function(file) {file.copy("cazyPlot_MT_heatmap.pdf", file, overwrite=TRUE)}
)
output$cazyPlot_heatmap_MP <- renderPlot({
print("CAZyPlot_heatmap_MP rendering")
group <- input$cazyGroup
if (!do_metaP) {
print("returning...")
return()
}
selected_data <- data$cazymes_expanded %>%
select(-contains("metaT_TPM")) %>%
filter(CAZyText == group, Bin == input$cazyPlot_heatmap_binSelector) %>%
pivot_longer(cols = contains("metaP_LFQ"), names_to = "Sample", values_to = "Value") %>%
group_by(CAZyClassNumber, Sample) %>%
summarise(Value = sum(Value, na.rm = TRUE))
n_cc <- selected_data %>% select(CAZyClassNumber) %>% distinct() %>% deframe()
if (length(n_cc) == 0) {
ggplot() + annotate("text", x=4, y=25, size=8, label="Not enough CAZy data for the selected bin") + theme_void()
} else {
#Sort numbers alphabetically
selected_data <- selected_data %>% mutate(CAZyClassNumber = factor(CAZyClassNumber, levels = c(1:300, 'cohesin', 'dockerin', 'SLH')))
plot <- ggplot(selected_data) +
geom_tile(aes(y=CAZyClassNumber, x=Sample, fill=Value), color="white") +
labs(title = paste0("Abundance of ", group,", summed protein LFQ")) +
theme(legend.position = 'top', axis.text.x = element_text(angle = 70, vjust = 1, hjust=1)) +
guides(fill = guide_colourbar(title="", barwidth = 20, barheight = 0.5)) +
scale_y_discrete(position = "right")
ggsave("cazyPlot_MP_heatmap.pdf", plot)
plot
}
},width = function() {
ncol(data$cazymes_expanded %>% select(contains("metaP_LFQ"))) * 50 + 200
},height = function() {
round(length(data$cazymes_expanded %>%
filter(CAZyText == input$cazyGroup, Bin == input$cazyPlot_heatmap_binSelector) %>%
select(CAZyClassNumber) %>% distinct() %>% deframe()) * 15 + 250)
})
output$download_cazyPlot_MP_heatmap <- downloadHandler(
filename = function() {"cazyPlot_MP_heatmap.pdf"},
content = function(file) {file.copy("cazyPlot_MP_heatmap.pdf", file, overwrite=TRUE)}
)
###################### KEGG Plots ###############################################
output$keggCaption <- renderText({ input$keggGroup })
#Plots, updates on selected function in combo-box
output$keggPlot_legend <- renderPlot({
colors_to_use <- global_swatch[unique(data$kegg_proteins_expanded$Bin)]
tmp_plot <- ggplot(data$kegg_proteins_expanded) + geom_bar(mapping = aes(x=KEGG, fill = Bin)) + scale_fill_manual(values=colors_to_use) + theme(legend.position='bottom')
legend <- get_legend(tmp_plot)
grid.newpage()
grid.draw(legend)
}, height = function() {
round(length(data$kegg_proteins_expanded %>%
filter(Parent.term == input$keggGroup) %>%
select(Bin) %>% distinct() %>% deframe()) * 2.3 + 60)
})
#KEGGPlot - MG
output$keggPlot_MG <- renderPlot({
showNotification("Loading...", type = "message", duration = 2)
dataSubset <- data$kegg_proteins_expanded %>% filter(Parent.term == input$keggGroup)
plot <- ggplot(dataSubset) +
geom_bar(mapping = aes(x=KEGG, fill = Bin)) +
scale_fill_manual(values=global_swatch) +
scale_y_continuous(breaks = function(x) pretty(x, n=3)) +
labs(title = "", y="Gene count", x="KEGG Pathway") +
theme(legend.position = 'none', axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
ggsave("keggPlot_MG.pdf", plot)
plot
})
output$download_keggPlot_MG <- downloadHandler(
filename = function() {"keggPlot_MG.pdf"},
content = function(file) {file.copy("keggPlot_MG.pdf", file, overwrite=TRUE)}
)
#KEGGPlot - MT
output$keggPlot_MT <- renderPlot({
print("KeggPlot_MT rendering")
group <- input$keggGroup
if (!do_metaT) {
print("returning...")
return()
}
dataSubset <- data$kegg_proteins_expanded %>%
select(-contains("metaP_LFQ")) %>%
filter(Parent.term == input$keggGroup) %>%
pivot_longer(cols = contains("metaT_TPM"), names_to="Sample", values_to="Value") %>%
filter(Sample == input$kegg_sample_MT)
dataSubset <- dataSubset %>%
group_by(KEGG, Bin) %>%
summarise(y_axis = sum(Value, na.rm = TRUE))
plot <- ggplot(dataSubset) +
geom_col(mapping = aes(x=KEGG, fill = Bin, y=y_axis)) +
scale_fill_manual(values=global_swatch) +
scale_y_continuous(breaks = function(x) pretty(x, n=3)) +
labs(title = "", y="Transcripts per million (tpm)", x="KEGG Pathway") +
theme(legend.position='none', axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
ggsave("keggPlot_MT.pdf", plot)