Automated Flow (autoFlow)"

In OmicFlow: Fast and Efficient (Automated) Analysis of Sparse Omics Data

knitr::opts_chunk$set(warning = FALSE,
                      message = FALSE,
                      echo = FALSE,
                      results = "asis",
                      fig.width = 10,
                      fig.height = 5,
                      fig.alin = "left"
                      )
# You need this code to conduct the magic dependences attaching...(https://github.com/rstudio/DT/issues/67)
DT::datatable(matrix())

create_plot_tabs <- function(plots, data = NULL, method, label_col, label_row, show_data = FALSE) {

  save_content <- function(data, name) {
    # Save each data frame as CSV with a name-based tempfile
    temp_file <- tempfile(pattern = name, fileext = ".csv")
    data.table::fwrite(data, temp_file)
    return(temp_file)
  }

  if (method == "matrix") {
    for (i in seq_len(nrow(plots))) {
      for (j in seq_len(ncol(plots))) {
        if (!is.null(plots[[i, j]])) {
          cat(paste0("\n\n### Plot: ", label_col[i], "-", label_row[j], "\n"))
          print(plots[[i,j]])
          cat("\n")

          if (show_data && !is.null(data)) {
            # Expect datas[[i,j]] to be a named list of data.frames
            data_list <- data[[i, j]]
            if (is.list(data_list) && length(data_list) > 0) {
              # table with HTML buttons
              overview <- data.frame(
                Dataset = character(),
                Download = character(),
                stringsAsFactors = FALSE
                )
              for (df_name in names(data_list)) {
                df <- data_list[[df_name]]
                file_path <- save_content(df, df_name)

                button_html <- as.character(
                  downloadthis::download_file(
                    path = file_path,
                    output_name = paste0(label_col[i], "_", label_row[j], "_", df_name, ".csv"),
                    button_label = "Download",
                    button_type = "primary",
                    has_icon = TRUE,
                    icon = "fa fa-save"
                  )
                )
                ## Add button to overview table
                overview <- rbind(
                  overview,
                  data.frame(
                    Dataset = df_name,
                    Download = button_html,
                    stringsAsFactors = FALSE
                  )
                )
              }
            }
          # Display as interactive table
          cat(knitr::knit_print(DT::datatable(
            overview,
            rownames = FALSE,
            escape = FALSE,
            height = "100%",
            width = "100%"
            )))
          cat("\n")
          }
        }
      }
    }
  }

  if (method == "vector") {
    for (i in seq_along(plots)) {
      if (!is.null(plots[[i]])) {
        cat(paste0("\n\n### Plot: ", label_col[i], "\n"))
        print(plots[[i]])
        cat("\n")

        if (show_data && !is.null(data)) {
          # Expect datas[[i]] to be a named list of data.frames
          data_list <- data[[i]]
          if (is.list(data_list) && length(data_list) > 0) {
            # table with HTML buttons
            overview <- data.frame(
              Dataset = character(),
              Download = character(),
              stringsAsFactors = FALSE
              )

            for (df_name in names(data_list)) {
              df <- data_list[[df_name]]
              file_path <- save_content(df, df_name)

              button_html <- as.character(
                downloadthis::download_file(
                  path = file_path,
                  output_name = paste0(label_col[i], "_", df_name, ".csv"),
                  button_label = "Download",
                  button_type = "primary",
                  has_icon = TRUE,
                  icon = "fa fa-save"
                )
              )
              ## Add button to overview table
              overview <- rbind(
                overview,
                data.frame(
                  Dataset = df_name,
                  Download = button_html,
                  stringsAsFactors = FALSE
                )
              )
            }
          }
        # Display as interactive table
        cat(knitr::knit_print(DT::datatable(
          overview,
          escape = FALSE,
          rownames = FALSE,
          height = "100%",
          width = "100%"
        )))
        cat("\n")
        }
      }
    }
  }
}

if (!is.null(plots$rankstat_plot)) {
  cat("## Rank stats \n")
  plots$rankstat_plot
}

if (!is.null(plots$alpha_div_plots)) {
  cat("## Alpha diversity {.tabset} \n")
  create_plot_tabs(
    plots = plots$alpha_div_plots, 
    data = data$alpha_div_data,
    method = "vector", 
    label_col = CONTRAST_names,
    show_data = TRUE
    )
}

if (!is.null(plots$pcoa_plots)) {
  cat("## Beta Diversity: Principal Coordinate Analysis (PCoA) {.tabset} \n")
  create_plot_tabs(
    plots = plots$pcoa_plots,
    data = data$pcoa_data,
    method = "matrix", 
    label_col = CONTRAST_names,
    label_row = distance_metrics,
    show_data = TRUE
    )
}

if (!is.null(plots$nmds_plots)) {
  cat("## Beta Diversity: NMDS {.tabset} \n")
  create_plot_tabs(
    plots = plots$nmds_plots, 
    data = data$nmds_data,
    method = "matrix",
    label_col = CONTRAST_names,
    label_row = distance_metrics,
    show_data = TRUE
    )
}

if (!is.null(plots$composition_plots)) {
  cat("## Composition {.tabset} \n")
  create_plot_tabs(
    plots = plots$composition_plots, 
    data = data$composition_data,
    method = "matrix", 
    label_col = CONTRAST_names, 
    label_row = feature_contrast,
    show_data = TRUE
    )
}

if (!is.null(plots$Log2FC_plots)) {
  cat("## Log2 FoldChanges {.tabset} \n")
  create_plot_tabs(
    plots = plots$Log2FC_plots, 
    data = data$Log2FC_data,
    method = "matrix", 
    label_col = CONTRAST_names, 
    label_row = feature_contrast,
    show_data = TRUE
    )
}

cat("## Thanks for using OmicFlow!\n")
utils::citation("OmicFlow")

OmicFlow documentation built on Sept. 9, 2025, 5:24 p.m.