/
app.R
1077 lines (1014 loc) · 53.9 KB
/
app.R
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library(shiny)
library(shinyjs, lib.loc = "../r-packages/")
library(ggplot2, lib.loc = "../r-packages/")
library(cowplot, lib.loc = "../r-packages/")
library(data.table, lib.loc = "../r-packages/")
library(ggpubr, lib.loc = "../r-packages/")
library(mimosa, lib.loc ="../r-packages/")
library(readr, lib.loc = "../r-packages/")
library(RColorBrewer) #, lib.loc = "../r-packages/")
library(shinyBS, lib.loc = "../r-packages/")
options(datatable.webSafeMode = TRUE, scipen = 20000, stringsAsFactors = F, shiny.usecairo = F, shiny.maxRequestSize=300*1024^2,
show.error.locations=TRUE, shiny.trace = F)
theme_set(theme_cowplot() + theme(text = element_text(family = 'Helvetica')))
#library(ggpubr)
microbiome_data_upload = function(){
fluidPage(
tags$head(tags$style("#file1{color: gray } ")),
tags$tr(class = "mainContainer",
tags$td(
h4(get_text("microbiome_header"), width='100%'),
#,tags$a(tags$img(src = "help.png", border = 0), href = "https://www.github.com/borenstein-lab/", target = "_blank"), width = '100%')
p(get_text("microbiome_description")),
fluidRow(
column(radioButtons("file1_type", get_text("database_title"), choices = get_text("database_choices"), width="100%"), width = 8),
column(tipify(tags$img(src = "help.png", border = 0), title = get_text("microbiome_tooltip"), placement = "right"), width = 4)),
fluidRow(
column(fileInput("file1", get_text("microbiome_input_title"),
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv",
".tsv",
"text/tsv"), width = '430px'), width = 8),
column(tags$a(tags$img(src = "example.png", border = 0), href = "test_seqs.txt", target = "_blank"), width = 4)) #,
# checkboxInput("metagenomeOpt", label=get_text("metagenome_option"), width = '100%'),
#p(get_text("metagenome_description")),
# fluidRow(
# column(radioButtons("metagenome_format", get_text("metagenome_title"), choices = get_text("metagenome_options"), width="100%"), width = 8),
# column(tipify(tags$img(src = "help.png", border = 0), title = get_text("metagenome_tooltip"), placement = "right"), width = 4)),
# fluidRow(
# column( fileInput("metagenome", get_text("metagenome_input_title"),
# multiple = FALSE,
# accept = c("text/csv",
# "text/comma-separated-values,text/plain",
# ".csv",
# ".tsv",
# "text/tsv"), width = '430px'), width = 8),
# column(tags$a(tags$img(src = "example.png", border = 0), href = "test_metagenome.txt", target = "_blank"), width = 4))
#) )#,
# disabled(checkboxInput("metagenome_use", get_text("metagenome_use_option"),
# ))
)
),
id = "microbiome_section" , tags$style(type = "text/css", "#microbiome_section { horizontal-align: left; width: 100%}" ) #tags$style(".mainContainer { border: 2px black; }"),
)
}
metabolome_data_upload = function(){
fluidPage(
h4(get_text("metabolome_header")),
#tags$a(tags$img(src = "help.png", border = 0), href = "https://www.github.com/borenstein-lab/", target = "_blank"), id = "metabolome", width='100%')
p(get_text("metabolome_description")),
fluidRow(
column(radioButtons("metType", label = get_text("met_type_title"), choices = get_text("met_type_choices"), selected = get_text("selected_met_type"), width="100%"), width = 8),
column(tipify(tags$img(src = "help.png", border = 0), title = get_text("metabolome_tooltip"), placement = "right"), width = 4), width='100%'),
fluidRow(
column(checkboxInput("logTransform", get_text("metabolome_norm_description"), width="100%"), width = 8),
column(tipify(tags$img(src = "help.png", border = 0), title = get_text("metabolome_transform_tooltip"), placement = "right"), width = 4)
),
fluidRow(
column(fileInput("file2", get_text("metabolome_upload_title"),
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv",
".tsv",
"text/tsv"), width = '430px'), width = 8),
column(tags$a(tags$img(src = "example.png", border = 0), href = "test_mets.txt", target = "_blank"), width = 4)),
id = "metabolome_section", tags$style(type = "text/css", "#metabolome_section { horizontal-align: left; width: 100%}" ))
}
network_settings = function(){
fluidPage(
h4(get_text("network_header"), width='100%'),
#tags$a(tags$img(src = "help.png", border = 0), href = "https://www.github.com/borenstein-lab/", target = "_blank"), id = "genome", width='100%'),
p(get_text("network_description")),
fluidRow(
column(radioButtons("ref_choices", label = get_text("source_title"), choices = get_text("source_choices"), selected = get_text("source_choices")[1], width="100%"), width = 8),
column(tipify(tags$img(src = "help.png", border = 0), title = get_text("network_tooltip"), placement = "right"), width = 4)
),
fluidRow(
column(numericInput("simThreshold", get_text("sim_title"), value = 0.99, min=0.8, max = 1, step = 0.01, width='430px'), width = 8), #, width = '100%'),
column(tipify(tags$img(src = "help.png", border = 0), title = get_text("network_mapping_tooltip"), placement = "right"), width = 4)
),
fluidRow(
column(fileInput("netAdd", get_text("net_mod_input_title"), multiple = FALSE, accept = c("text/csv",
"text/comma-separated-values,text/plain",".csv",
".tsv",
"text/tsv"), width = '550px'), width = 8), #, #, width = '100%') #,
column(tags$a(tags$img(src = "example.png", border = 0), href = "test_netAdd_species_rxns_KEGG_clean.txt", target = "_blank"), width = 4)
)
# radioButtons("modelTemplate", "Metabolic model template", choices = c("Generic KEGG metabolic model", "AGORA metabolic models (recommended)")),
#disabled(radioButtons("closest", get_text("closest_title"), choices = get_text("closest_options"))),
#checkboxInput("gapfill", get_text("gapfill_option"), width = '100%')
, id = "network_section"
)
}
algorithm_settings = function(){
fluidPage(
h4(get_text("algorithm_header"), id = "algorithm"),
p(get_text("algorithm_description")),
#radioButtons("contribType", get_text("stat_title"), choices = get_text("stat_choices")),
fluidRow(
column( radioButtons("regType", get_text("regression_title"), choices = get_text("regression_choices"), width="100%"), width = 8),
column(tipify(tags$img(src = "help.png", border = 0), title = get_text("algorithm_tooltip"), placement = "right"), width = 4)
),
fluidRow(
column( checkboxInput("compare_only", get_text("skip_contribs_option"), width="100%"), width = 8),
column(tipify(tags$img(src = "help.png", border = 0), title = get_text("skip_contribs_tooltip"), placement = "right"), width = 4)
),
id = "algorithm_section"
)
}
output_settings = function(){
fluidPage(
h4("Output", id = "outputResults"),
#radioButtons("returnType", "Select desired output:", choices = c("Summarized contribution results", "Full verbose data output")),
checkboxInput("netVis", "Generate interactive metabolic network visualization of results")
)
}
###### Core app function
run_pipeline = function(input_data, configTable, analysisID){
withProgress(message = "Running MIMOSA!", {
if(is.character(input_data)){ #Error occurred
stop(paste0("Problem reading files: ", input_data)) #Print error message
}
#process arguments
species = input_data$species
if(configTable[V1 == "file1_type", V2] != get_text("database_choices")[4]) species[,OTU:=as.character(OTU)]
mets = input_data$mets
#print(configTable)
configTable[,V2:=as.character(V2)]
#print(configTable)
configTable = check_config_table(configTable, app = T)
incProgress(2/10, detail = "Building metabolic model")
validate(need(!(configTable[V1=="file1_type", V2] %in% get_text("database_choices")[4:5] &
configTable[V1=="ref_choices", V2 != get_text("source_choices")[1]]), "Error: Only KEGG metabolic model (network option 1) can be used with KEGG Ortholog data"))
print(input_data$netAdd)
network_results = build_metabolic_model(species, configTable, netAdd = input_data$netAdd) #, input_data$netAdd) #input_data$geneAdd,
network = network_results[[1]]
species = network_results[[2]]
#Allow for modifying this for AGORA
# if(!is.null(input_data$metagenome) & configTable[V1=="file1_type", V2!=get_text("database_choices")[4]]){
# #If we are doing a comparison of the species network and the metagenome network
# #Metagenome data
# metagenome_network = build_metabolic_model(input_data$metagenome, configTable)
# }
## If it's ASVs->greengenes OTUs, read in the taxonomy table and merge
if(configTable[V1 == "file1_type", V2==get_text("database_choices")[1]] & configTable[V1 == "ref_choices", V2== get_text("source_choices")[1]] & configTable[V1 == "compare_only", V2==F]){
taxonomy = fread("data/picrustGG/99_otu_taxonomy.txt", header = F, col.names = c("OTU", "TaxString"), sep = "\t")
taxonomy = taxonomy[OTU %in% species[,OTU]]
taxonomy[,TaxStringShort:=gsub(".*; f__", "f__", TaxString)]
taxonomy[,NewOTU_ID:=paste0(OTU, "_", TaxStringShort)]
species = merge(species, taxonomy[,list(OTU, NewOTU_ID)], by = "OTU", all.x = T)
species[,OTU:=NULL]
species[,OTU:=NewOTU_ID]
species[,NewOTU_ID:=NULL]
network = merge(network, taxonomy[,list(OTU, NewOTU_ID)], by = "OTU", all.x = T)
network[,OTU:=NULL]
network[,OTU:=NewOTU_ID]
network[,NewOTU_ID:=NULL]
rm(taxonomy)
}
if(configTable[V1=="metType", V2 ==get_text("met_type_choices")[2]]){
mets = map_hmdb_to_kegg_webchem(mets)
} else if(configTable[V1=="metType", V2 == get_text("met_type_choices")[3]]){
mets = map_to_kegg_webchem(mets)
}
incProgress(1/10, detail = "Calculating metabolic potential and fitting metabolite models")
#Get CMP scores
if("rxnEdit" %in% configTable[,V1]){
rxn_param = T
cat("Will refine reaction network\n")
} else rxn_param = F
if(configTable[V1 == "rankBased", V2 == "TRUE"]){
rank_based = T
cat("Will use rank-based/robust regression\n")
if("rank_type" %in% configTable[,V1]){
rank_type = configTable[V1=="rank_type", V2]
} else {
rank_type = "rfit"
}
cat(paste0("Regression type is ", rank_type, "\n"))
} else rank_based = F
if(configTable[V1=="file1_type", V2==get_text("database_choices")[4]]){
no_spec_param = T
humann2_param = F
rel_abund_param = T
} else if(configTable[V1=="file1_type", V2==get_text("database_choices")[5]]){
no_spec_param = F
humann2_param = T
rel_abund_param = F
cat("Humann2 format\n")
} else {
no_spec_param = F
humann2_param = F
rel_abund_param = T
}
# if("revRxns" %in% configTable[,V1]){ #Whether to add reverse of reversible-annotated rxns - mainly for agora networks
# network = add_rev_rxns(network, sameID = T) # Give reverse the same rxn ID
# cat("Will add reverse of reversible reactions\n")
# }
if("met_transform" %in% configTable[,V1]){
met_transform = configTable[V1=="met_transform", V2]
cat(paste0("Will transform metabolite values, transform is ", met_transform))
} else met_transform = ""
#indiv_cmps = get_cmp_scores_kos(species, network) #Use KO abundances instead of species abundances to get cmps
mets_melt = melt(mets, id.var = "compound", variable.name = "Sample")
if(met_transform != ""){
mets_melt = transform_mets(mets_melt, met_transform)
}
if(rxn_param){
cmp_mods = fit_cmp_net_edit(network, species, mets_melt, manual_agora = agora_param, rank_based = rank_based)
network = cmp_mods[[3]] #Revised network
indiv_cmps = cmp_mods[[4]]
#Will have to report nice summary of rxns removed, rxns direction switched, etc
} else {
indiv_cmps = get_species_cmp_scores(species, network, normalize = !rxn_param, leave_rxns = rxn_param, manual_agora = F, kos_only = no_spec_param, humann2 = humann2_param, relAbund = rel_abund_param)
indiv_cmps = indiv_cmps[compound %in% mets[,compound]]
if(nrow(indiv_cmps)==0) stop("Error: No overlap between predicted and measured metabolites")
if(nrow(indiv_cmps[,length(CMP), by=list(compound, Species, Sample)][V1 != 1]) > 0){ #If there is not just 1 value for each data feature we will have problems
stop("Error calculating CMP scores - duplicates in data")
}
cmp_mods = fit_cmp_mods(indiv_cmps, mets_melt, rank_based = rank_based, rank_type = rank_type)
}
#print(cmp_mods[[1]])
if(!configTable[V1 == "compare_only", V2==T]){
incProgress(2/10, detail = "Calculating microbial contributions")
#Get low-abundance species to remove
if(!humann2_param){
spec_dat = melt(species, id.var = "OTU", variable.name = "Sample")[,list(value/sum(value), OTU), by=Sample] #convert to relative abundance
bad_spec = spec_dat[,list(length(V1[V1 != 0])/length(V1), max(V1)), by=OTU]
bad_spec = bad_spec[V1 < 0.2 & V2 < 0.1, OTU] #Never higher than 10% and absent in at least 80% of samples
print(bad_spec)
} else bad_spec = NULL
#print(indiv_cmps)
var_shares = calculate_var_shares(indiv_cmps, met_table = mets_melt, model_results = cmp_mods, config_table = configTable, species_merge = bad_spec, signif_threshold = 0.1)
#print(var_shares)
## If nothing significant was analyzed, behave as if compare_only were selected
if(is.null(var_shares)){
configTable[V1 == "compare_only", V2:="TRUE"]
}
print("Var shares species")
if(!is.null(var_shares)) print(var_shares[,unique(Species)])
} else {
var_shares = NULL
}
#Add species/rxn info
#print(species)
#print(network)
cmp_summary = get_cmp_summary(species, network, normalize = !rxn_param, manual_agora = F, kos_only = no_spec_param, humann2 = humann2_param,
met_subset = cmp_mods[[1]][!is.na(Rsq) & Rsq != 0,compound], contrib_sizes = var_shares)
#print(cmp_summary)
cmp_mods[[1]] = merge(cmp_mods[[1]], cmp_summary$CompLevelSummary, by = "compound", all.x = T)
#print(var_shares)
#print(cmp_summary$SpeciesLevelSummary[,list(compound, Species)])
#print(cmp_mods[[1]])
if(length(cmp_summary) > 1) var_shares = merge(var_shares, cmp_summary$SpeciesLevelSummary, by = c("compound", "Species"), all.x = T)
cmp_mods[[1]][,compound:=as.character(compound)]
indiv_cmps[,compound:=as.character(compound)]
if(!is.null(var_shares)){
var_shares[,compound:=as.character(compound)]
var_shares[,Species:=as.character(Species)]
var_shares[,MetaboliteName:=met_names(as.character(compound))]
var_shares[is.na(MetaboliteName), MetaboliteName:=compound]
var_shares = var_shares[,list(compound, MetaboliteName, Rsq, VarDisp, ModelPVal, ModelPValFDRAdj, Slope, Intercept, Species, VarShare, NumSynthGenes, SynthGenes, NumDegGenes, DegGenes)]
}
#shinyjs::logjs(devtools::session_info())
#Order dataset for plotting
incProgress(1/10, detail = "Making CMP-Metabolite plots")
#print(indiv_cmps[compound %in% mets_melt[,compound]])
CMP_plots = plot_all_cmp_mets(cmp_table = indiv_cmps, met_table = mets_melt, mod_results = cmp_mods[[1]])
if(configTable[V1 == "compare_only", V2 != TRUE]){
incProgress(1/10, detail = "Making metabolite contribution plots")
#print(var_shares)
comp_list = var_shares[!is.na(VarShare)][VarShare != 0, unique(as.character(compound))]
comp_list = comp_list[!comp_list %in% var_shares[Species == "Residual" & VarShare == 1, as.character(compound)]]
all_contrib_taxa = var_shares[compound %in% comp_list & !is.na(VarShare) & Species != "Residual", sort(as.character(unique(Species)))]
#alphabetical order please
#print(all_contrib_taxa)
getPalette = colorRampPalette(brewer.pal(12, "Paired"))
if(var_shares[compound %in% comp_list & Species != "Residual", length(unique(Species[VarShare != 0])), by=compound][,any(V1 > 10)]){
contrib_color_palette = c("gray", getPalette(length(all_contrib_taxa))) #"black", #Work with plotting function filters
names(contrib_color_palette) = c( "Other", all_contrib_taxa) #"Residual",
} else {
contrib_color_palette = getPalette(length(all_contrib_taxa)) #"black",
names(contrib_color_palette) = all_contrib_taxa #"Residual",
}
#print("Color palette")
#print(contrib_color_palette)
met_contrib_plots = lapply(comp_list, function(x){
#print(x)
if(is.na(met_names(x))){
met_id = x
} else { met_id = met_names(x)}
plot_contributions(var_shares, met_id, metIDcol = "MetaboliteName", color_palette = contrib_color_palette, include_residual = F, merge_threshold = 0.01)
})
} else {
met_contrib_plots = NULL
}
incProgress(1/10, detail = "Saving results")
#print(analysisID)
dir.create(path = paste0("www/analysisResults/", analysisID), showWarnings = T)
print(dir.exists(analysisID))
for(i in 1:length(CMP_plots)){
#print(paste0("www/analysisResults/", analysisID, "/", analysisID, "_", names(CMP_plots)[i], ".png"))
if(!identical(CMP_plots[[i]], NA)){
save_plot(CMP_plots[[i]], file = paste0("www/analysisResults/", analysisID, "/", analysisID, "_", names(CMP_plots)[i], ".png"), base_width = 2, base_height = 2)
}
}
if(!configTable[V1 == "compare_only", V2==T]){
for(i in 1:length(met_contrib_plots)){
#print(comp_list[i])
if(!is.null(met_contrib_plots[[i]])){
save_plot(met_contrib_plots[[i]] + guides(fill = F), file = paste0("www/analysisResults/", analysisID, "/", analysisID, "_", comp_list[i], "_contribs.png"), base_width = 2, base_height = 2)
}
}
# if(!exists("contrib_legend")){ #Get legend from first non-nulll compound
#
# }
# print("Making legend")
# print(all_contrib_taxa)
# print(contrib_color_palette)
leg_dat = data.table(V1 = factor(names(contrib_color_palette), levels = c(all_contrib_taxa, "Other"))) #, "Residual"
setnames(leg_dat, "V1", "Contributing Taxa")
#print(leg_dat)
legend_plot = ggplot(leg_dat, aes(fill = `Contributing Taxa`, x=`Contributing Taxa`)) + geom_bar() + scale_fill_manual(values = contrib_color_palette, name = "Contributing Taxa") # + theme(legend.text = element_text(size = 10))
contrib_legend = tryCatch(get_legend(legend_plot), error = function(){ return(NULL)})
#save(contrib_legend, file = "data/exampleData/example_contrib_legend.rda")
if(!is.null(contrib_legend)){
save_plot(contrib_legend, file = paste0("www/analysisResults/", analysisID, "/", analysisID, "_", "contribLegend.png"), dpi=120, base_width = 14,
base_height = min(c(4+(length(all_contrib_taxa)/4), 45)), limitsize = F)
}
} else {
contrib_legend = NULL
}
if(configTable[V1=="ref_choices", V2 == get_text("source_choices")[1]]){
network_sub = network[Prod %in% cmp_mods[[1]][,compound]|Reac %in% cmp_mods[[1]][,compound]] #Network is in KEGG compounds
} else {
network[,KEGGReac:=agora_kegg_mets(Reac)]
network[,KEGGProd:=agora_kegg_mets(Prod)]
network_sub = network[(KEGGReac %in% cmp_mods[[1]][,compound] & grepl("[e]", Reac, fixed = T))|(KEGGProd %in% cmp_mods[[1]][,compound]& grepl("[e]", Prod, fixed = T))]
}
analysis_summary = get_analysis_summary(input_species = input_data[[1]], species = species, mets = mets, network = network, indiv_cmps = indiv_cmps, cmp_mods = cmp_mods, var_shares = var_shares, config_table = configTable)
#clean up config file
config1 = configTable[!grepl("prefix", V1) & V1 != "vsearch_path"]
return(list(newSpecies = species, varShares = var_shares, modelData = cmp_mods[[1]], configs = config1,
networkData = network_sub, CMPScores = indiv_cmps[CMP != 0], CMPplots = CMP_plots, metContribPlots = met_contrib_plots, plotLegend = contrib_legend,
analysisSummary = analysis_summary))
#Send var_shares for download
#Generate plot of var shares
#source(other stuff)
})
}
ui = fluidPage(
useShinyjs(),
tags$head(
#tags$link(rel = "stylesheet", type = "text/css", href = "lab_styles.css"),
#tags$link(rel = "stylesheet", type = "text/css", href = "shiny-alt.css"),
#tags$link(rel = "stylesheet", type = "text/css", href = "software.css"),
tags$link(rel = "stylesheet", type = "text/css", href = "burritostyle.css"),
tags$style("#errorMessage{color: red;
font-size: 14px;
}"
)
),
tags$img(src = "title_lab_800.jpg", border = 0), #Borenstein lab img
titlePanel("MIMOSA2", windowTitle = "MIMOSA2"),
mainPanel(fluidPage(
p("MIMOSA2 is a tool for metabolic model-based evaluation of paired microbiome and metabolomics datasets. MIMOSA2 1) constructs community metabolic models,
2) assesses whether metabolite measurements are consistent with estimated community metabolic potential, and 3) identifies specific taxa and reactions that can
explain metabolite variation.
For more information, see the ",
tags$a(tags$b("documentation."), href = "https://borenstein-lab.github.io/MIMOSA2shiny", target = "_blank"))), id="description", width = 12),
tags$style(type = "text/css", "#title { color: #3CB371; horizontal-align: left; width: 100%}" ),
mainPanel(
uiOutput("uploadPage"),
tags$style(type = "text/css", "#title { horizontal-align: left; width: 100%}", ".col-sm-12 {width: 95%}" ),
width = 12
))
server <- function(input, output, session) {
#shinyjs::logjs(sessionInfo())
#shinyjs::logjs(devtools::session_info())
#shinyjs::logjs(Cairo.capabilities())
analysisID = randomString()
## Download zip file function
download_all_zip = function(file, example_data = F){
if(example_data){
file_ids = paste0("data/exampleData/", list.files(path = "data/exampleData"))
} else {
#print(list.files(path = analysisID))
file_ids = paste0("www/analysisResults/", analysisID, "/", list.files(path = paste0("www/analysisResults/", analysisID)))
file_ids = file_ids[!grepl(".zip", file_ids, fixed = T)] # In case this already exists
}
#print(file_ids)
zip(zipfile = file, files = file_ids, flags = "-j")
}
#Save text output files and contribution plot
save_output_files = function(){
if(is.list(datasetInput())){
write.table(datasetInput()$analysisSummary, file = paste0("www/analysisResults/", analysisID, "/summaryStats.txt"), row.names = F, sep = "\t", quote=F)
write.table(datasetInput()$configs, file = paste0("www/analysisResults/", analysisID, "/configSettings.txt"), row.names = F, sep = "\t", quote=F, col.names = F)
if(!input$compare_only & input$file1_type != get_text("database_choices")[4] & !is.null(datasetInput()$varShares)) write.table(datasetInput()$varShares, file = paste0("www/analysisResults/", analysisID, "/contributionResults.txt"), row.names = F, sep = "\t", quote=F)
write.table(datasetInput()$newSpecies, file = paste0("www/analysisResults/", analysisID, "/mappedTaxaData.txt"), row.names = F, sep = "\t", quote=F)
write.table(datasetInput()$modelData, file = paste0("www/analysisResults/", analysisID, "/modelResults.txt"), row.names = F, sep = "\t", quote=F)
write.table(datasetInput()$networkData, file = paste0("www/analysisResults/", analysisID, "/communityNetworkModels.txt"), row.names = F, sep = "\t", quote=F)
write.table(datasetInput()$CMPScores, file = paste0("www/analysisResults/", analysisID, "/communityMetabolicPotentialScores.txt"), row.names = F, sep = "\t", quote=F)
if(!input$compare_only & input$file1_type != get_text("database_choices")[4] & !is.null(datasetInput()$varShares)){
plotData = datasetInput()$varShares
tableData = datasetInput()$modelData[!is.na(Slope)]
# plotData = merge(plotData, tableData, by="compound", all.x = T)
# if("Slope.x" %in% names(plotData)) setnames(plotData, "Slope.x", "Slope")
summary_plot_width = plotData[,length(unique(compound))]/2+5
summary_plot_height = plotData[,length(unique(Species))]/2+4
save_plot(plot_summary_contributions(plotData, include_zeros = T, remove_resid_rescale = F), filename = paste0("www/analysisResults/", analysisID, "/contributionHeatmapPlotSelected.pdf"),
base_width = 10, base_height = 8)
}
download_all_zip(paste0("www/analysisResults/", analysisID, "/allResults.zip"), example_data = F)
}
}
analysisResultsFile = reactive({
if("example_data" %in% names(datasetInput())){
"example_allResults.zip"
} else {
paste0(analysisID, "/allResults.zip")
}
})
output$uploadPage = renderUI({
if(is.null(input$goButton) & is.null(input$exampleButton)){
return(fluidPage(
#h3("Data Input"),
microbiome_data_upload(),
network_settings(),
metabolome_data_upload(),
algorithm_settings(),
fluidRow(
column(
actionButton("goButton", " Run MIMOSA "),
actionButton("exampleButton", "Show results for example dataset"),
br(),
br(),
tags$style(type='text/css', "#goButton { vertical-align: middle; horizontal-align: middle; font-size: 22px; background-color: #3CBCDB; padding: 5px;}"),
tags$style(type='text/css', "#exampleButton { vertical-align: middle; horizontal-align: middle; font-size: 14px; background-color: #C3D2D5}"),
width = 12, align = "center"
)), width="100%", tags$style(type = "text/css", "#title { horizontal-align: left; width: 100%}")))
} else if(input$goButton==0 & input$exampleButton == 0){
return(fluidPage(
#h3("Data Input"),
microbiome_data_upload(),
network_settings(),
metabolome_data_upload(),
#h3("Settings"),
algorithm_settings(),
#output_settings(),
fluidRow(
column(
actionButton("goButton", " Run MIMOSA "),
actionButton("exampleButton", "Show results for example dataset"),
br(),
br(),
tags$style(type='text/css', "#goButton { vertical-align: middle; horizontal-align: middle; font-size: 22px; background-color: #3CBCDB; padding: 5px; margin:5px;}"),
tags$style(type='text/css', "#exampleButton { vertical-align: middle; horizontal-align: center; font-size: 14px; background-color: #C3D2D5; margin:5px;}"),
width = 12, align = "center"
)), width="100%", tags$style(type = "text/css", "#title { horizontal-align: left; width: 100%}" )))
} else {
if(input$compare_only == F & !(input$file1_type==get_text("database_choices")[4])){
if(class(datasetInput()) != "character"){
fluidPage(
fluidRow(
column(
downloadButton("downloadAll", "Download All Results Tables and Plots", class = "downloadButton"),
downloadButton("downloadSummaryStats", "Analysis Summary Statistics", class = "downloadButton"),
downloadButton("downloadModelData", "Model Summaries", class = "downloadButton"),
downloadButton("downloadData", "Contribution Results", class = "downloadButton"),
downloadButton("downloadSpecies", "Mapped Taxa Abundances", class = "downloadButton"),
downloadButton("downloadNetworkData", "Community Reactions Linked to Metabolites", class = "downloadButton"),
downloadButton("downloadCMPs", "Community Metabolic Potential Scores", class = "downloadButton"),
downloadButton("downloadSettings", "Record of Configuration Settings", class = "downloadButton"),
tags$style(type='text/css', ".downloadButton { float: left; font-size: 14px; margin: 2px; margin-bottom: 3px; }"), width = 12, align = "center")),
p(strong(get_text("find_results_description"), a(paste0(get_text("app_url"), "analysisResults/", analysisResultsFile()), href = paste0("analysisResults/", analysisResultsFile()), target = "_blank"))),
p(get_text("result_table_description")),
fluidRow( # Big table
DT::dataTableOutput("allMetaboliteInfo"), width="100%"
),
fluidRow(
plotOutput("contribLegend")
),
fluidRow(
downloadButton("downloadComparePlots", "Download selected CMP-Metabolite plots", class = "downloadButton"),
downloadButton("downloadContribPlots", "Download selected taxa contribution plots", class = "downloadButton"),
downloadButton("downloadContributionHeatmap", "Generate and download contribution heatmap plot", class = "downloadButton")
#verbatimTextOutput('x4')
#tableOutput("data2"), width = "100%"
),
tags$style(
type="text/css",
"#image img {max-width: 2in; max-height: 2in; width: auto; height: auto}"
),
tags$style(
type="text/css",
"#downloadAll {background-color: #3CBCDB}"
)#,
#tags$script(HTML("$.fn.dataTable.ext.errMode = 'throw';"))
# ),
# fluidRow(
# plotOutput("contribPlots", height = "650px") #, click = "plot_click", hover = "plot_hover")
# )
)
} else {
textOutput("errorMessage")
}
} else {
fluidPage(
fluidRow(
column(
downloadButton("downloadAll", "Download All Results Tables and Plots", class = "downloadButton"),
downloadButton("downloadSummaryStats", "Analysis Summary Statistics", class = "downloadButton"),
downloadButton("downloadModelData", "Model Summaries", class = "downloadButton"),
downloadButton("downloadSpecies", "Mapped Taxa Abundances", class = "downloadButton"),
downloadButton("downloadNetworkData", "Community Reactions Linked to Metabolites", class = "downloadButton"),
downloadButton("downloadCMPs", "Community Metabolic Potential Scores", class = "downloadButton"),
downloadButton("downloadSettings", "Record of Configuration Settings", class = "downloadButton"),
tags$style(type='text/css', ".downloadButton { float: left; font-size: 14px; margin: 2px; margin-bottom: 3px; }"),
tags$style(
type="text/css",
"#downloadAll {background-color: #3CBCDB}"
), width = 12, align = "center")),
p(strong(get_text("find_results_description"), a(paste0(get_text("app_url"), "analysisResults/", analysisResultsFile()), href = paste0("analysisResults/", analysisResultsFile())))),
p(get_text("result_table_description")),
fluidRow( # Big table
DT::dataTableOutput("allMetaboliteInfo"), width="100%"
),
fluidRow(
downloadButton("downloadComparePlots", "Download Selected CMP-Metabolite Plots", class = "downloadButton") #,
#downloadButton("downloadContribPlots", "Download Selected CMP-Metabolite Plots", class = "downloadButton"),
#downloadButton("downloadContributionHeatmap", "Generate and download contribution heatmap plot", class = "downloadButton")
#verbatimTextOutput('x4')
#tableOutput("data2"), width = "100%"
)
# fluidRow(
# plotOutput("contribPlots", height = "650px") #, click = "plot_click", hover = "plot_hover")
# )
)
}
}
})
observeEvent(input$file1_type, {
if(input$file1_type==get_text("database_choices")[1]){
#updateRadioButtons(session, "ref_choices", selected = get_text("source_choices")[2])
} else {
#updateRadioButtons(session, "ref_choices", selected = get_text("source_choices")[1])
}
# if(input$database==get_text("database_choices")[4]){
# #updateCheckboxInput(session, "metagenomeOpt", value = T)
# disable("file1")
# } else {
# #updateCheckboxInput(session, "metagenomeOpt", value = F)
# enable("file1")
# }
})
observeEvent(input$ref_choices, {
# if(input$ref_choices==get_text("source_choices")[2] & input$database==get_text("database_choices")[1]){ #Only enable mapping threshold if providing sequences
# enable("simThreshold")
# } else {
# disable("simThreshold")
# }
})
config_table = reactive({
if(is.null(input$exampleButton)|input$exampleButton==0){
initial_inputs = c("file1_type","ref_choices", #"geneAdd",
"metType", "simThreshold", "logTransform", "regType", "compare_only") #Check that this is all of them
# if(is.null(input$metagenome)){
# initial_inputs = initial_inputs[initial_inputs != "metagenome_format"]
# }
if(input$file1_type != get_text("database_choices")[1]){
initial_inputs = initial_inputs[initial_inputs != "simThreshold"] #Does nothing if we don't have ASV data
}
inputs_provided = initial_inputs[which(sapply(initial_inputs, function(x){ !is.null(input[[x]])}))]
values_provided = sapply(inputs_provided, function(x){ return(input[[x]])})
inputs_provided = c("analysisID", inputs_provided, "kegg_prefix", "data_prefix", "vsearch_path")
values_provided = c(analysisID, values_provided, "data/KEGGfiles/KEGG_2019/", "data/", "bin/vsearch") #print(file.exists(input$file1$datapath))
#print(inputs_provided)
#print(values_provided)
if(input$logTransform == T){
inputs_provided[inputs_provided == "logTransform"] = "met_transform"
values_provided[inputs_provided == "met_transform"] = "logplus"
}
if(input$regType == get_text("regression_choices")[1]){
inputs_provided = c(inputs_provided, "rankBased", "rank_type")
values_provided = c(values_provided, T, "rfit")
}
#Add file names just for provenance/reproducibility
print("file1")
print(input$file1)
inputs_provided = c(inputs_provided, "file1", "file2", "netAdd")
values_provided = c(values_provided, input$file1$name, input$file2$name, ifelse(is.null(input$netAdd), NA, normalizePath(input$netAdd$name)))
return(data.table(V1 = inputs_provided, V2 = values_provided))
} else {
return(fread("data/exampleData/configs_example.txt", header = F))
}
})
datasetInput = reactive({
if(is.null(input$exampleButton)|input$exampleButton==0){
validate(need(input$file1, "No microbiome file provided"),
need(input$file2, "No metabolite file provided")
)
file_list1 = list(input$file1, input$file2, input$netAdd) # input$geneAddFile,
names(file_list1) = c("file1","file2", "netAdd") # "geneAddFile",
#logjs(config_table())
print(file_list1)
print(config_table())
input_data = tryCatch(read_mimosa2_files(file_list = file_list1, configTable = config_table()), error = function(e){ return(e$message)})
## If input data failed, return error message
if(class(input_data)=="character") return(input_data) else {
tryCatch(run_pipeline(input_data, config_table(), analysisID), error=function(e){
return(paste0(e$message, "\n", paste0(e$call, collapse = " ")))}) #paste0(e$call, "\n", e$message)
}
} else {
#logjs("Reading example data")
config = config_table()
species_dat = fread("data/exampleData/speciesExample.txt")
var_shares = fread("data/exampleData/contributionResultsExample.txt")
modResults = fread("data/exampleData/modelResultsExample.txt")
networkData = fread("data/exampleData/communityNetworkModelsExample.txt")
cmpScores = fread("data/exampleData/CMPScoresExample.txt")
summaryStats = fread("data/exampleData/StatsExample.txt")
#analysisID = "example"
return(list(newSpecies = species_dat, varShares = var_shares, modelData = modResults, configs = config, networkData = networkData,
CMPScores = cmpScores, analysisSummary = summaryStats, example_data = T))
}
})
observeEvent(input$goButton, {
req(input$file1)
req(input$file2)
#if(error_checks){
config_table()
datasetInput()
#
enable("downloadSettings")
enable("downloadData")
enable("downloadModelData")
enable("downloadNetworkData")
save_output_files()
#var_shares = run_pipeline(input) #Just give it everything and go from there
#}
})
observeEvent(input$exampleButton, {
config_table()
datasetInput()
enable("downloadSettings")
enable("downloadData")
enable("downloadModelData")
enable("downloadNetworkData")
})
output$errorMessage = renderText({
paste0("Error: ", datasetInput())
})
output$downloadAll = downloadHandler(
filename = function(){
if("example_data" %in% names(datasetInput())){
"example_allResults.zip"
} else {
"allResults.zip"
}
},
content = function(file){
if("example_data" %in% names(datasetInput())){
download_all_zip(file, example_data = T)
} else {
download_all_zip(file, example_data = F)
}
}
)
output$downloadSummaryStats <- downloadHandler(
filename = function() {
"summaryStats.txt"
#paste(input$dataset, ".csv", sep = "")
},
content = function(file) {
write.table(datasetInput()$analysisSummary, file, row.names = F, sep = "\t", quote=F)
}
)
output$downloadSettings <- downloadHandler(
filename = function() {
"configSettings.txt"
#paste(input$dataset, ".csv", sep = "")
},
content = function(file) {
write.table(datasetInput()$configs, file, row.names = F, sep = "\t", quote=F)
}
)
output$downloadData <- downloadHandler(
filename = function() {
"contributionResults.txt"
#paste(input$dataset, ".csv", sep = "")
},
content = function(file) {
write.table(datasetInput()$varShares, file, row.names = F, sep = "\t", quote=F)
}
)
output$downloadSpecies = downloadHandler(
filename = function() {
"mappedTaxaData.txt"
},
content = function(file){
write.table(datasetInput()$newSpecies, file, row.names = F, sep = "\t", quote=F)
}
)
output$downloadModelData <- downloadHandler(
filename = function() {
"modelResults.txt"
#paste(input$dataset, ".csv", sep = "")
},
content = function(file) {
write.table(datasetInput()$modelData, file, row.names = F, sep = "\t", quote=F)
}
)
output$downloadNetworkData = downloadHandler(
filename = function(){
"communityNetworkModels.txt"
},
content = function(file) {
write.table(datasetInput()$networkData, file, row.names = F, sep = "\t", quote=F)
}
)
output$downloadCMPs = downloadHandler(
filename = function(){
"communityMetabolicPotentialScores.txt"
},
content = function(file) {
write.table(datasetInput()$CMPScores, file, row.names = F, sep = "\t", quote=F)
}
)
output$contribPlots = renderPlot({
plotData = datasetInput()$varShares
# plotData = merge(plotData, datasetInput()$modelData, by="compound", all.x = T)
# if("Slope.x" %in% names(plotData)) setnames(plotData, "Slope.x", "Slope")
#print(plotData)
##met1 = plotData[1,compound]
#### Make a drop bar to select one metabolite to display plot & data for at a time!!! cool.
#plotData[,hist(V1)]
return(plot_summary_contributions(plotData, include_zeros = T, remove_resid_rescale = F))
}, res = 100)
output$allMetaboliteInfo = DT::renderDT({
#print(datasetInput())
# print(datasetInput()$modelData)
# print(names(datasetInput()$modelData))
if(typeof(datasetInput())=="character"){
stop(datasetInput())
}
if(is.null(datasetInput()$modelData)){
stop("No metabolite results found. The analysis produced the error message: ", datasetInput())
}
tableData = datasetInput()$modelData[!is.na(Slope)]
#Get order before rounding
tableData[,m2R:=ifelse(Slope < 0, -1*sqrt(Rsq), sqrt(Rsq))]
compound_order = tableData[order(m2R, decreasing = T), compound] #, tableData[Slope <= 0][order(Rsq, decreasing = T), compound])
print(head(tableData[,Slope]))
#tableData[,m2R:=ifelse(Slope < 0, -1*sqrt(Rsq), sqrt(Rsq))]
tableData[,PVal:=round(PVal, 5)]
tableData[,FDRAdj:=round(FDRAdj, 5)]
tableData[,Rsq:=round(Rsq, 3)]
tableData[,Slope:=round(Slope, 3)]
tableData[,Intercept:=round(Intercept, 3)]
tableData[,metName:=sapply(compound, met_names)]
tableData2 = tableData[Rsq != 0,list(compound, metName, Rsq, PVal, FDRAdj, Slope, TopSynthSpecGenes, TopDegSpecGenes, Intercept)]
tableData2[,compound:=factor(compound, levels = compound_order)]
print(tableData2)
#good_comps = list.files(path = getwd(), pattern = analysisID)
if("example_data" %in% names(datasetInput())){
analysisID2 = "data/exampleData/example"
} else {
analysisID2 = paste0("www/analysisResults/", analysisID, "/", analysisID)
}
print(analysisID2)
tableData2[,Plot:=sapply(paste0(analysisID2, "_", compound, ".png"), function(x){
if(file.exists(x)){
return(img_uri(x))
} else {
return(img_uri("blank_plot.png"))
}})]
if(datasetInput()$configs[V1=="compare_only", V2==FALSE]){
#tableData2 = tableData2[paste0(analysisID, "_",compound,"_contribs.png") %in% good_comps]
tableData2[,ContribPlot:=sapply(paste0(analysisID2, "_", compound, "_contribs.png"), function(x){
if(file.exists(x)){
return(img_uri(x))
} else {
return(img_uri("blank_plot.png"))
}
})]
tableData2 = tableData2[Rsq != 0,list(compound, metName, Rsq, PVal, FDRAdj, Slope, Plot, ContribPlot, TopSynthSpecGenes, TopDegSpecGenes, Intercept)]
setnames(tableData2, c("Compound ID", "Name", "R-squared", "P-value", "FDR-Corrected P-value", "Slope", "Comparison Plot", "Contribution Plot", "Top Producing Taxa and Genes/Rxns", "Top Utilizing Taxa and Genes/Rxns", "Intercept"))
tooltip_table = htmltools::withTags(table(
class = 'display',
thead(
tr(
th('', title = get_text("results_table_titles")[1]),
th('Compound ID', title = get_text("results_table_titles")[2]),
th('Name', title = get_text("results_table_titles")[3]),
th('R-squared', title = get_text("results_table_titles")[4]),
th('P-value', title = get_text("results_table_titles")[5]),
th('FDR-adjusted P-value', title = get_text("results_table_titles")[6]),
th('Slope', title = get_text("results_table_titles")[7]),
th('Comparison Plot', title = get_text("results_table_titles")[8]),
th('Contribution Plot', title = get_text("results_table_titles")[9]),
th("Top Producing Taxa and Genes/Rxns", title = get_text("results_table_titles")[10]),
th("Top Utilizing Taxa and Genes/Rxns", title = get_text("results_table_titles")[12]),
th("Intercept", title = get_text("results_table_titles")[14])
))))
final_table = DT::datatable(
tableData2[order(`Compound ID`)], escape = c("Name", "Compound ID", "R-squared", "P-value", "FDR-Corrected P-value", "Slope", "Top Producing Taxa and Genes/Rxns", "Top Utilizing Taxa and Genes/Rxns", "Intercept"), options = list(lengthMenu = c(5, 10), pageLength = 5, rowCallback = DT::JS("function(r,d) {$(r).attr('overflow', 'hidden').attr('height', '217px')}")), filter = "top",
container = tooltip_table)
} else {
if(datasetInput()$configs[V1=="file1_type", V2==get_text("database_choices")[4]]){ #Skip species
tableData2 = tableData2[Rsq != 0,list(compound, metName, Rsq, PVal, FDRAdj, Slope, Plot, TopSynthSpecGenes, TopDegSpecGenes, Intercept)]
setnames(tableData2, c("Compound ID", "Name", "R-squared", "P-value", "FDR-Corrected P-value", "Slope", "Comparison Plot", "Top Producing Genes/Rxns","Top Utilizing Genes/Rxns", "Intercept"))
tooltip_table = htmltools::withTags(table(
class = 'display',
thead(
tr(
th('', title = get_text("results_table_titles")[1]),
th('Compound ID', title = get_text("results_table_titles")[2]),
th('Name', title = get_text("results_table_titles")[3]),
th('R-squared', title = get_text("results_table_titles")[4]),
th('P-value', title = get_text("results_table_titles")[5]),
th('FDR-adjusted P-value', title = get_text("results_table_titles")[6]),
th('Slope', title = get_text("results_table_titles")[7]),
th('Comparison Plot', title = get_text("results_table_titles")[8]),
th("Top Producing Genes/Rxns", title = get_text("results_table_titles")[10]),
th("Top Utilizing Genes/Rxns", title = get_text("results_table_titles")[12]),
th("Intercept", title = get_text("results_table_titles")[14])
))))
final_table = DT::datatable(
tableData2[order(`Compound ID`)], escape = c("Name", "Compound ID", "R-squared", "P-value", "FDR-Corrected P-value", "Slope", "Top Producing Genes/Rxns", "Top Utilizing Genes/Rxns", "Intercept"), options = list(lengthMenu = c(5, 10), pageLength = 5, rowCallback = DT::JS("function(r,d) {$(r).attr('overflow', 'hidden').attr('height', '217px')}")), filter = "top",
container = tooltip_table)
} else {
tableData2 = tableData2[Rsq != 0,list(compound, metName, Rsq, PVal, FDRAdj, Slope, Plot, TopSynthSpecGenes, TopDegSpecGenes, Intercept)]
setnames(tableData2, c("Compound ID", "Name", "R-squared", "P-value", "FDR-Corrected P-value", "Slope", "Comparison Plot", "Top Producing Taxa and Genes/Rxns", "Top Utilizing Taxa and Genes/Rxns", "Intercept"))
tooltip_table = htmltools::withTags(table(
class = 'display',
thead(
tr(
th('', title = get_text("results_table_titles")[1]),
th('Compound ID', get_text("results_table_titles")[2]),
th('Name', title = get_text("results_table_titles")[3]),
th('R-squared', title = get_text("results_table_titles")[4]),
th('P-value', title = get_text("results_table_titles")[5]),
th('FDR-adjusted P-value', title = get_text("results_table_titles")[6]),
th('Slope', title = get_text("results_table_titles")[7]),
th('Comparison Plot', title = get_text("results_table_titles")[8]),
th("Top Producing Taxa and Genes/Rxns", title = get_text("results_table_titles")[10]),
th("Top Utilizing Taxa and Genes/Rxns", title = get_text("results_table_titles")[12]),
th("Intercept", title = get_text("results_table_titles")[14])
))))
print(names(tableData2))
final_table = DT::datatable(
tableData2[order(`Compound ID`)], escape = c("Name", "Compound ID", "R-squared", "P-value", "FDR-Corrected P-value", "Slope", "Top Producing Taxa and Genes/Rxns", "Top Utilizing Taxa and Genes/Rxns", "Intercept"), options = list(lengthMenu = c(5, 10), pageLength = 5, rowCallback = DT::JS("function(r,d) {$(r).attr('overflow', 'hidden').attr('height', '217px')}")), filter = "top",
container = tooltip_table)
}
}
return(final_table)
#return(DT::datatable(tableData[order(m2R, decreasing = T),list(compound, Rsq, PVal, Slope, Intercept)],
# options = list(lengthMenu = c(5, 10), pageLength = 5)))
}, server = F)
output$downloadComparePlots = downloadHandler(
filename = function() {
"CMP_Metabolite_Compare.zip"
#paste(input$dataset, ".csv", sep = "")
},
content = function(file) {
comp_num = input$allMetaboliteInfo_rows_selected
tableData = datasetInput()$modelData[!is.na(Slope)]
#Get order before rounding
tableData[,m2R:=ifelse(Slope < 0, -1*sqrt(Rsq), sqrt(Rsq))]
compound_order = tableData[order(m2R, decreasing = T), compound]
#compound_order = c(tableData[Slope > 0][order(Rsq, decreasing = T), compound], tableData[Slope <= 0][order(Rsq, decreasing = T), compound])
comp_ids = compound_order[comp_num]
print(comp_ids)
print(analysisID)
if("example_data" %in% names(datasetInput())){
analysisID2 = "data/exampleData/example"
} else {
analysisID2 = paste0("www/analysisResults/", analysisID, "/", analysisID)
}
print(analysisID2)
file_ids = paste0(analysisID2, "_", comp_ids, ".png")
file_ids = file_ids[sapply(file_ids, file.exists)]
zip(zipfile = file, files = file_ids, flags = "-j")
}
)
output$downloadContribPlots = downloadHandler(
filename = function() {
"Metabolite_Taxa_Contributions.zip"
#paste(input$dataset, ".csv", sep = "")
},
content = function(file) {
comp_num = input$allMetaboliteInfo_rows_selected
tableData = datasetInput()$modelData[!is.na(Slope)]
#Get order before rounding
tableData[,m2R:=ifelse(Slope < 0, -1*sqrt(Rsq), sqrt(Rsq))]
compound_order = tableData[order(m2R, decreasing = T), compound]
#compound_order = c(tableData[Slope > 0][order(Rsq, decreasing = T), compound], tableData[Slope <= 0][order(Rsq, decreasing = T), compound])
comp_ids = compound_order[comp_num]
print(comp_ids)
if("example_data" %in% names(datasetInput())){
analysisID2 = "data/exampleData/example"
} else {
analysisID2 = paste0("www/analysisResults/", analysisID, "/", analysisID)
}
file_ids = paste0(analysisID2, "_", comp_ids, "_contribs.png")
file_ids = file_ids[sapply(file_ids, file.exists)]
if(length(file_ids) > 0) zip(zipfile = file, files = file_ids, flags = "-j")
}
)
output$downloadContributionHeatmap = downloadHandler(
filename = function() {
"contributionHeatmapPlotSelected.pdf"
},
content = function(file) {
comp_num = input$allMetaboliteInfo_rows_selected
tableData = datasetInput()$modelData[!is.na(Slope)]
#Get order before rounding
tableData[,m2R:=ifelse(Slope < 0, -1*sqrt(Rsq), sqrt(Rsq))]
compound_order = tableData[order(m2R, decreasing = T), compound]
#compound_order = c(tableData[Slope > 0][order(Rsq, decreasing = T), compound], tableData[Slope <= 0][order(Rsq, decreasing = T), compound])
comp_list = compound_order[comp_num]
plotData = datasetInput()$varShares[compound %in% comp_list]
#plotData = merge(plotData, tableData[compound %in% comp_list], by="compound", all.x = T)
#print(plotData)
##met1 = plotData[1,compound]
#### Make a drop bar to select one metabolite to display plot & data for at a time!!! cool.
#plotData[,hist(V1)]
save_plot(plot_summary_contributions(plotData, include_zeros = T, remove_resid_rescale = F), filename = file, base_width = 10, base_height = 8)
}
)
output$x4 = renderPrint({
s = input$allMetaboliteInfo_rows_selected
if (length(s)) {