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app.R
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app.R
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library(shiny)
library(readxl)
library(stringr)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
library(plotly)
library(protti)
library(formattable)
library(forcats)
library(DT)
library(shinyWidgets)
parameters <- data.frame(
parameter = c("file_name",
"date",
"ppb_quantile_low",
"ppb_quantile_mid",
"ppb_quantile_high",
"fit_quantile_high",
"fit_quantile_mid",
"fit_quantile_low",
"cps_low",
"cps_high",
"rsd_quality_low",
"rsd_quality_high",
"best_metal_modes",
"dilution_factor",
"weight",
"dilution_factor_samples",
"expected_ppb",
"expected_uM",
"expected_metal",
"expected_sample",
"original_sample_name",
"new_sample_name",
"condition",
"colours",
"plot_height",
"plot_width"),
value = c("",
"",
"0.5",
"0.75",
"0.9",
"0.5",
"0.25",
"0.1",
"1000",
"100000000",
"1",
"2",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"600",
"1000")
)
metal_mw <- data.frame(metal = c("Ca",
"Mg",
"Fe",
"Co",
"Cu",
"Ni",
"Zn",
"Mn"),
mw = c(40.078,
24.305,
55.845,
58.933,
63.546,
58.6934,
65.38,
54.938))
ui <- fluidPage(
navbarPage("ICP-MS Data Explorer",
id = "tabs",
tabPanel("Calibration overview",
sidebarPanel(
fileInput("raw_data",
"ICP-MS raw data",
accept = c(".xlsx")),
h4("Parameter file"),
helpText("If you performed an analysis on the same dataset before and you downloaded the
parameter file, you can provide it here in order to obtain the same result."),
fileInput("parameters_input", label = NULL, accept = c(".csv")),
downloadButton("parameters_output", label = "Download Parameters")
),
mainPanel(
width = 7,
plotlyOutput("error_plot"),
br(),
plotlyOutput("fit_plot")
)
),
tabPanel(
"Explore metal modes",
sidebarPanel(
selectizeInput("metal_mode", "Select a metal mode", choices = NULL)
),
mainPanel(
width = 7,
plotlyOutput("curve_plot"),
br(),
formattableOutput("curve_table")
)
),
tabPanel(
"Metal mode quality",
sidebarPanel(
selectizeInput("metal", "Select a metal", choices = NULL),
selectizeInput("best_metal_mode", "Select the best metal modes for analysis", choices = NULL, multiple = TRUE),
br(),
h2("Advanced settings"),
h4("ppb quality"),
sliderInput("ppb_quantile_low", 'Quantile for "good" (below) ppb quality', min = 0, max = 1, value = as.numeric(parameters[parameters$parameter == "ppb_quantile_low",]$value), step = 0.05),
sliderInput("ppb_quantile_mid", 'Quantile for "okay" (below) ppb quality', min = 0, max = 1, value = as.numeric(parameters[parameters$parameter == "ppb_quantile_mid",]$value), step = 0.05),
sliderInput("ppb_quantile_high", 'Quantile for "not good" (below) and "bad" (above) ppb quality', min = 0, max = 1, value = as.numeric(parameters[parameters$parameter == "ppb_quantile_high",]$value), step = 0.05),
br(),
h4("fit quality"),
sliderInput("fit_quantile_high", 'Quantile for "good" (above) fit quality', min = 0, max = 1, value = as.numeric(parameters[parameters$parameter == "fit_quantile_high",]$value), step = 0.05),
sliderInput("fit_quantile_mid", 'Quantile for "okay" (above) fit quality', min = 0, max = 1, value = as.numeric(parameters[parameters$parameter == "fit_quantile_mid",]$value), step = 0.05),
sliderInput("fit_quantile_low", 'Quantile for "not good" (above) and "bad" (below) fit quality', min = 0, max = 1, value = as.numeric(parameters[parameters$parameter == "fit_quantile_low",]$value), step = 0.05),
br(),
h4("cps quality"),
numericInput("cps_low", "The lower cps bound", value = as.numeric(parameters[parameters$parameter == "cps_low",]$value)),
numericInput("cps_high", "The higher cps bound", value = as.numeric(parameters[parameters$parameter == "cps_high",]$value)),
br(),
h4("rsd quality"),
sliderInput("rsd_quality", 'rsd quality for "good" (below first), "okay" (below second) and "bad" (above second)',
min = 0,
max = 10,
value = c(as.numeric(parameters[parameters$parameter == "rsd_quality_low",]$value), as.numeric(parameters[parameters$parameter == "rsd_quality_high",]$value)),
step = 0.1)
),
mainPanel(
width = 7,
htmlOutput("metal_title"),
br(),
formattableOutput("quality_table"),
br(),
plotlyOutput("ppb_sample_distribution")
)
),
tabPanel("Experiment overview",
sidebarPanel(
h2("Selected metal modes"),
verbatimTextOutput("list_best_metals")
),
mainPanel(
width = 7,
DT::dataTableOutput("sample_table")
)
),
tabPanel("Analyse experiment",
sidebarPanel(
h2("Specify sample properties"),
helpText("Once sample properties have been specified they appear in the results table."),
selectizeInput("sample", "Select samples", choices = NULL, multiple = TRUE),
numericInput("dilution_factor", "Specify dilution factor", value = NULL),
numericInput("weight", "Specify sample weight if applicable", value = NULL),
actionButton("add_sample_properties", "Add", icon = icon("plus")),
actionButton("reset_sample_properties", "Reset", icon = icon("trash-can")),
br(),
br(),
verbatimTextOutput("sample_properties"),
br(),
h2("Specify expected values"),
helpText("For each metal and sample combination specify expected values. If you don't know
what to expect just don't specify anything."),
selectizeInput("metal_expected", "Select metal", choices = NULL),
selectizeInput("sample_expected", "Select samples", choices = NULL, multiple = TRUE),
numericInput("value_expected", "Specify the expected value", value = NULL),
selectizeInput("unit_expected", "Unit", choices = c("ppb", "uM")),
actionButton("add_sample_expected", "Add", icon = icon("plus")),
actionButton("reset_sample_expected", "Reset", icon = icon("trash-can")),
br(),
br(),
verbatimTextOutput("sample_expected_output"),
br(),
downloadButton("download_result", label = "Download Results")
),
mainPanel(
width = 7,
DT::dataTableOutput("result")
)
),
tabPanel("Plot results",
sidebarPanel(
h2("Create plot"),
selectizeInput("sample_plot", "Select samples", choices = NULL, multiple = TRUE),
radioGroupButtons("plot_type", "Concentration",
choiceNames = list("ppb", "uM"),
choiceValues = list("ppb", "uM")),
numericInput("multiply_concentration", "Multiplicator for concentration (to change the unit)", value = 1),
textInput("plot_title", "Plot title", value = "Plot title"),
textInput("x_axis", "X-Axis", value = "Sample"),
textInput("y_axis", "Y-Axis", value = "Concentration [ppb]"),
selectizeInput(
'colour', label = "Colour", selected = protti_colours[1:2], choices = protti_colours, multiple = TRUE,
options = list(create = TRUE,
render = I("
{
item: function(item, escape) { return '<div><span style=\"font-weight: bold; color:' + escape(item.label) + ';\">' + escape(item.label) + '</span></div>'; },
option: function(item, escape) { return '<div><span style=\"font-weight: bold; color:' + escape(item.label) + ';\">' + escape(item.label) + '</span></div>'; }
}"))
),
# style=\"background-color:' + item.label + ';\" # for changing the background instead of text colour (paste into first div)
helpText("You can also add your own colour by just pasting a colour name or hex code."),
checkboxInput("plot_expected", "Plot expected values", FALSE),
checkboxInput("plot_conditions", "Colour by conditions", FALSE),
numericInput("plot_height", "Plot height", value = 600),
numericInput("plot_width", "Plot width", value = 1000),
downloadButton("download_plot_png", "Download Plot as .png"),
downloadButton("download_plot_pdf", "Download Plot as .pdf")
),
mainPanel(
width = 7,
uiOutput("plot.ui"),
br(),
br(),
h3("Rename samples and add conditions"),
helpText("Doubleclick on a sample name or condition to rename it."),
actionButton("reset_sample_rename", "Reset", icon = icon("trash-can")),
DTOutput('sample_name_edit'),
br()
)
),
)
)
# Define server logic required to draw a histogram
server <- function(input, output, session) {
# variables
start <- 0
list_best_metal_modes <- NULL
sample_names <- NULL
sample_properties_df <- data.frame(dilution_factor = c(),
weight = c(),
sample_name = c())
sample_names_expected_df <- NULL
sample_expected_df <- data.frame(metal = c(),
expected_ppb = c(),
expected_uM = c(),
sample_name = c())
sample_names_table <- NULL
# Update parameter file if uploaded
observeEvent(input$parameters_input, {
parameters <<- suppressMessages(read.csv(input$parameters_input$datapath))
# update inputs
updateSliderInput(session, "ppb_quantile_low", value = as.numeric(parameters[parameters$parameter == "ppb_quantile_low",]$value))
updateSliderInput(session, "ppb_quantile_mid", value = as.numeric(parameters[parameters$parameter == "ppb_quantile_mid",]$value))
updateSliderInput(session, "ppb_quantile_high", value = as.numeric(parameters[parameters$parameter == "ppb_quantile_high",]$value))
updateSliderInput(session, "fit_quantile_high", value = as.numeric(parameters[parameters$parameter == "fit_quantile_high",]$value))
updateSliderInput(session, "fit_quantile_mid", value = as.numeric(parameters[parameters$parameter == "fit_quantile_mid",]$value))
updateSliderInput(session, "fit_quantile_low", value = as.numeric(parameters[parameters$parameter == "fit_quantile_low",]$value))
updateNumericInput(session, "cps_low", value = as.numeric(parameters[parameters$parameter == "cps_low",]$value))
updateNumericInput(session, "cps_high", value = as.numeric(parameters[parameters$parameter == "cps_high",]$value))
updateSliderInput(session, "rsd_quality", value = c(as.numeric(parameters[parameters$parameter == "rsd_quality_low",]$value), as.numeric(parameters[parameters$parameter == "rsd_quality_high",]$value)))
list_best_metal_modes <<- map(split(str_remove_all(unlist(str_split(parameters[parameters$parameter == "best_metal_modes",]$value, pattern = "\\| ")), pattern = "[:alpha:]+: "),
str_extract(unlist(str_split(parameters[parameters$parameter == "best_metal_modes",]$value, pattern = "\\| ")), pattern = "[:alpha:]+(?=:)")),
.f = ~{
unlist(str_split(.x, pattern = ", "))
}
)
sample_properties_df <<- parameters %>%
filter(parameter %in% c("dilution_factor", "dilution_factor_samples", "weight")) %>%
mutate(parameter = case_when(parameter == "dilution_factor_samples" ~ "sample_name",
TRUE ~ parameter)) %>%
pivot_wider(names_from = parameter, values_from = value) %>%
mutate(across(everything(), ~str_split(.x, pattern = "\\| "))) %>%
unnest(c(dilution_factor, weight, sample_name)) %>%
mutate(dilution_factor = suppressWarnings(as.numeric(dilution_factor)),
weight = suppressWarnings(as.numeric(weight)))
sample_names <<- sample_names[!sample_names %in% unlist(str_split(sample_properties_df$sample_name, pattern = ","))]
updateSelectizeInput(session, "sample", choices = sample_names, server = TRUE)
# expected concentrations
sample_expected_df <<- parameters %>%
filter(parameter %in% c("expected_ppb", "expected_uM", "expected_sample", "expected_metal")) %>%
mutate(parameter = case_when(parameter == "expected_sample" ~ "sample_name",
parameter == "expected_metal" ~ "metal",
TRUE ~ parameter)) %>%
pivot_wider(names_from = parameter, values_from = value) %>%
mutate(across(everything(), ~str_split(.x, pattern = "\\| "))) %>%
unnest(c(expected_ppb, expected_uM, sample_name, metal)) %>%
mutate(expected_ppb = suppressWarnings(as.numeric(expected_ppb)),
expected_uM = suppressWarnings(as.numeric(expected_uM)))
exclude_samples <- sample_expected_df %>%
mutate(sample_name = str_split(sample_name, pattern = ",")) %>%
unnest(sample_name)
sample_names_expected_df <<- sample_names_expected_df %>%
left_join(exclude_samples, by = c("sample_name", "metal")) %>%
filter(!(!is.na(expected_ppb) | !is.na(expected_uM))) %>%
distinct(sample_name, metal)
if (input$metal_expected != ""){
sample_names_subset <- sample_names_expected_df %>%
filter(metal == input$metal_expected) %>%
pull(sample_name)
} else {
sample_names_subset <- NULL
}
updateSelectizeInput(session, "sample_expected", choices = sample_names_subset, server = TRUE)
# update sample renaming
sample_names_table <<- parameters %>%
filter(parameter %in% c("original_sample_name", "new_sample_name", "condition")) %>%
mutate(parameter = case_when(parameter == "original_sample_name" ~ "sample_name",
parameter == "condition" ~ "Condition",
TRUE ~ parameter)) %>%
pivot_wider(names_from = parameter, values_from = value) %>%
mutate(across(everything(), ~str_split(.x, pattern = ","))) %>%
unnest(c(sample_name, new_sample_name, Condition))
# update colour
updateSelectizeInput(session, "colour", selected = str_split(parameters[parameters$parameter == "colours",]$value, pattern = ",")[[1]],
choices = unique(c(protti_colours, str_split(parameters[parameters$parameter == "colours",]$value, pattern = ",")[[1]])))
# update plot parameters
updateNumericInput(session, "plot_height", value = as.numeric(parameters[parameters$parameter == "plot_height",]$value))
updateNumericInput(session, "plot_width", value = as.numeric(parameters[parameters$parameter == "plot_width",]$value))
})
# Generate parameters file reactively once it is downloaded
parameters_download <- reactive({
best_metal_mode_string <- paste0(
map2(.x = list_best_metal_modes,
.y = names(list_best_metal_modes),
.f = ~ {
paste0(.y, ": ", paste0(.x, collapse = ", "))
}),
collapse = "| ")
if (nrow(sample_properties_df) != 0){
sample_result_parameters <- sample_properties_df %>%
mutate(across(everything(), ~ as.character((.x)))) %>%
pivot_longer(cols = c(dilution_factor, weight, sample_name), names_to = "parameter", values_to = "value") %>%
group_by(parameter) %>%
mutate(value = paste0(value, collapse = "| ")) %>%
ungroup() %>%
distinct() %>%
mutate(parameter = case_when(parameter == "sample_name" ~ "dilution_factor_samples",
TRUE ~ parameter))
} else {
sample_result_parameters <- data.frame(parameter = c('dilution_factor', 'weight', 'dilution_factor_samples'),
value = c("", "", ""))
}
if (nrow(sample_expected_df) != 0){
sample_expected_parameters <- sample_expected_df %>%
mutate(across(everything(), ~ as.character((.x)))) %>%
pivot_longer(cols = c(expected_ppb, expected_uM, metal, sample_name), names_to = "parameter", values_to = "value") %>%
group_by(parameter) %>%
mutate(value = paste0(value, collapse = "| ")) %>%
ungroup() %>%
distinct() %>%
mutate(parameter = case_when(parameter == "sample_name" ~ "expected_sample",
parameter == "metal" ~ "expected_metal",
TRUE ~ parameter))
} else {
sample_expected_parameters <- data.frame(parameter = c('expected_sample', 'expected_metal', 'expected_ppb', 'expected_uM'),
value = c("", "", "", ""))
}
if(!is.null(sample_names_table)){
sample_names_table_parameters <- sample_names_table %>%
pivot_longer(cols = c(sample_name, new_sample_name, Condition), names_to = "parameter", values_to = "value") %>%
group_by(parameter) %>%
mutate(value = paste0(value, collapse = ",")) %>%
ungroup() %>%
distinct() %>%
mutate(parameter = case_when(parameter == "sample_name" ~ "original_sample_name",
parameter == "Condition" ~ "condition",
TRUE ~ parameter))
} else {
sample_names_table_parameters <- data.frame(parameter = c("original_sample_name", "new_sample_name", "condition"),
value = c("", "", ""))
}
parameters <<- parameters %>%
mutate(value = ifelse(parameter == "best_metal_modes", best_metal_mode_string, value),
value = ifelse(parameter == "date", as.character(Sys.time()), value),
value = ifelse(parameter == "file_name" & !is.null(input$raw_data$name), input$raw_data$name, value),
value = ifelse(parameter == "colours", paste0(input$colour, collapse = ","), value),
value = ifelse(parameter == "plot_height", input$plot_height, value),
value = ifelse(parameter == "plot_width", input$plot_width, value)
) %>%
filter(!parameter %in% c("dilution_factor", "dilution_factor_samples", "weight",
'expected_sample', 'expected_metal', 'expected_ppb', 'expected_uM',
"original_sample_name", "new_sample_name", "condition")) %>%
bind_rows(sample_result_parameters) %>%
bind_rows(sample_expected_parameters) %>%
bind_rows(sample_names_table_parameters)
})
# Download parameters
output$parameters_output <- downloadHandler(
filename = function() {
paste0("parameters_", Sys.time(), ".csv")
},
content = function(file) {
write.csv(parameters_download(), file, row.names = FALSE)
}
)
read_result <- reactive({
if(is.null(input$raw_data)){
return(NULL)
} else {
# Read data
raw_data <- suppressMessages(read_excel(input$raw_data$datapath)[-1,])
col_names <- colnames(raw_data)
col_names[1] <- "sample_type"
col_names[2] <- "sample_name"
for(i in 1:length(col_names)){
if(i > 2){
if(i%%3 == 0){
metal_mode <- col_names[i]
col_names[i] <- paste(metal_mode, "concentration_ppb")
}
if(i%%3 == 1){
col_names[i] <- paste(metal_mode, "cps")
}
if(i%%3 == 2){
col_names[i] <- paste(metal_mode, "rsd")
}
}
}
colnames(raw_data) <- col_names
raw_data
}
})
# Clean up calibration curve data
clean_calibration_curve <- reactive({
if(is.null(read_result())){
return(NULL)
} else {
data <- read_result() |>
filter(sample_type %in% c("CalBlk", "CalStd")) |>
select(-sample_type) |>
pivot_longer(cols = -sample_name,
names_to = "metal_mode",
values_to = "value") |>
mutate(value = suppressWarnings(as.numeric(value))) |>
mutate(type = str_extract(metal_mode, pattern = "concentration_ppb|cps|rsd")) |>
mutate(metal_mode = str_replace(metal_mode, pattern = "concentration_ppb|cps|rsd", replacement = "")) |>
pivot_wider(names_from = type, values_from = value) |>
mutate(expected_ppb = as.numeric(str_extract(sample_name, pattern = "\\d+(?= ppb)"))) %>%
mutate(metal = str_extract(metal_mode, pattern = "(?<=\\d )[:alpha:]+"))
data <- split(data, data$metal_mode) |>
map_dfr(.f = ~ {
.x |>
mutate(fit = summary(lm(concentration_ppb ~ expected_ppb, data = .))$r.squared)
})
data
}
})
# clean up samples
clean_samples <- reactive({
if(is.null(read_result())){
return(NULL)
} else {
data <- read_result() |>
filter(sample_type %in% c("Sample")) |>
select(-sample_type) |>
pivot_longer(cols = -sample_name,
names_to = "metal_mode",
values_to = "value") |>
mutate(value = suppressWarnings(as.numeric(value))) |>
mutate(type = str_extract(metal_mode, pattern = "concentration_ppb|cps|rsd")) |>
mutate(metal_mode = str_replace(metal_mode, pattern = "concentration_ppb|cps|rsd", replacement = "")) |>
pivot_wider(names_from = type, values_from = value) |>
mutate(metal = str_extract(metal_mode, pattern = "(?<=\\d )[:alpha:]+")) %>%
mutate(concentration_ppb = ifelse(is.na(concentration_ppb), 0, concentration_ppb)) %>%
group_by(sample_name, metal) %>%
mutate(outlier = concentration_ppb %in% boxplot.stats(concentration_ppb)$out) %>%
ungroup()
data
}
})
# sample high rsd
sample_rsd <- reactive({
if(is.null(clean_samples())){
return(NULL)
} else {
data <- clean_samples() %>%
group_by(metal_mode) %>%
mutate(samples_high_rsd = sum(rsd > 2 | is.na(rsd), na.rm = TRUE),
percent_samples_high_rsd = round(sum(rsd > 2 | is.na(rsd), na.rm = TRUE) / n() * 100, digits = 2)) %>%
distinct(samples_high_rsd, percent_samples_high_rsd, metal_mode)
data
}
})
assess_calibration_quality <- reactive({
if(is.null(clean_calibration_curve()) | is.null(clean_samples())){
return(NULL)
} else {
sample_outlier <- clean_samples() %>%
distinct(sample_name, outlier, metal_mode) %>%
group_by(metal_mode) %>%
mutate(n_sample_outlier = sum(outlier, na.rm = TRUE),
percent_sample_outlier = round((n_sample_outlier/n())*100, digits = 2)) %>%
ungroup() %>%
distinct(metal_mode, n_sample_outlier, percent_sample_outlier)
data_quality <- clean_calibration_curve() %>%
select(-sample_name) %>%
filter(expected_ppb != 0) %>%
mutate(error = abs(expected_ppb - concentration_ppb)) %>%
group_by(expected_ppb) %>%
mutate(quant50 = quantile(error, na.rm = TRUE, probs = c(input$ppb_quantile_low)),
quant75 = quantile(error, na.rm = TRUE, probs = c(input$ppb_quantile_mid)),
quant90 = quantile(error, na.rm = TRUE, probs = c(input$ppb_quantile_high))) %>%
ungroup() %>%
mutate(quality_ppb = case_when(error <= quant50 ~ 2,
error <= quant75 ~ 1,
error <= quant90 ~ -1,
TRUE ~ -2)) %>%
select(-c(quant50, quant75, quant90)) %>%
mutate(quant50 = quantile(unique(fit), na.rm = TRUE, probs = c(input$fit_quantile_high)),
quant25 = quantile(unique(fit), na.rm = TRUE, probs = c(input$fit_quantile_mid)),
quant10 = quantile(unique(fit), na.rm = TRUE, probs = c(input$fit_quantile_low))) %>%
mutate(quality_fit = case_when(fit >= quant50 ~ 10,
fit >= quant25 ~ 5,
fit >= quant10 ~ -5,
TRUE ~ -10)) %>%
select(-c(quant50, quant25, quant10)) %>%
mutate(quality_cps = ifelse(cps < input$cps_low | cps > input$cps_high | is.na(cps), -2, 2)) %>%
mutate(quality_rsd = case_when(rsd <= input$rsd_quality[1] ~ 2,
rsd <= input$rsd_quality[2] ~ 1,
TRUE ~ -2)) %>%
group_by(metal_mode) %>%
mutate(quality = sum(quality_ppb, quality_rsd, quality_cps, unique(quality_fit))) %>%
ungroup() %>%
select(-c(concentration_ppb, cps, rsd, fit, error)) %>%
left_join(sample_rsd(), by = "metal_mode") %>%
left_join(sample_outlier, by = "metal_mode")
data_quality
}
})
# select metal mode
select_metal_mode <- reactive({
if(is.null(clean_calibration_curve())){
return(NULL)
} else {
distinct_data <- clean_calibration_curve() %>%
distinct(metal_mode, metal)
modes <- split(distinct_data$metal_mode, distinct_data$metal)
}
})
observe({
updateSelectizeInput(session, "metal_mode", choices = select_metal_mode(), server = TRUE)
})
# select metal
select_metal <- reactive({
if(is.null(clean_calibration_curve())){
return(NULL)
} else {
metals <- unique(clean_calibration_curve()$metal)
}
})
observe({
updateSelectizeInput(session, "metal", choices = select_metal(), server = TRUE)
})
# select best metal modes
select_best_metal_modes <- reactive({
if(is.null(assess_calibration_quality())){
return(NULL)
} else {
metal_modes <- assess_calibration_quality() %>%
filter(metal == input$metal) %>%
pull(metal_mode) %>%
unique()
}
})
observe({
input$parameters_input
if(!is.null(list_best_metal_modes[[input$metal]]) && list_best_metal_modes[[input$metal]] != ""){
selected_modes <- list_best_metal_modes[[input$metal]]
} else {
selected_modes <- NULL
}
updateSelectizeInput(session, "best_metal_mode", choices = select_best_metal_modes(), selected = selected_modes, server = TRUE)
})
# Make header
output$metal_title <- renderText({
if(is.null(select_metal())){
return(NULL)
} else {
paste("<h1>", input$metal, "</h1>")
}
})
# metal mode selection
observe({
# observe events in
input$best_metal_mode
input$metal
# make initial list
if(start == 1){
print("Metal list created")
list_best_metal_modes <<- vector(mode = "list", length = length(select_metal()))
names(list_best_metal_modes) <<- select_metal()
}
# modify list
if(start > 1){
print("Metal list modified")
if(is.null(input$best_metal_mode)){
list_best_metal_modes[[input$metal]] <<- ""
} else {
list_best_metal_modes[[input$metal]] <<- input$best_metal_mode
}
}
print(list_best_metal_modes)
# Update counter for inital list
start <<- start + 1
})
output$list_best_metals <- renderPrint({
if(is.null(input$best_metal_mode) & input$metal == ""){
return("Please load data!")
} else {
glimpse(list_best_metal_modes)
}
})
# Make cuve plot
output$curve_plot <- renderPlotly({
if(is.null(select_metal_mode())){
return(NULL)
} else {
data_metal_mode <- clean_calibration_curve() |>
filter(metal_mode == input$metal_mode)
plot <- data_metal_mode |>
ggplot(aes(x = expected_ppb, y = concentration_ppb))+
geom_point(size = 2)+
geom_smooth(formula = y ~ x,
method = "lm", size = 1, col = protti_colours[1])+
labs(title = input$metal_mode, x = "Expected Concentration [ppb]", y = "Measured Concentration [ppb]")+
theme_bw() +
theme(plot.title = ggplot2::element_text(size = 20),
axis.title.x = ggplot2::element_text(size = 15),
axis.text.y = ggplot2::element_text(size = 15),
axis.text.x = ggplot2::element_text(size = 12),
axis.title.y = ggplot2::element_text(size = 15),
legend.title = ggplot2::element_text(size = 15),
legend.text = ggplot2::element_text(size = 15),
strip.text.x = ggplot2::element_text(size = 15),
strip.text = ggplot2::element_text(size = 15),
strip.background = element_blank()
)
ggplotly(plot) %>%
add_annotations(
x= 0.5,
y= max(data_metal_mode$expected_ppb, na.rm = TRUE),
xanchor = "left",
text = paste0("<b>R<sup>2</sup>: ", unique(data_metal_mode$fit), "</b>"),
showarrow = F
)
}
})
# Make error plot
output$error_plot <- renderPlotly({
if(is.null(clean_calibration_curve())){
return(NULL)
} else {
plot <- clean_calibration_curve() |>
mutate(error = abs(expected_ppb - concentration_ppb)) %>%
mutate(expected_ppb = forcats::fct_reorder(as.character(expected_ppb),expected_ppb)) %>%
ggplot(aes(expected_ppb, error))+
geom_boxplot()+
labs(title = "Error distribution", x = "Expected Concentration [ppb]", y = "Measurment error [ppb]")+
theme_bw() +
theme(plot.title = ggplot2::element_text(size = 20),
axis.title.x = ggplot2::element_text(size = 15),
axis.text.y = ggplot2::element_text(size = 15),
axis.text.x = ggplot2::element_text(size = 12),
axis.title.y = ggplot2::element_text(size = 15),
legend.title = ggplot2::element_text(size = 15),
legend.text = ggplot2::element_text(size = 15),
strip.text.x = ggplot2::element_text(size = 15),
strip.text = ggplot2::element_text(size = 15),
strip.background = element_blank()
)
ggplotly(plot)
}
})
# Make r square plot
output$fit_plot <- renderPlotly({
if(is.null(clean_calibration_curve())){
return(NULL)
} else {
plot <- clean_calibration_curve() |>
distinct(fit) %>%
ggplot(aes(x = "All curve fits", y = fit))+
geom_boxplot()+
labs(title = "R<sup>2</sup> Distribution", x = "", y = "R<sup>2</sup>")+
theme_bw() +
theme(plot.title = ggplot2::element_text(size = 20),
axis.title.x = ggplot2::element_text(size = 15),
axis.text.y = ggplot2::element_text(size = 15),
axis.text.x = ggplot2::element_text(size = 12),
axis.title.y = ggplot2::element_text(size = 15),
legend.title = ggplot2::element_text(size = 15),
legend.text = ggplot2::element_text(size = 15),
strip.text.x = ggplot2::element_text(size = 15),
strip.text = ggplot2::element_text(size = 15),
strip.background = element_blank()
)
ggplotly(plot) %>%
layout(yaxis = list(hoverformat = '.6f'))
}
})
# Make table
output$curve_table <- renderFormattable({
if(is.null(select_metal_mode())){
return(NULL)
} else {
clean_calibration_curve() |>
mutate(error = abs(expected_ppb - concentration_ppb)) %>%
group_by(expected_ppb) %>%
mutate(quant50 = quantile(error, na.rm = TRUE, probs = c(input$ppb_quantile_low)),
quant75 = quantile(error, na.rm = TRUE, probs = c(input$ppb_quantile_mid)),
quant90 = quantile(error, na.rm = TRUE, probs = c(input$ppb_quantile_high))) %>%
ungroup() %>%
mutate(quality = case_when(error <= quant50 ~ "good",
error <= quant75 ~ "okay",
error <= quant90 ~ "not good",
TRUE ~ "bad")) %>%
filter(metal_mode == input$metal_mode) |>
select(expected_ppb, concentration_ppb, cps, rsd, quality) |>
formattable(list(
quality = FALSE,
cps = formatter("span", style = x ~ ifelse(x > input$cps_high | x < input$cps_low | is.na(x),
style(color = "red", font.weight = "bold"), NA)),
rsd = formatter("span", style = x ~ case_when(x > input$rsd_quality[2] ~ style(color = "red", font.weight = "bold"),
x <= input$rsd_quality[1] ~ style(color = "green", font.weight = "bold"),
x <= input$rsd_quality[2] ~ style(color = "#c4e84d", font.weight = "bold"))),
concentration_ppb = formatter("span", style = ~ case_when(
quality == "bad" ~ style(color = "red", font.weight = "bold"),
quality == "not good" ~ style(color = "#e38c29", font.weight = "bold"),
quality == "okay" ~ style(color = "#c4e84d", font.weight = "bold"),
quality == "good" ~ style(color = "green", font.weight = "bold")
))
))
}
})
# Make metal mode quality table
output$quality_table <- renderFormattable({
if(is.null(assess_calibration_quality())){
return(NULL)
} else {
assess_calibration_quality() %>%
filter(metal == input$metal) |>
arrange(desc(quality)) %>%
formattable(list(
metal = FALSE,
quality_ppb = formatter("span", style = x ~ case_when(x == 2 ~ style(color = "green", font.weight = "bold"),
x == 1 ~ style(color = "#c4e84d", font.weight = "bold"),
x == -1 ~ style(color = "#e38c29", font.weight = "bold"),
x == -2 ~ style(color = "red", font.weight = "bold"))),
quality_cps = formatter("span", style = x ~ case_when(x == 2 ~ style(color = "green", font.weight = "bold"),
x == 1 ~ style(color = "#c4e84d", font.weight = "bold"),
x == -1 ~ style(color = "#e38c29", font.weight = "bold"),
x == -2 ~ style(color = "red", font.weight = "bold"))),
quality_rsd = formatter("span", style = x ~ case_when(x == 2 ~ style(color = "green", font.weight = "bold"),
x == 1 ~ style(color = "#c4e84d", font.weight = "bold"),
x == -1 ~ style(color = "#e38c29", font.weight = "bold"),
x == -2 ~ style(color = "red", font.weight = "bold"))),
quality_fit = formatter("span", style = x ~ case_when(x == 10 ~ style(color = "green", font.weight = "bold"),
x == 5 ~ style(color = "#c4e84d", font.weight = "bold"),
x == -5 ~ style(color = "#e38c29", font.weight = "bold"),
x == -10 ~ style(color = "red", font.weight = "bold"))),
n_sample_outlier = formatter("span", style = x ~ case_when(x >= 1 ~ style(color = "red", font.weight = "bold"),
TRUE ~ style(color = "green", font.weight = "bold"))),
percent_sample_outlier = formatter("span", style = x ~ case_when(x > 0 ~ style(color = "red", font.weight = "bold"),
TRUE ~ style(color = "green", font.weight = "bold")))
)
)
}
})
# Make sample table
output$sample_table <- DT::renderDataTable({
if(is.null(clean_samples()) & is.null(input$best_metal_mode)){
return(NULL)
} else {
clean_samples() %>%
filter(metal_mode %in% unlist(list_best_metal_modes)) %>%
formattable(list(
rsd = formatter("span", style = x ~ case_when(x > 2 ~ style(color = "red", font.weight = "bold"),
x <= 1 ~ style(color = "green", font.weight = "bold"),
x <= 2 ~ style(color = "#c4e84d", font.weight = "bold")))
)
) %>%
formattable::as.datatable()
}
})
# Plot ppb distribution per sample and metal
output$ppb_sample_distribution <- renderPlotly({
if(is.null(clean_samples())){
return(NULL)
} else {
plot <- clean_samples() %>%
filter(metal == input$metal) |>
ggplot(aes(x = sample_name, y = concentration_ppb, col = outlier, label = metal_mode))+
geom_jitter(width = 0.15)+
labs(title = "ppb Distribution", x = "", y = "Concentration [ppb]")+
scale_color_manual(values=c("black", "red"))+
theme_bw() +
theme(plot.title = ggplot2::element_text(size = 20),
axis.title.x = ggplot2::element_text(size = 15),
axis.text.y = ggplot2::element_text(size = 15),
axis.text.x = ggplot2::element_text(size = 12, angle = 75, hjust = 1),
axis.title.y = ggplot2::element_text(size = 15),
legend.title = ggplot2::element_text(size = 15),
legend.text = ggplot2::element_text(size = 15),
strip.text.x = ggplot2::element_text(size = 15),
strip.text = ggplot2::element_text(size = 15),
strip.background = element_blank()
)
ggplotly(plot, height = 600)
}
})
##### Sample properties
# update sample selection
observe({
sample_names <<- unique(clean_samples()$sample_name)
updateSelectizeInput(session, "sample", choices = sample_names, server = TRUE)
})
# Add samples to dataframe
observeEvent(input$add_sample_properties, {
new_entry <- data.frame(dilution_factor = input$dilution_factor,
weight = ifelse(input$weight == 0, NA, input$weight),
sample_name = paste0(input$sample, collapse = ","))
sample_properties_df <<- sample_properties_df %>%
bind_rows(new_entry)
sample_names <<- sample_names[!sample_names %in% input$sample]
updateSelectizeInput(session, "sample", choices = sample_names, server = TRUE)
})
# Reset sample dataframe
observeEvent(input$reset_sample_properties, {
sample_properties_df <<- data.frame(dilution_factor = c(),
weight = c(),
sample_name = c())
sample_names <<- unique(clean_samples()$sample_name)
updateSelectizeInput(session, "sample", choices = sample_names, server = TRUE)
})
# return dataframe
output$sample_properties <- renderPrint({
input$add_sample_properties
input$reset_sample_properties
input$parameters_input
Sys.sleep(1)
print(str(sample_properties_df))
})
#### Specify expected values
# update metal selection
observe({
updateSelectizeInput(session, "metal_expected", choices = unique(clean_samples()$metal), server = TRUE)
})
# update sample selection
observe({
if (!is.null(clean_samples()) & input$metal_expected == ""){
sample_names_expected_df <<- clean_samples() %>%
distinct(metal, sample_name)
}
if (input$metal_expected != ""){
sample_names_subset <- sample_names_expected_df %>%
filter(metal == input$metal_expected) %>%
pull(sample_name)
} else {
sample_names_subset <- NULL
}
updateSelectizeInput(session, "sample_expected", choices = sample_names_subset, server = TRUE)
})
# Add samples to expected dataframe
observeEvent(input$add_sample_expected, {
new_entry <- data.frame(metal = input$metal_expected,
expected_ppb = ifelse(input$value_expected == 0 | input$unit_expected == "uM", NA, input$value_expected),
expected_uM = ifelse(input$value_expected == 0 | input$unit_expected == "ppb", NA, input$value_expected),
sample_name = paste0(input$sample_expected, collapse = ","))
sample_expected_df <<- sample_expected_df %>%
bind_rows(new_entry)
sample_names_expected_df <<- sample_names_expected_df %>%
filter(!(metal == input$metal_expected & sample_name %in% input$sample_expected))
if (input$metal_expected != ""){
sample_names_subset <- sample_names_expected_df %>%
filter(metal == input$metal_expected) %>%
pull(sample_name)
} else {
sample_names_subset <- NULL
}
updateSelectizeInput(session, "sample_expected", choices = sample_names_subset, server = TRUE)
})
# Reset sample expected dataframe
observeEvent(input$reset_sample_expected, {
sample_expected_df <<- data.frame(metal = c(),
expected_ppb = c(),
expected_uM = c(),
sample_name = c())
sample_names_expected_df <<- clean_samples() %>%
distinct(metal, sample_name)
if (input$metal_expected != ""){
sample_names_subset <- sample_names_expected_df %>%
filter(metal == input$metal_expected) %>%
pull(sample_name)
} else {
sample_names_subset <- NULL
}
updateSelectizeInput(session, "sample_expected", choices = sample_names_subset, server = TRUE)
})
# return dataframe
output$sample_expected_output <- renderPrint({
input$add_sample_expected
input$reset_sample_expected
input$parameters_input
print(str(sample_expected_df))
})