R/trelliPlots_seqData.R

In pmartR/pmartRqc: Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data

#' @name trelli_rnaseq_boxplot
#'
#' @title Boxplot trelliscope building function for RNA-seq data 
#'
#' @description Specify a boxplot design and cognostics for the RNA-Seq
#'   boxplot trelliscope. Each boxplot will have its own groups as specified by
#'   the first main effect in group_designation. Use "trelli_abundance_boxplot"
#'   for MS/NMR-based omics. 
#'
#' @param trelliData A trelliscope data object made by as.trelliData or
#'   as.trelliData.edata, and grouped by trelli_panel_by. Must be built using 
#'   seqData. Required.
#' @param cognostics A vector of cognostic options for each plot. Valid entries
#'   are "count", "mean lcpm", "median lcpm", and "cv lcpm". 
#'   If data are paneled by a biomolecule, the count will be "sample count".
#'   If data are paneled by a sample or a biomolecule class, the count will be "biomolecule count". 
#'   If statRes data is included, "p-value" and "fold change" data per comparisons
#'   may be added. If grouping information is included, only "sample count" and 
#'   "mean lcpm" will be calculated, along with "p-value" and "fold change"
#'   if specified. "p-value" will not be included if paneling a trelliscope
#'   display by a biomolecule class. Default is "sample count" and "mean lcpm".
#' @param ggplot_params An optional vector of strings of ggplot parameters to
#'   the backend ggplot function. For example, c("ylab('')", "ylim(c(2,20))").
#'   Default is NULL.
#' @param interactive A logical argument indicating whether the plots should be
#'   interactive or not. Interactive plots are ggplots piped to ggplotly (for
#'   now). Default is FALSE.
#' @param include_points Add points as a geom_jitter. Default is TRUE.
#' @param path The base directory of the trelliscope application. Default is
#'   Downloads.
#' @param name The name of the display. Default is Trelliscope.
#' @param test_mode A logical to return a smaller trelliscope to confirm plot
#'   and design. Default is FALSE.
#' @param test_example A vector of plot indices to return for test_mode. Default
#'   is 1.
#' @param single_plot A TRUE/FALSE to indicate whether 1 plot (not a
#'   trelliscope) should be returned. Default is FALSE.
#' @param ... Additional arguments to be passed on to the trelli builder
#'
#' @return No return value, builds a trelliscope display of boxplots that is stored in `path`
#' 
#' @examplesIf requireNamespace("pmartRdata", quietly = TRUE)
#' \dontrun{
#' library(pmartRdata)
#' 
#' trelliData_seq1 <- as.trelliData.edata(e_data = rnaseq_edata,
#'                                       edata_cname = "Transcript",
#'                                       omics_type = "seqData")
#' omicsData_seq <- group_designation(omicsData = rnaseq_object, main_effects = c("Virus"))
#' 
#' # Filter low transcript counts
#' omicsData_seq <- applyFilt(filter_object = total_count_filter(omicsData = omicsData_seq), 
#'  omicsData = omicsData_seq, min_count = 15)
#' 
#' # Select a normalization and statistics method (options are 'edgeR', 'DESeq2', and 'voom').
#' # See ?difexp_seq for more details
#' statRes_seq <- diffexp_seq(omicsData = omicsData_seq, method = "voom")
#' 
#' # Generate the trelliData object
#' trelliData_seq2 <- as.trelliData(omicsData = omicsData_seq)
#' trelliData_seq3 <- as.trelliData(statRes = statRes_seq)
#' trelliData_seq4 <- as.trelliData(omicsData = omicsData_seq, statRes = statRes_seq)
#' 
#' ## Generate trelliData objects using the as.trelliData.edata example code.
#' 
#' # Build the RNA-seq boxplot with an edata file where each panel is a biomolecule. 
#' trelli_panel_by(trelliData = trelliData_seq1, panel = "Transcript") %>% 
#'    trelli_rnaseq_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir())
#'    
#' # Build the RNA-seq boxplot where each panel is a sample.
#' # Include all applicable cognostics. Remove points. 
#' trelli_panel_by(trelliData = trelliData_seq1, panel = "Sample") %>% 
#'    trelli_rnaseq_boxplot(test_mode = TRUE, test_example = 1:10, 
#'                             include_points = FALSE,
#'                             cognostics = c("count", 
#'                                            "mean lcpm", 
#'                                            "median lcpm", 
#'                                            "cv lcpm"),
#'                             path = tempdir()
#'                            )
#' 
#' # Build the RNA-seq boxplot with an omicsData object.
#' # Let the panels be biomolecules. Here, grouping information is included.
#' trelli_panel_by(trelliData = trelliData_seq2, panel = "Transcript") %>% 
#'    trelli_rnaseq_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir())
#'    
#' # Build the RNA-seq boxplot with an omicsData object. The panel is a biomolecule class,
#' # which is proteins in this case.
#' trelli_panel_by(trelliData = trelliData_seq2, panel = "Gene") %>% 
#'    trelli_rnaseq_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir())
#'     
#' # Build the RNA-seq boxplot with an omicsData and statRes object.
#' # Panel by a biomolecule, and add statistics data to the cognostics
#' trelli_panel_by(trelliData = trelliData_seq4, panel = "Transcript") %>%
#'    trelli_rnaseq_boxplot(test_mode = TRUE, test_example = 1:10,
#'      cognostics = c("mean lcpm", "p-value", "fold change"), path = tempdir())
#'  
#' # Other options include modifying the ggplot  
#' trelli_panel_by(trelliData = trelliData_seq1, panel = "Transcript") %>%
#'    trelli_rnaseq_boxplot(test_mode = TRUE, test_example = 1:10,
#'      ggplot_params = c("ylab('')", "xlab('')"), path = tempdir())
#' 
#' # Or making the plot interactive 
#' trelli_panel_by(trelliData = trelliData_seq4, panel = "Gene") %>%
#'     trelli_rnaseq_boxplot(interactive = TRUE, test_mode = TRUE, 
#'      test_example = 1:10, path = tempdir())
#' 
#' \dontshow{closeAllConnections()}
#' }
#'
#' @author David Degnan, Lisa Bramer
#'
#' @export
trelli_rnaseq_boxplot <- function(trelliData,
                                     cognostics = c("count", "mean lcpm"),
                                     ggplot_params = NULL,
                                     interactive = FALSE,
                                     include_points = TRUE,
                                     path = .getDownloadsFolder(),
                                     name = "Trelliscope",
                                     test_mode = FALSE,
                                     test_example = 1,
                                     single_plot = FALSE,
                                     ...) {
  # Run initial checks----------------------------------------------------------
  
  # Run generic checks 
  trelli_precheck(trelliData = trelliData, 
                  trelliCheck = "omics",
                  cognostics = cognostics,
                  acceptable_cognostics = c("count", "mean lcpm", "median lcpm", "cv lcpm", "p-value", "fold change"),
                  ggplot_params = ggplot_params,
                  interactive = interactive,
                  test_mode = test_mode, 
                  test_example = test_example,
                  single_plot = single_plot,
                  seqDataCheck = "required",
                  seqText = "Use trelli_abundance_boxplot instead.",
                  p_value_thresh = NULL)
  
  
  # Remove stat specific options if no stats data was provided 
  if (is.null(trelliData$trelliData.stat)) {
    if (any(c("p-value", "fold change") %in% cognostics) & is.null(trelliData$trelliData.stat)) {
      cognostics <- cognostics[-match(c("p-value", "fold change"), cognostics, nomatch = 0)]
      message(paste("'p-value' and/or 'fold change' were listed as cognostics, but not provided in the trelliData object.",
                    "Did you forget to include a statRes object?")
      )
    }    
  }
  
  # Remove median and cv as cognostics if data is grouped
  if (!inherits(trelliData, "trelliData.edata")) {
    if (any(c("median lcpm", "cv lcpm") %in% cognostics)) {
      cognostics <- cognostics[-match(c("median lcpm", "cv lcpm"), cognostics, nomatch = 0)]
      message("'median lcpm' and 'cv lcpm' are not permitted when groups have been specified.")
    }
  }
  
  # Round test example to integer 
  if (test_mode) {
    test_example <- unique(abs(round(test_example)))
  }
  
  # Make sure include_points is a true or false
  if (!is.logical(include_points) & !is.na(include_points)) {
    stop("include_points must be a TRUE or FALSE.")
  }
  
  # Determine if count is biomolecule count or sample count 
  if ("count" %in% cognostics) {
    
    if (attr(trelliData, "panel_by_omics") == get_edata_cname(trelliData$omicsData)) {
      cognostics[which(cognostics == "count")] <- "sample nonzero count"
    } else {
      cognostics[which(cognostics == "count")] <- "biomolecule nonzero count"
    }
    
  }
  
  if (any(c("p-value", "fold change") %in% cognostics) & attr(trelliData, "panel_by_omics") != get_edata_cname(trelliData$omicsData)) {
    message(paste("Please panel by", get_edata_cname(trelliData$omicsData), "to get 'p-value' and 'fold change' as cognostics in the trelliscope display."))
  }
  
  # Make boxplot function-------------------------------------------------------
  
  # First, generate the boxplot function
  box_plot_fun <- function(DF, title) {
    # Add a blank group if no group designation was given
    if (is.null(attributes(trelliData$omicsData)$group_DF)) {
      DF$Group <- "x"
    }
    
    # Build plot
    boxplot <- ggplot2::ggplot(DF, ggplot2::aes(x = Group, fill = Group, y = .data$LCPM)) +
      ggplot2::geom_boxplot(outlier.shape = NA) +
      ggplot2::theme_bw() +
      ggplot2::ggtitle(title) +
      ggplot2::theme(legend.position = "none", plot.title = ggplot2::element_text(hjust = 0.5)) +
      ggplot2::ylab("Log Counts per Million (LCPM)")
    
    # Add include_points
    if (include_points) {
      boxplot <- boxplot + ggplot2::geom_jitter(height = 0, width = 0.25)
    }
    
    # Remove x axis if no groups
    if (is.null(attributes(trelliData$omicsData)$group_DF)) {
      boxplot <- boxplot + ggplot2::theme(
        axis.title.x = ggplot2::element_blank(),
        axis.ticks.x = ggplot2::element_blank(),
        axis.text.x = ggplot2::element_blank()
      )
    }
    
    # Add additional parameters
    if (!is.null(ggplot_params)) {
      for (param in ggplot_params) {
        boxplot <- boxplot + eval(parse(text = paste0("ggplot2::", param)))
      }
    }
    
    # If interactive, pipe to ggplotly
    if (interactive) {
      boxplot <- boxplot %>% plotly::ggplotly()
    }
    
    return(boxplot)
  }
  
  # Create cognostic function---------------------------------------------------
  
  # Second, create function to return cognostics
  box_cog_fun <- function(DF, biomolecule) {
    # Set basic cognostics for ungrouped data or in case when data is not split by fdata_cname
    cog <- list(
      "sample nonzero count" = dplyr::tibble(`Sample Non-Zero Count` = trelliscopejs::cog(sum(DF$Count != 0), desc = "Sample Non-Zero Count")),
      "biomolecule nonzero count" = dplyr::tibble(`Biomolecule Non-Zero Count` = trelliscopejs::cog(sum(DF$Count != 0), desc = "Biomolecule Non-Zero Count")),
      "mean lcpm" = dplyr::tibble(`Mean LCPM` = trelliscopejs::cog(round(mean(DF$LCPM, na.rm = TRUE), 4), desc = "Mean LCPM")), 
      "median lcpm" = dplyr::tibble(`Median LCPM` = trelliscopejs::cog(round(median(DF$LCPM, na.rm = TRUE), 4), desc = "Median LCPM")), 
      "cv lcpm" = dplyr::tibble(`CV LCPM` = trelliscopejs::cog(round((sd(DF$LCPM, na.rm = TRUE) / mean(DF$LCPM, na.rm = T)) * 100, 4), desc = "CV LCPM"))
    )
    
    # If cognostics are any of the cog, then add them 
    if (any(cognostics %in% c("sample nonzero count", "biomolecule nonzero count", "mean lcpm", "median lcpm", "cv lcpm"))) {
      cog_to_trelli <- do.call(dplyr::bind_cols, lapply(cognostics, function(x) {cog[[x]]})) %>% dplyr::tibble()
    } else {
      cog_to_trelli <- NULL
    }
    
    # Get fdata cname and panel_by selection
    fdata_cname <- pmartR::get_fdata_cname(trelliData$omicsData)
    panel_by_choice <- attr(trelliData, "panel_by_omics")
    
    # Additional group cognostics can be added only if group_designation was set and
    # trelli_panel_by is not the fdata_cname
    if (!is.null(attributes(trelliData$omicsData)$group_DF) & fdata_cname != panel_by_choice & !is.null(cog_to_trelli)) {
      # Since the number of groups is unknown, first panel_by the Groups,
      # then calculate all summary statistics, pivot to long format,
      # subset down to requested statistics, switch name to a more specific name,
      # combine group and name, and generate the cognostic tibble
      cogs_to_add <- DF %>%
        dplyr::group_by(Group) %>%
        dplyr::summarise(
          "sample nonzero count" = sum(Count != 0), 
          "biomolecule nonzero count" = sum(Count != 0), 
          "mean lcpm" = round(mean(.data$LCPM, na.rm = TRUE), 4),
          "median lcpm" = round(median(.data$LCPM, na.rm = TRUE), 4),
          "cv lcpm" = round((sd(.data$LCPM, na.rm = T) / mean(.data$LCPM, na.rm =T)) * 100, 4),
        ) %>%
        tidyr::pivot_longer(c(.data$`sample nonzero count`, .data$`biomolecule nonzero count`, .data$`mean lcpm`, .data$`median lcpm`, .data$`cv lcpm`)) %>%
        dplyr::filter(name %in% cognostics) %>%
        dplyr::mutate(name = paste(Group, name)) %>%
        dplyr::ungroup() %>%
        dplyr::select(-Group) 
      
      # Add new cognostics 
      cog_to_trelli <- do.call(cbind, lapply(1:nrow(cogs_to_add), function(row) {
        quick_cog(cogs_to_add$name[row], cogs_to_add$value[row])
      })) %>% dplyr::tibble() 
      
    }
    
    # Add cognostics that only apply when stats data is grouped by edata_cname
    edata_cname <- pmartR::get_edata_cname(trelliData$omicsData)
    
    if (!is.null(trelliData$trelliData.stat) && !is.na(attr(trelliData, "panel_by_stat")) && edata_cname == attr(trelliData, "panel_by_stat")) {
      
      # Downselect to only stats 
      stat_cogs <- cognostics[cognostics %in% c("fold change", "p-value")]
      
      if (length(stat_cogs) != 0) {
        # Subset down the dataframe down to group, unnest the dataframe,
        # pivot_longer to comparison, subset columns to requested statistics,
        # switch name to a more specific name
        cogs_to_add <- trelliData$trelliData.stat %>%
          dplyr::filter(trelliData$trelliData.stat[[edata_cname]] == biomolecule) %>%
          dplyr::select(Nested_DF) %>%
          tidyr::unnest(cols = c(Nested_DF)) %>%
          dplyr::rename(
            `p-value` = .data$p_value,
            `fold change` = fold_change
          ) %>%
          dplyr::select(c(Comparison, stat_cogs)) %>%
          tidyr::pivot_longer(stat_cogs) %>%
          dplyr::mutate(
            name = paste(Comparison, name),
            value = round(value, 4)
          ) %>%
          dplyr::ungroup() %>%
          dplyr::select(-Comparison)
        
        # Generate new cognostics
        new_cogs <- do.call(cbind, lapply(1:nrow(cogs_to_add), function(row) {
          quick_cog(cogs_to_add$name[row], cogs_to_add$value[row])
        })) %>% dplyr::tibble()
        
        # Add new cognostics, removing when it is NULL 
        if (!is.null(cog_to_trelli)) {
          cog_to_trelli <- cbind(cog_to_trelli, new_cogs) %>% dplyr::tibble()
        } else {
          cog_to_trelli <- new_cogs
        }
        
      }
    }
    
    return(cog_to_trelli)
  }
  
  # Build trelliscope display---------------------------------------------------
  
  # Return a single plot if single_plot is TRUE
  if (single_plot) {
    singleData <- trelliData$trelliData.omics[test_example[1], ]
    return(box_plot_fun(singleData$Nested_DF[[1]], unlist(singleData[1, 1])))
  } else {
    # If test_mode is on, then just build the required panels
    if (test_mode) {
      toBuild <- trelliData$trelliData.omics[test_example, ]
    } else {
      toBuild <- trelliData$trelliData.omics
    }
    
    # Pass parameters to trelli_builder function
    trelli_builder(toBuild = toBuild,
                   cognostics = cognostics, 
                   plotFUN = box_plot_fun,
                   cogFUN = box_cog_fun,
                   path = path,
                   name = name,
                   remove_nestedDF = FALSE,
                   ...)
    
  }
}

#' @name trelli_rnaseq_histogram
#'
#' @title Histogram trelliscope building function for RNA-Seq data
#'
#' @description Specify a plot design and cognostics for the abundance histogram
#'   trelliscope. Main_effects grouping are ignored. Data must be grouped by
#'   edata_cname. For MS/NMR data, use "trelli_abundance_histogram". 
#' @param trelliData A trelliscope data object made by as.trelliData or
#'   as.trelliData.edata, and grouped by edata_cname in trelli_panel_by.
#'   Must be built using seqData. Required.
#' @param cognostics A vector of cognostic options for each plot. Valid entries
#'   are "sample count", "mean lcpm", "median lcpm", "cv lcpm", 
#'   and "skew lcpm". All are included by default. 
#' @param ggplot_params An optional vector of strings of ggplot parameters to
#'   the backend ggplot function. For example, c("ylab('')", "ylim(c(1,2))").
#'   Default is NULL.
#' @param interactive A logical argument indicating whether the plots should be
#'   interactive or not. Interactive plots are ggplots piped to ggplotly (for
#'   now). Default is FALSE.
#' @param path The base directory of the trelliscope application. Default is
#'   Downloads.
#' @param name The name of the display. Default is Trelliscope.
#' @param test_mode A logical to return a smaller trelliscope to confirm plot
#'   and design. Default is FALSE.
#' @param test_example A vector of plot indices to return for test_mode. Default
#'   is 1.
#' @param single_plot A TRUE/FALSE to indicate whether 1 plot (not a
#'   trelliscope) should be returned. Default is FALSE.
#' @param ... Additional arguments to be passed on to the trelli builder
#'
#' @return No return value, builds a trelliscope display of histograms that is stored in `path`
#' 
#' @examplesIf requireNamespace("pmartRdata", quietly = TRUE)
#' \dontrun{
#' library(pmartRdata)
#' 
#' trelliData_seq1 <- as.trelliData.edata(e_data = rnaseq_edata, 
#'                                       edata_cname = "Transcript",
#'                                       omics_type = "seqData")
#' omicsData_seq <- group_designation(omicsData = rnaseq_object, main_effects = c("Virus"))
#' 
#' # Filter low transcript counts
#' omicsData_seq <- applyFilt(filter_object = total_count_filter(omicsData = omicsData_seq), 
#'  omicsData = omicsData_seq, min_count = 15)
#' 
#' # Select a normalization and statistics method (options are 'edgeR', 'DESeq2', and 'voom').
#' # See ?difexp_seq for more details
#' statRes_seq <- diffexp_seq(omicsData = omicsData_seq, method = "voom")
#' 
#' # Generate the trelliData object
#' trelliData_seq2 <- as.trelliData(omicsData = omicsData_seq)
#' trelliData_seq3 <- as.trelliData(statRes = statRes_seq)
#' trelliData_seq4 <- as.trelliData(omicsData = omicsData_seq, statRes = statRes_seq)
#' 
#' # Build the RNA-seq histogram with an edata file. 
#' # Generate trelliData in as.trelliData.edata
#' trelli_panel_by(trelliData = trelliData_seq1, panel = "Transcript") %>% 
#'    trelli_rnaseq_histogram(test_mode = TRUE, test_example = 1:10, path = tempdir())
#' 
#' # Build the RNA-seq histogram with an omicsData object. 
#' # Generate trelliData in as.trelliData
#' trelli_panel_by(trelliData = trelliData_seq2, panel = "Transcript") %>% 
#'    trelli_rnaseq_histogram(test_mode = TRUE, test_example = 1:10, path = tempdir())
#'     
#' # Build the RNA-seq histogram with an omicsData and statRes object. 
#' # Generate trelliData in as.trelliData.
#' trelli_panel_by(trelliData = trelliData_seq4, panel = "Transcript") %>%
#'    trelli_rnaseq_histogram(test_mode = TRUE, test_example = 1:10, 
#'      cognostics = "sample count", path = tempdir())
#'    
#' # Users can modify the plotting function with ggplot parameters and interactivity, 
#' # and can also select certain cognostics.     
#' trelli_panel_by(trelliData = trelliData_seq1, panel = "Transcript") %>% 
#'    trelli_rnaseq_histogram(test_mode = TRUE, test_example = 1:10, 
#'      ggplot_params = c("ylab('')", "xlab('')"), interactive = TRUE,
#'      cognostics = c("mean lcpm", "median lcpm"), path = tempdir())  
#' 
#' \dontshow{closeAllConnections()}
#' }
#'
#' @author David Degnan, Lisa Bramer
#'
#' @export
trelli_rnaseq_histogram <- function(trelliData,
                                       cognostics = c("sample count", "mean lcpm", "median lcpm", "cv lcpm", "skew lcpm"),
                                       ggplot_params = NULL,
                                       interactive = FALSE,
                                       path = .getDownloadsFolder(),
                                       name = "Trelliscope",
                                       test_mode = FALSE,
                                       test_example = 1,
                                       single_plot = FALSE,
                                       ...) {
  # Run initial checks----------------------------------------------------------
  
  # Run generic checks 
  trelli_precheck(trelliData = trelliData, 
                  trelliCheck = "omics",
                  cognostics = cognostics,
                  acceptable_cognostics = c("sample count", "mean lcpm", "median lcpm", "cv lcpm", "skew lcpm"),
                  ggplot_params = ggplot_params,
                  interactive = interactive,
                  test_mode = test_mode, 
                  test_example = test_example,
                  single_plot = single_plot,
                  seqDataCheck = "required",
                  seqText = "Use trelli_abundance_histogram instead.",
                  p_value_thresh = NULL)
  
  
  # Round test example to integer 
  if (test_mode) {
    test_example <- unique(abs(round(test_example)))
  }
  
  # Check that group data is edata_cname
  edata_cname <- pmartR::get_edata_cname(trelliData$omicsData)
  if (edata_cname != attr(trelliData, "panel_by_omics")) {
    stop("trelliData must be grouped by edata_cname.")
  }
  
  # Convert sample count to sample nonzero count
  if ("sample count" %in% cognostics) {
    cognostics[grepl("sample count", cognostics, fixed = T)] <- "sample nonzero count"
  }
  
  # Make histogram function-----------------------------------------------------
  
  # First, generate the histogram function
  hist_plot_fun <- function(DF, title) {
    # Remove NAs
    DF <- DF[!is.na(DF$LCPM), ]
    
    # Build plot
    histogram <- ggplot2::ggplot(DF, ggplot2::aes(x = .data$LCPM)) +
      ggplot2::geom_histogram(bins = 10, fill = "steelblue", color = "black") +
      ggplot2::ggtitle(title) +
      ggplot2::theme_bw() +
      ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5)) +
      ggplot2::xlab("Log Counts per Million (LCPM)") +
      ggplot2::ylab("Frequency")
    
    # Add additional parameters
    if (!is.null(ggplot_params)) {
      for (param in ggplot_params) {
        histogram <- histogram + eval(parse(text = paste0("ggplot2::", param)))
      }
    }
    
    # If interactive, pipe to ggplotly
    if (interactive) {
      histogram <- histogram %>% plotly::ggplotly()
    }
    
    return(histogram)
  }
  
  # Create cognostic function---------------------------------------------------
  
  # Second, create function to return cognostics
  hist_cog_fun <- function(DF, biomolecule) {
    # Set basic cognostics for ungrouped data or in case when data is not split by fdata_cname
    cog <- list(
      "sample nonzero count" = dplyr::tibble(`Sample Non-Zero Count` = trelliscopejs::cog(sum(DF$Count != 0), desc = "Sample Non-Zero Count")),
      "mean lcpm" = dplyr::tibble(`Mean LCPM` = trelliscopejs::cog(round(mean(DF$LCPM, na.rm = TRUE), 4), desc = "Mean LCPM")), 
      "median lcpm" = dplyr::tibble(`Median LCPM` = trelliscopejs::cog(round(median(DF$LCPM, na.rm = TRUE), 4), desc = "Median LCPM")), 
      "cv lcpm" = dplyr::tibble(`CV LCPM` = trelliscopejs::cog(round((sd(DF$LCPM, na.rm = TRUE) / mean(DF$LCPM, na.rm = T)) * 100, 4), desc = "CV LCPM")), 
      "skew lcpm" = dplyr::tibble(`Skew LCPM` = trelliscopejs::cog(round(e1071::skewness(DF$LCPM, na.rm = TRUE), 4), desc= "Skew LCPM"))
    )
    
    # If cognostics are any of the cog, then add them 
    cog_to_trelli <- do.call(dplyr::bind_cols, lapply(cognostics, function(x) {cog[[x]]})) %>% dplyr::tibble()
    
    return(cog_to_trelli)
  }
  
  # Build trelliscope display---------------------------------------------------
  
  # Return a single plot if single_plot is TRUE
  if (single_plot) {
    singleData <- trelliData$trelliData.omics[test_example[1], ]
    return(hist_plot_fun(singleData$Nested_DF[[1]], unlist(singleData[1, 1])))
  } else {
    # If test_mode is on, then just build the required panels
    if (test_mode) {
      toBuild <- trelliData$trelliData.omics[test_example, ]
    } else {
      toBuild <- trelliData$trelliData.omics
    }
    
    # Pass parameters to trelli_builder function
    trelli_builder(toBuild = toBuild,
                   cognostics = cognostics, 
                   plotFUN = hist_plot_fun,
                   cogFUN = hist_cog_fun,
                   path = path,
                   name = name,
                   remove_nestedDF = FALSE,
                   ...)
    
  }
}

#' @name trelli_rnaseq_heatmap
#'
#' @title Heatmap trelliscope building function for RNA-seq data
#'
#' @description Specify a plot design and cognostics for the RNA-seq heatmap
#'   trelliscope. Data must be grouped by an e_meta column. Main_effects order
#'   the y-variables. All statRes data is ignored. 
#'   For MS/NMR data, use "trelli_abundance_heatmap".
#'
#' @param trelliData A trelliscope data object made by as.trelliData, and
#'   grouped by an emeta variable. Must be built using seqData. Required.
#' @param cognostics A vector of cognostic options. Defaults are "sample count", 
#'   "mean LCPM" and "biomolecule count". "sample count" and "mean LCPM"
#'   are reported per group, and "biomolecule count" is the total number of biomolecules
#'   in the biomolecule class (e_meta column).
#' @param ggplot_params An optional vector of strings of ggplot parameters to
#'   the backend ggplot function. For example, c("ylab('')", "xlab('')").
#'   Default is NULL.
#' @param interactive A logical argument indicating whether the plots should be
#'   interactive or not. Interactive plots are ggplots piped to ggplotly. Default is FALSE.
#' @param path The base directory of the trelliscope application. Default is
#'   Downloads.
#' @param name The name of the display. Default is Trelliscope
#' @param test_mode A logical to return a smaller trelliscope to confirm plot
#'   and design. Default is FALSE.
#' @param test_example A vector of plot indices to return for test_mode. Default
#'   is 1.
#' @param single_plot A TRUE/FALSE to indicate whether 1 plot (not a
#'   trelliscope) should be returned. Default is FALSE.
#' @param ... Additional arguments to be passed on to the trelli builder
#'
#' @return No return value, builds a trelliscope display of heatmaps that is stored in `path`
#' 
#' @examplesIf requireNamespace("pmartRdata", quietly = TRUE)
#' \dontrun{
#' library(pmartRdata)
#' 
#' omicsData_seq <- group_designation(omicsData = rnaseq_object, main_effects = c("Virus"))
#' 
#' # Filter low transcript counts
#' omicsData_seq <- applyFilt(filter_object = total_count_filter(omicsData = omicsData_seq), 
#'  omicsData = omicsData_seq, min_count = 15)
#' 
#' # Select a normalization and statistics method (options are 'edgeR', 'DESeq2', and 'voom').
#' # See ?difexp_seq for more details
#' statRes_seq <- diffexp_seq(omicsData = omicsData_seq, method = "voom")
#' 
#' # Generate the trelliData object
#' trelliData_seq2 <- as.trelliData(omicsData = omicsData_seq)
#' trelliData_seq4 <- as.trelliData(omicsData = omicsData_seq, statRes = statRes_seq)
#' 
#' # Build the RNA-seq heatmap with an omicsData object with emeta variables. 
#' # Generate trelliData in as.trelliData.
#' trelli_panel_by(trelliData = trelliData_seq2, panel = "Gene") %>% 
#'    trelli_rnaseq_heatmap(test_mode = TRUE, test_example = c(1532, 1905, 6134), path = tempdir())
#'    
#' # Users can modify the plotting function with ggplot parameters and interactivity, 
#' # and can also select certain cognostics.     
#' trelli_panel_by(trelliData = trelliData_seq4, panel = "Gene") %>% 
#'    trelli_rnaseq_heatmap(test_mode = TRUE, test_example = c(1532, 1905, 6134), 
#'      ggplot_params = c("ylab('')", "xlab('')"), 
#'      interactive = TRUE, cognostics = c("biomolecule count"), path = tempdir())  
#' 
#' \dontshow{closeAllConnections()}
#' }
#' @author David Degnan, Lisa Bramer
#'
#' @export
trelli_rnaseq_heatmap <- function(trelliData,
                                  cognostics = c("sample count", "mean LCPM", "biomolecule count"),
                                  ggplot_params = NULL,
                                  interactive = FALSE,
                                  path = .getDownloadsFolder(),
                                  name = "Trelliscope",
                                  test_mode = FALSE,
                                  test_example = 1,
                                  single_plot = FALSE,
                                  ...) {
  # Run initial checks----------------------------------------------------------
  
  # Run generic checks 
  trelli_precheck(trelliData = trelliData, 
                  trelliCheck = "omics",
                  cognostics = cognostics,
                  acceptable_cognostics = c("sample count", "mean LCPM", "biomolecule count"),
                  ggplot_params = ggplot_params,
                  interactive = interactive,
                  test_mode = test_mode, 
                  test_example = test_example,
                  single_plot = single_plot,
                  seqDataCheck = "required",
                  seqText = "Use trelli_abundance_heatmap instead.",
                  p_value_thresh = NULL)
  
  # Round test example to integer 
  if (test_mode) {
    test_example <- unique(abs(round(test_example)))
  }
  
  # Check that group data is grouped by an e_meta variable
  if (attr(trelliData, "panel_by_omics") %in% attr(trelliData, "emeta_col") == FALSE) {
    stop("trelliData must be paneled_by an e_meta column.")
  }
  
  # If no group designation, set cognostics to NULL.
  if (is.null(attributes(trelliData$omicsData)$group_DF)) {
    cognostics <- NULL
  }
  
  # Get the edata variable name
  edata_cname <- get_edata_cname(trelliData$omicsData)
  
  # Make heatmap function-------------------------------------------------------
  
  # First, generate the heatmap function
  hm_plot_fun <- function(DF, title) {
    # Get fdata_cname
    fdata_cname <- get_fdata_cname(trelliData$omicsData)
    
    # If group designation was set, then convert Group to a factor variable
    if (!is.null(attributes(trelliData$omicsData)$group_DF)) {
      theLevels <- attr(trelliData$omicsData, "group_DF") %>%
        dplyr::arrange(Group) %>%
        dplyr::select(dplyr::all_of(fdata_cname)) %>%
        unlist()
      DF[, fdata_cname] <- factor(unlist(DF[, fdata_cname]), levels = theLevels)
    } else {
      DF[, fdata_cname] <- as.factor(unlist(DF[, fdata_cname]))
    }
    
    # Build plot: this should be edata_cname
    hm <- ggplot2::ggplot(DF, ggplot2::aes(x = as.factor(.data[[edata_cname]]), y = .data[[fdata_cname]], fill = .data$LCPM)) +
      ggplot2::geom_tile() +
      ggplot2::theme_bw() +
      ggplot2::ylab("Sample") +
      ggplot2::xlab("Biomolecule") +
      ggplot2::theme(
        plot.title = ggplot2::element_text(hjust = 0.5),
        axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1)
      ) +
      ggplot2::scale_fill_gradient(low = "blue", high = "red", na.value = "white") +
      ggplot2::ggtitle(title)
    
    # Add additional parameters
    if (!is.null(ggplot_params)) {
      for (param in ggplot_params) {
        hm <- hm + eval(parse(text = paste0("ggplot2::", param)))
      }
    }
    
    # If interactive, pipe to ggplotly
    if (interactive) {
      hm <- hm %>% plotly::ggplotly()
    }
    
    return(hm)
  }
  
  # Create cognostic function---------------------------------------------------
  
  hm_cog_fun <- function(DF, emeta_var) {
    
    
    # Adjust names
    if ("sample count" %in% cognostics) {cognostics[grepl("sample count", cognostics)] <- "Sample Non-Zero Count"}
    if ("biomolecule count" %in% cognostics) {cognostics[grepl("biomolecule count", cognostics)] <- "Biomolecule Non-Zero Count"}
    
    # Subset down the dataframe down to group, unnest the dataframe,
    # pivot_longer to comparison, subset columns to requested statistics,
    # switch name to a more specific name
    cogs_to_add <- DF %>%
      dplyr::group_by(Group) %>%
      dplyr::summarise(
        "Sample Non-Zero Count" = sum(Count != 0), 
        "mean LCPM" = round(mean(.data$LCPM, na.rm = TRUE), 4),
      ) %>%
      tidyr::pivot_longer(c(.data$`Sample Non-Zero Count`, .data$`mean LCPM`)) %>%
      dplyr::filter(name %in% cognostics) %>%
      dplyr::mutate(name = paste(Group, name)) %>%
      dplyr::ungroup() %>%
      dplyr::select(-Group) 
    
    if ("Biomolecule Non-Zero Count" %in% cognostics) {
      cogs_to_add <- rbind(
        cogs_to_add, 
        data.frame(
          name = "Biomolecule Non-Zero Count",
          value = DF[[edata_cname]] %>% unique() %>% length()
        )
      )
    }
    
    # Add new cognostics 
    cog_to_trelli <- do.call(cbind, lapply(1:nrow(cogs_to_add), function(row) {
      quick_cog(cogs_to_add$name[row], cogs_to_add$value[row])
    })) %>% dplyr::tibble()
    
    return(cog_to_trelli)
  }
  
  # Build trelliscope display---------------------------------------------------
  
  # Return a single plot if single_plot is TRUE
  if (single_plot) {
    singleData <- trelliData$trelliData.omics[test_example[1], ]
    return(hm_plot_fun(singleData$Nested_DF[[1]], unlist(singleData[1, 1])))
  } else {
    # If test_mode is on, then just build the required panels
    if (test_mode) {
      toBuild <- trelliData$trelliData.omics[test_example, ]
    } else {
      toBuild <- trelliData$trelliData.omics
    }
    
    # Pass parameters to trelli_builder function
    trelli_builder(toBuild = toBuild,
                   cognostics = cognostics, 
                   plotFUN = hm_plot_fun,
                   cogFUN = hm_cog_fun,
                   path = path,
                   name = name,
                   remove_nestedDF = FALSE,
                   ...) 
    
  }
}

#' @name trelli_rnaseq_nonzero_bar
#'
#' @title Bar chart trelliscope building function for Non-Zero counts in RNA-seq data
#'
#' @description Specify a plot design and cognostics for the Non-Zero barchart
#'    trelliscope. Non-Zeroes are displayed per panel_by variable. Main_effects
#'    data is used to split samples when applicable. For MS/NMR data, use 
#'    "trelli missingness bar". 
#'
#' @param trelliData A trelliscope data object made by as.trelliData.edata or
#'    as.trelliData. Must be built using seqData. Required.
#' @param cognostics A vector of cognostic options for each plot. Defaults are "total count",
#'    "non-zero count", and "non-zero proportion". If grouping
#'    data is included, all cognostics will be reported per group. If the 
#'    trelliData is paneled by a biomolecule, the counts and proportion we be 
#'    samples. If paneled by a sample or biomolecule class, the counts and proportions
#'    will be biomolecules.
#' @param proportion A logical to determine whether plots should display counts
#'    or proportions. Default is TRUE.
#' @param ggplot_params An optional vector of strings of ggplot parameters to
#'    the backend ggplot function. For example, c("ylab('')", "xlab('')").
#'    Default is NULL.
#' @param interactive A logical argument indicating whether the plots should be
#'    interactive or not. Interactive plots are ggplots piped to ggplotly (for
#'    now). Default is FALSE.
#' @param path The base directory of the trelliscope application. Default is
#'    Downloads.
#' @param name The name of the display. Default is Trelliscope.
#' @param test_mode A logical to return a smaller trelliscope to confirm plot
#'    and design. Default is FALSE.
#' @param test_example A vector of plot indices to return for test_mode. Default
#'    is 1.
#' @param single_plot A TRUE/FALSE to indicate whether 1 plot (not a
#'    trelliscope) should be returned. Default is FALSE.
#' @param ... Additional arguments to be passed on to the trelli builder
#'   
#' @return No return value, builds a trelliscope display of bar charts that is stored in `path`
#' 
#' @examplesIf requireNamespace("pmartRdata", quietly = TRUE)
#' \dontrun{
#' library(pmartRdata)
#' 
#' trelliData_seq1 <- as.trelliData.edata(e_data = rnaseq_edata, 
#'                                       edata_cname = "Transcript",
#'                                       omics_type = "seqData")
#' omicsData_seq <- group_designation(omicsData = rnaseq_object, main_effects = c("Virus"))
#' 
#' # Filter low transcript counts
#' omicsData_seq <- applyFilt(filter_object = total_count_filter(omicsData = omicsData_seq), 
#'  omicsData = omicsData_seq, min_count = 15)
#' 
#' # Select a normalization and statistics method (options are 'edgeR', 'DESeq2', and 'voom').
#' # See ?difexp_seq for more details
#' statRes_seq <- diffexp_seq(omicsData = omicsData_seq, method = "voom")
#' 
#' # Generate the trelliData object
#' trelliData_seq2 <- as.trelliData(omicsData = omicsData_seq)
#' trelliData_seq3 <- as.trelliData(statRes = statRes_seq)
#' trelliData_seq4 <- as.trelliData(omicsData = omicsData_seq, statRes = statRes_seq)
#' 
#' # Build the non-zero bar plot with an edata file. Generate trelliData in as.trelliData.edata
#' trelli_panel_by(trelliData = trelliData_seq1, panel = "Transcript") %>% 
#'   trelli_rnaseq_nonzero_bar(test_mode = TRUE, test_example = 1:10, path = tempdir())
#' trelli_panel_by(trelliData = trelliData_seq1, panel = "Sample") %>% 
#'   trelli_rnaseq_nonzero_bar(test_mode = TRUE, test_example = 1:10, 
#'    cognostics = "non-zero proportion", path = tempdir())
#' 
#' # Build the non-zero bar plot with an omicsData object. Generate trelliData in as.trelliData
#' trelli_panel_by(trelliData = trelliData_seq2, panel = "Transcript") %>% 
#'   trelli_rnaseq_nonzero_bar(test_mode = TRUE, test_example = 1:10, path = tempdir())
#' 
#' # Build the non-zero bar plot with a statRes object. Generate trelliData in as.trelliData
#' trelli_panel_by(trelliData = trelliData_seq3, panel = "Transcript") %>%
#'   trelli_rnaseq_nonzero_bar(test_mode = TRUE, test_example = 1:10,
#'                          cognostics = c("non-zero proportion"), path = tempdir())
#' 
#' # Build the non-zero bar plot with an omicsData and statRes object. 
#' # Generate trelliData in as.trelliData.
#' trelli_panel_by(trelliData = trelliData_seq4, panel = "Gene") %>%
#'   trelli_rnaseq_nonzero_bar(test_mode = TRUE, test_example = 1:10, path = tempdir()) 
#' 
#' # Or making the plot interactive 
#' trelli_panel_by(trelliData = trelliData_seq2, panel = "Transcript") %>% 
#'    trelli_rnaseq_nonzero_bar(test_mode = TRUE, test_example = 1:5, 
#'      interactive = TRUE, path = tempdir())
#'    
#' # Or visualize only count data 
#' trelli_panel_by(trelliData = trelliData_seq2, panel = "Transcript") %>% 
#'    trelli_rnaseq_nonzero_bar(test_mode = TRUE, test_example = 1:5, 
#'      cognostics = "non-zero count", proportion = FALSE, path = tempdir())
#' 
#' \dontshow{closeAllConnections()}   
#' }
#'
#' @author David Degnan, Lisa Bramer
#'
#' @export
trelli_rnaseq_nonzero_bar <- function(trelliData,
                                   cognostics = c("total count", "non-zero count", "non-zero proportion"),
                                   proportion = TRUE,
                                   ggplot_params = NULL,
                                   interactive = FALSE,
                                   path = .getDownloadsFolder(),
                                   name = "Trelliscope",
                                   test_mode = FALSE,
                                   test_example = 1,
                                   single_plot = FALSE,
                                   ...) {
  # Run initial checks----------------------------------------------------------
  
  # Run generic checks 
  trelli_precheck(trelliData = trelliData, 
                  trelliCheck = c("either"),
                  cognostics = cognostics,
                  acceptable_cognostics = c("total count", "non-zero count", "non-zero proportion"),
                  ggplot_params = ggplot_params,
                  interactive = interactive,
                  test_mode = test_mode, 
                  test_example = test_example,
                  single_plot = single_plot,
                  seqDataCheck = "required",
                  seqText = "Use trelli_missingness_bar instead.",
                  p_value_thresh = NULL)
  
  # Check that proportion is a non NA logical
  if (!is.logical(proportion) | is.na(proportion)) {
    stop("proportion must be a TRUE or FALSE.")
  }
  
  # Round test example to integer 
  if (test_mode) {
    test_example <- unique(abs(round(test_example)))
  }
  
  # Determine if the trelliData is paneled by the edata column
  if (is.na(attr(trelliData, "panel_by_omics"))) {
    paneled_by_edata <- attr(trelliData, "panel_by_stat") == get_edata_cname(trelliData$statRes)
  } else {
    paneled_by_edata <- attr(trelliData, "panel_by_omics") == get_edata_cname(trelliData$omicsData)
  }
  
  # Generate a function to make nonzero dataframes------------------------------
  get_nonzero_DF <- function(DF) {
    
    # If there is no omics data, use statRes
    if (is.null(trelliData$omicsData)) {
      
      # Add to total counts
      NonZero <- data.table::data.table(
        Group = gsub("NonZero_Count_", "", colnames(DF)),
        `Non-Zero Count` = unlist(DF)
      ) %>%
        merge(totalCounts, by = "Group") %>%
        dplyr::mutate(
          `Zero Count` = Total - .data$`Non-Zero Count`,
          `Zero Proportion` = round(.data$`Zero Count` / Total, 4),
          `Non-Zero Proportion` = round(.data$`Non-Zero Count` / Total, 4)
        ) %>%
        dplyr::select(-Total) %>%
        dplyr::mutate(Group, .data$`Zero Count`, .data$`Non-Zero Count`, .data$`Zero Proportion`, .data$`Non-Zero Proportion`)
    
      } else {
      
      # Add a blank group if no group designation was given
      if (is.null(attributes(trelliData$omicsData)$group_DF)) {
        DF$Group <- "x"
      }
      
      # Create nonzero data.frame
      NonZero <- DF %>%
        dplyr::group_by(Group) %>%
        dplyr::summarise(
          `Zero Count` = sum(Count == 0),
          `Non-Zero Count` = sum(Count != 0),
          `Zero Proportion` = round(.data$`Zero Count` / sum(c(.data$`Zero Count`, .data$`Non-Zero Count`)), 4),
          `Non-Zero Proportion` = round(.data$`Non-Zero Count` / sum(c(.data$`Zero Count`, .data$`Non-Zero Count`)), 4)
        )
    }
    
    return(NonZero)
  }
  
  # Make nonzero bar function---------------------------------------------------
  
  # First, generate the boxplot function
  nonzero_bar_plot_fun <- function(DF, title) {
    
    # Get non-zero dataframe
    NonZero <- get_nonzero_DF(DF)
    
    # Subset based on count or proportion
    if (proportion) {
      NZPlotDF <- NonZero %>%
        dplyr::select(c(Group, .data$`Zero Proportion`, .data$`Non-Zero Proportion`)) %>%
        dplyr::rename(Zero = .data$`Zero Proportion`, `Non-Zero` = .data$`Non-Zero Proportion`) %>%
        tidyr::pivot_longer(c(.data$Zero, .data$`Non-Zero`)) %>%
        dplyr::mutate(name = factor(name, levels = c("Zero", "Non-Zero")))
      ylab <- "Proportion"
    } else {
      NZPlotDF <- NonZero %>%
        dplyr::select(c(Group, .data$`Zero Count`, .data$`Non-Zero Count`)) %>%
        dplyr::rename(Zero = .data$`Zero Count`, `Non-Zero` = .data$`Non-Zero Count`) %>%
        tidyr::pivot_longer(c(.data$Zero, .data$`Non-Zero`)) %>%
        dplyr::mutate(name = factor(name, levels = c("Zero", "Non-Zero")))
      ylab <- "Count"
    }
    
    # Build plot
    zero_bar <- ggplot2::ggplot(NZPlotDF, ggplot2::aes(x = Group, y = value, fill = name)) +
      ggplot2::geom_bar(stat = "identity", position = "stack", color = "black") +
      ggplot2::theme_bw() +
      ggplot2::ggtitle(title) +
      ggplot2::ylab(ylab) +
      ggplot2::scale_fill_manual(values = c("Non-Zero" = "steelblue", "Zero" = "black")) +
      ggplot2::theme(
        plot.title = ggplot2::element_text(hjust = 0.5),
        legend.title = ggplot2::element_blank()
      )
    
    # Remove x axis if no groups
    if (is.null(attributes(trelliData$omicsData)$group_DF) & stats_mode == FALSE) {
      zero_bar <- zero_bar + ggplot2::theme(
        axis.title.x = ggplot2::element_blank(),
        axis.ticks.x = ggplot2::element_blank(),
        axis.text.x = ggplot2::element_blank()
      )
    }
    
    # Add additional parameters
    if (!is.null(ggplot_params)) {
      for (param in ggplot_params) {
        zero_bar <- zero_bar + eval(parse(text = paste0("ggplot2::", param)))
      }
    }
    
    # If interactive, pipe to ggplotly
    if (interactive) {
      zero_bar <- zero_bar %>% plotly::ggplotly()
    }
    
    return(zero_bar)
  }
  
  # Create the cognostic function-----------------------------------------------
  
  # Next, generate the cognostic function
  nonzero_bar_cog_fun <- function(DF, GroupingVar) {
    
    # Get nonzero dataframe
    NonZero <- get_nonzero_DF(DF)
    
    # Build cognostics
    NZ_Cog <- NonZero %>%
      dplyr::mutate(`total count` = .data$`Non-Zero Count` + .data$`Zero Count`) %>%
      dplyr::select(-c(.data$`Zero Count`, .data$`Zero Proportion`)) %>%
      dplyr::rename(`non-zero count` = .data$`Non-Zero Count`, `non-zero proportion` = .data$`Non-Zero Proportion`) %>%
      tidyr::pivot_longer(c(.data$`non-zero count`, .data$`non-zero proportion`, .data$`total count`)) %>%
      dplyr::filter(name %in% cognostics)
    
    # Expand the name depending on whether the counts are samples or biomolecules
    if (paneled_by_edata & nrow(NZ_Cog) > 0) {
      NZ_Cog <- NZ_Cog %>% 
        dplyr::mutate(
          name = lapply(name, function(x) {
            splitNames <- strsplit(x, " ") %>% unlist()
            return(paste(splitNames[1], "sample", splitNames[2]))
          }) %>% unlist()
        )
    } else if (nrow(NZ_Cog) > 0) {
      NZ_Cog <- NZ_Cog %>% 
        dplyr::mutate(
          name = lapply(name, function(x) {
            splitNames <- strsplit(x, " ") %>% unlist()
            return(paste(splitNames[1], "biomolecule", splitNames[2]))
          }) %>% unlist()
        )
    }
    
    # Add grouping data if there's more than one group 
    if (length(unique(NZ_Cog$Group)) > 1) {
      NZ_Cog <- NZ_Cog %>% dplyr::mutate(name = paste(Group, name))
    }
    
    # Remove group column
    NZ_Cog <- NZ_Cog %>% dplyr::select(c(name, value))
    
    # Return NULL if there's no cognostics 
    if (nrow(NZ_Cog) == 0) {
      return(NULL)
    }
    
    # Generate cognostics 
    cog_to_trelli <- do.call(cbind, lapply(1:nrow(NZ_Cog), function(row) {
      quick_cog(name = NZ_Cog$name[row], value = NZ_Cog$value[row])
    })) %>% dplyr::tibble()
    
    return(cog_to_trelli)
  }
  
  # Build trelliscope display---------------------------------------------------
  
  # If test_mode is on, then just build the required panels. If the data is statRes, we
  # will need to restructure the data a bit.
  if (!is.null(trelliData$trelliData.omics)) {
    stats_mode <- FALSE
    if (test_mode) {
      toBuild <- trelliData$trelliData.omics[test_example, ]
    } else {
      toBuild <- trelliData$trelliData.omics
    }
  } else {
    stats_mode <- TRUE
    
    # Get the edata column name
    edata_cname <- get_edata_cname(trelliData$statRes)
    
    # Get the columns with counts
    count_cols <- colnames(trelliData$statRes)[grepl("Count", colnames(trelliData$statRes))]

    # Build toBuild dataframe
    toBuild <- trelliData$statRes %>%
      dplyr::select(c(edata_cname, count_cols)) %>%
      dplyr::group_by(!!dplyr::sym(edata_cname)) %>%
      tidyr::nest() %>%
      dplyr::ungroup() %>%
      dplyr::rename(Nested_DF = data) 
    
    # Save total counts 
    totalCounts <- attr(trelliData$statRes, "group_DF")$Group %>% 
      table(dnn = "Group") %>% 
      data.frame() %>% 
      dplyr::rename(Total = Freq)
    
    if (test_mode) {
      toBuild <- toBuild[test_example, ]
    }
  }
  
  # Return a single plot if single_plot is TRUE
  if (single_plot) {
    singleData <- toBuild[test_example[1], ]
    return(nonzero_bar_plot_fun(singleData$Nested_DF[[1]], unlist(singleData[1, 1])))
  } else {
    # Pass parameters to trelli_builder function
    if (!is.null(trelliData$omicsData)) {
      trelli_builder(toBuild = toBuild,
                     cognostics = cognostics, 
                     plotFUN = nonzero_bar_plot_fun,
                     cogFUN = nonzero_bar_cog_fun,
                     path = path,
                     name = name,
                     remove_nestedDF = FALSE,
                     ...)
    } else {
      trelli_builder(toBuild = toBuild,
                     cognostics = cognostics, 
                     plotFUN = nonzero_bar_plot_fun,
                     cogFUN = nonzero_bar_cog_fun,
                     path = path,
                     name = name,
                     remove_nestedDF = TRUE,
                     ...)
    }
  }
}