plotVoom.R

# Gabriel Hoffman
# April 6, 2021
#
# plot voom applied to pseudo-bulk from each cell type


#' Plot voom curves from each cell type
#'
#' Plot voom curves from each cell type
#'
#' @param x dreamletProcessedData
#' @param ncol number of columns in the plot
#' @param alpha transparency of points
#' @param assays which assays to plot
#' @param ... other arguments
#'
#' @return Plot of mean-variance trend
#'
#' @examples
#' library(muscat)
#' library(SingleCellExperiment)
#'
#' data(example_sce)
#'
#' # create pseudobulk for each sample and cell cluster
#' pb <- aggregateToPseudoBulk(example_sce,
#'   assay = "counts",
#'   cluster_id = "cluster_id",
#'   sample_id = "sample_id",
#'   verbose = FALSE
#' )
#'
#' # voom-style normalization
#' res.proc <- processAssays(pb, ~group_id)
#'
#' # Show mean-variance trend from voom
#' plotVoom(res.proc)
#'
#' # plot for first two cell types
#' plotVoom(res.proc[1:2])
#'
#' @export
#' @docType methods
#' @rdname plotVoom-methods
setGeneric(
  "plotVoom",
  function(x, ncol = 3, alpha = .5, ...) {
    standardGeneric("plotVoom")
  }
)


#' @rdname plotVoom-methods
#' @aliases plotVoom,dreamletProcessedData,dreamletProcessedData-method
setMethod(
  "plotVoom", "dreamletProcessedData",
  function(x, ncol = 3, alpha = .5, assays = names(x)) {
    # Pass R CMD check
    y <- NULL

    # intersect preserving order from assays
    assays <- intersect(assays, names(x))
    if (length(assays) == 0) stop("No valid assays selected")

    # get common range across all plots
    ###################################
    df_range <- lapply(assays, function(id) {
      if (!is.null(x[[id]]$voom.xy)) {
        res <- with(x[[id]]$voom.xy, data.frame(range(x), range(y), id = id))
      } else {
        res <- NULL
      }
      res
    })
    df_range <- do.call(rbind, df_range)

    if (is.null(df_range)) {
      stop("Voom was not run on this object")
    }

    xlim <- range(df_range$range.x.)
    ylim <- c(0, max(df_range$range.y.))

    xlab <- bquote(log[2](counts + 0.5))
    ylab <- bquote(sqrt(standard ~ deviation))

    # only included assays were voom succeeded
    validAssays <- droplevels(factor(unique(df_range)$id, assays))

    # make data.frame of points
    df.list <- lapply(levels(validAssays), function(id) {
      with(x[[id]]$voom.xy, data.frame(id, x, y))
    })
    df_points <- do.call(rbind, df.list)
    df_points$id <- droplevels(factor(df_points$id, assays))
    df_points <- df_points[order(df_points$id), ]

    # make data.frame of curves
    df.list <- lapply(levels(validAssays), function(id) {
      with(x[[id]]$voom.line, data.frame(id, x, y))
    })
    df_curve <- do.call(rbind, df.list)

    ggplot(df_points, aes(x, y)) +
      geom_point(size = 0.1, alpha = alpha) +
      theme_classic() +
      theme(aspect.ratio = 1, plot.title = element_text(hjust = 0.5)) +
      facet_wrap(~id, ncol = ncol) +
      xlab(xlab) +
      ylab(ylab) +
      xlim(xlim) +
      ylim(ylim) +
      geom_line(data = df_curve, aes(x, y), color = "red")
  }
)



#' @rdname plotVoom-methods
#' @aliases plotVoom,list,list-method
setMethod(
  "plotVoom", "EList",
  function(x, ncol = 3, alpha = .5) {
    # Pass R CMD check
    y <- NULL

    # get common range across all plots
    ###################################

    df_range <- with(x$voom.xy, data.frame(range(x), range(y)))

    xlim <- range(df_range$range.x.)
    ylim <- c(0, max(df_range$range.y.))

    xlab <- bquote(log[2](counts + 0.5))
    ylab <- bquote(sqrt(standard ~ deviation))

    # make data.frame of points
    df_points <- with(x$voom.xy, data.frame(x, y))

    # make data.frame of curves
    df_curve <- with(x$voom.line, data.frame(x, y))

    ggplot(df_points, aes(x, y)) +
      geom_point(size = 0.1, alpha = alpha) +
      theme_classic() +
      theme(aspect.ratio = 1, plot.title = element_text(hjust = 0.5)) +
      xlab(xlab) +
      ylab(ylab) +
      xlim(xlim) +
      ylim(ylim) +
      geom_line(data = df_curve, aes(x, y), color = "red")
  }
)