In loosolab/i2dash.scrnaseq: Iterative and Interactive Dashboards for single-cell RNA-sequencing data

{{ env_id }} = readRDS(file.path(datadir, "{{ env_id }}.rds"))

is_shiny <- identical(knitr::opts_knit$get("rmarkdown.runtime"), "shiny")

library(magrittr)
library(scran)
library(S4Vectors)

UI elements {.sidebar}

# var.field
selectInput("selected_var.field_{{ env_id }}", label = "Select column containing the relevant metric of variation:", choices = colnames({{ env_id }}$data), selected = {{ env_id }}$default)

sliderInput("selected_prop_{{ env_id }}", label = "Set the proportion of genes to report as HVGs:", min = 0, max = 1, value = 0.1, step = 0.01)

# Maximum number of columns depends on selected features
output$range_{{ env_id }} <- renderUI({
  max <- round(max({{ env_id }}$data[[input$selected_var.field_{{ env_id }}]], na.rm = TRUE), 1)
  #max <- max({{ env_id }}$data[[input$selected_var.field_{{ env_id }}]])
  min <- 0
  sliderInput("selected_threshold_{{ env_id }}", label = "Set the minimum threshold on the metric of variation:", value = 0, min = min, max = max)
})
uiOutput("range_{{ env_id }}")

# download data
htmltools::div(style="display:block;float:left;margin-top:20px",
               downloadButton("download_data_{{ env_id }}", "Download data"))
# download hvgs
htmltools::div(style="display:block;float:left;margin-top:20px",
               downloadButton("download_hvgs_{{ env_id }}", "Download list of HVGs"))

Column

htmltools::HTML("<div class='alert alert-warning' role='alert'>
  <h4>This page can only be used with the shiny-based interactive mode.</h4>
</div>")

reactive_{{ env_id }} <- shiny::reactive({
  df <- {{ env_id }}$data
  hvgs <- scran::getTopHVGs(
    stats = df,
    var.field = input$selected_var.field_{{ env_id }},
    prop = input$selected_prop_{{ env_id }},
    var.threshold = input$selected_threshold_{{ env_id }})
  df$HVGs <- "normal"
  if(length(hvgs)>0) df[hvgs,]$HVGs <- "HVG"
  df$HVGs <- as.factor(df$HVGs)
  if(length(hvgs)>0) hvg_df <- df[hvgs, -ncol(df)] else hvg_df <- df[0, -ncol(df)]
  return(list(complete_df = df, hvg_df = hvg_df, hvg_names = hvgs))
})

output$plot_{{ env_id }} <- plotly::renderPlotly({
  if({{ env_id }}$default == "bio") y_title <- "Variance of log-expression" else y_title <- "Squared coefficient of variation (CV^2)"
  if({{ env_id }}$default == "bio") type <- "-" else type <- "log"
  i2dash.scrnaseq::plotly_scatterplot(reactive_{{ env_id }}()$complete_df, x = ~mean, y = ~total, color = ~HVGs, text = names({{ env_id }}$fit$mean), y_title = y_title, x_title = "Mean of log-expression", type = "scatter", colors = c("normal" = "#28BBD7", "HVG" = "#ED90A4"), mode = "markers") %>%
    plotly::add_lines(x=reactive_{{ env_id }}()$complete_df$mean, y={{ env_id }}$fit$trend(reactive_{{ env_id }}()$complete_df$mean), type = 'scatter', mode = 'lines', name = "trendline", inherit = F, color = I('red')) %>% 
    plotly::layout(xaxis = list(type = type),
                   yaxis = list(type = type)) %>%
    plotly::toWebGL()
})

plotly::plotlyOutput("plot_{{ env_id }}", height = "100%")

# Download data.frame
output$download_data_{{ env_id }} <- downloadHandler(
  filename =  paste('data-', Sys.Date(), '.csv', sep=''),
  content = function(file) {
    df <- reactive_{{ env_id }}()$complete_df
    write.csv(df, file)
  }
)

output$download_hvgs_{{ env_id }} <- downloadHandler(
  filename =  paste('data-', Sys.Date(), '.csv', sep=''),
  content = function(file) {
    df <- data.frame(hvgs = reactive_{{ env_id }}()$hvg_names)
    write.csv(df, file)
  }
)

Column

Table of highly variable genes

output$tbl_{{ env_id }} <- DT::renderDataTable({
  options(DT.options = list(scrollY="50vh",scrollX="300px", pageLength = 100, autoWidth = TRUE))
  DT::datatable(round(reactive_{{ env_id }}()$hvg_df, 3), filter = 'top')
})
DT::dataTableOutput('tbl_{{ env_id }}')

loosolab/i2dash.scrnaseq documentation built on Jan. 1, 2021, 8:23 a.m.