Table
In CALANGO: Comparative Analysis with Annotation-Based Genomic Components

Some columns are not visible by default.

knitr::opts_chunk$set(echo = TRUE)

# multiple scatterplots, with confidence intervals, in a single html page

#creating plot titles and axes labels
f <- list(size = 18, color = "black") # family = "Verdana",

title_p1 <- list(text      = "Raw data",
                 font      = f,
                 xref      = "Phenotype",
                 yref      = "Annotation term frequency",
                 yanchor   = "bottom",
                 xanchor   = "center",
                 align     = "center",
                 x         = 0.5,
                 y         = 1,
                 showarrow = FALSE)

title_p2      <- title_p1
title_p2$text <- "Rank data"
title_p2$xref <- "Phenotype rank"
title_p2$yref <- "Annotation term frequency rank"

title_p3      <- title_p1
title_p3$text <- "Phylogeny-aware linear model data"
title_p3$xref <- "Contrasts of phenotypes"
title_p3$yref <- "Contrasts of annotation term frequencies"

# TODO: multicore-parallelize this for loop.
for (i in seq_along(df_cutoff$name)){#df_cutoff$name[1:length(df_cutoff[, 1])]) { # get filtered to plot a lot

  fname <- df_cutoff$name[i]

  #output dir
  file <- paste0(cpd, gsub(":", "_", fname, fixed = TRUE), ".html")

  # setting up data
  Xdf         <- defs$x
  names(Xdf)  <- "X_var"
  Xdf$feature <- defs$y[, fname] / defs$denominator
  ID          <- row.names(Xdf)
  group <-defs$groups
  p1 <- ggplot2::ggplot(data    = Xdf, 
                        mapping = ggplot2::aes(x     = X_var, 
                                               y     = feature,
                                               label = ID
                                               )) + 
    ggplot2::geom_point() +
    #    expand_limits(x = 0, y = 0) +
    ggplot2::geom_smooth(method = "lm", formula = y ~ x, se = TRUE) + 
    ggplot2::theme_bw() +
    ggplot2::labs(x = "Phenotype value", 
                  y = "Annotation frequency")

  Xdf           <- as.data.frame(rank(defs$x[, 1]))
  rownames(Xdf) <- rownames(defs$x)
  names(Xdf)    <- "X_var"
  Xdf$feature   <- rank(defs$y[, fname] / defs$denominator)

  p2 <- ggplot2::ggplot(data    = Xdf, 
                        mapping = ggplot2::aes(x     = X_var, 
                                               y     = feature,
                                               label = ID)) + 
    ggplot2::geom_point() +
    ggplot2::geom_smooth(method = "loess", formula = y ~ x, se = TRUE) + 
    ggplot2::theme_bw() +
    ggplot2::labs(x = "Phenotype rank", 
                  y = "Annotation frequency (rank)")

  # TODO: plots only consider "pic". If we implement "gls", maybe we'll need to 
  # change something here too.
  tmp_x        <- defs$x[, 1]
  names(tmp_x) <- rownames(defs$x)
  contrast_x   <- ape::pic(x = tmp_x, phy = defs$tree)

  tmp_y        <- defs$y[, fname] / defs$denominator
  names(tmp_y) <- rownames(defs$x)
  contrast_y   <- ape::pic(x = tmp_y, phy = defs$tree)

  Xdf   <- data.frame(contrast_x, contrast_y)
  model <- stats::lm(contrast_y ~ contrast_x + 0)

  p3 <- ggplot2::ggplot(data    = Xdf, 
                        mapping = ggplot2::aes(x     = contrast_x, 
                                               y     = contrast_y, 
                                               label = rownames(Xdf))) +
    ggplot2::geom_point() +
    ggplot2::geom_abline(slope     = model$coefficients[1], 
                         intercept = 0, 
                         color     = c("#3366FFFF"), 
                         size      = 1) +
    ggplot2::theme_bw() +
    ggplot2::labs(x = "contrasts for phenotype", 
                  y = "Contrasts for annotation frequency")
  #             ggplot2::geom_smooth(method = "lm", se = FALSE) +
  #    ggplot2::geom_line(data = fortify(model), aes(x = contrast_x, y = .fitted))
  #             ggtitle(paste0("phylogeny-aware linear model data for "), i),
  #    ggtitle("Representation of raw data             Representation of rank data                Representation of phylogenetic-aware linear model data")

  p4 <- plotly::subplot(p1, p2, p3, shareX = TRUE, shareY = FALSE, titleY = TRUE)

  p1ly <- plotly::ggplotly(p4,
                            tooltip = c('label','X_var','Normalized feature'))

  htmlwidgets::saveWidget(p1ly, file = file,
                          libdir = 'lib',
                          selfcontained = FALSE)
}

for (i in seq_along(df_cutoff$name)){

  fname <- df_cutoff$name[i]

  #output dir
  file <- paste0(cpd, gsub(":", "_", fname, fixed = TRUE), '_boxplot.html')

  # file_name <- paste0(fname, "_boxplot_per_group.pdf")
  # file_name <- gsub(":", "_", file_name)

  tmp <- data.frame(defs$x, defs$short.name, defs$groups, defs$y[fname], (defs$y[fname]/defs$denominator))

  colnames(tmp) <- c("cell_type", "short", "group", "count", "norm")

  my_pallete <- data.frame(group  = unique(tmp$group),
                           colour = I(CALANGO_brewer_pal(length(unique(tmp$group)), 
                                                       name = 'Set1')))
  tmp$fill <- my_pallete$colour[match(tmp$group, my_pallete$group)]

  tmp$group <- stats::reorder(tmp$group, tmp$count, FUN = median)
  p1 <- ggplot2::ggplot(tmp, ggplot2::aes(x = group,y = count)) + 
    ggplot2::geom_violin(ggplot2::aes(fill = I(fill)), 
                         scale = "width", alpha = 0.25) +  
    ggplot2::geom_boxplot(ggplot2::aes(fill = I(fill)), 
                          alpha = 0.3) + 
    ggplot2::geom_jitter(width = 0.05, height = 0, size = .5) +
    ggplot2::theme(axis.text.x  = ggplot2::element_text(angle = 45, hjust = 1), 
                   axis.title.x = ggplot2::element_blank())

  tmp$group <- stats::reorder(tmp$group, tmp$norm, FUN = median)
  p2 <- p1 %+% tmp +
    ggplot2::aes(y = norm) + 
    ggplot2::annotate("text", y = 1.02 * max(tmp$norm), x = 1, label = "Norm")

  p1 <- p1 + 
    ggplot2::annotate("text", y = 1.02 * max(tmp$count), x = 1, label = "Count") + 
    ggplot2::ggtitle(paste0(fname, " - ", defs$annotation.cor[fname]))

  prow <- plotly::subplot(plotly::style(p1, showlegend = FALSE, width = .1), 
                          plotly::style(p2, showlegend = FALSE, width = .1),
                          shareY = FALSE, titleY = FALSE)

  p3 <- plotly::ggplotly(prow, tooltip = c("x", "y"))

  htmlwidgets::saveWidget(p3,
                          file = file,
                          libdir = 'lib',
                          selfcontained = FALSE
  )
}

# CHECK HERE - NAMES, INDICES, URL ETC.

dtable <- df_cutoff

#build links to scatterplots
dtable$corr_plots <- paste0('<a  target=_blank href="', 
                            paste0('correlation_Plots/', 
                                   gsub(":", "_", df_cutoff$name, fixed = TRUE),
                                   '.html'),
                            '">', df_cutoff$name,'</a>') 

#build links to boxplots
dtable$boxplots <- paste0('<a  target=_blank href="', 
                          paste0('correlation_Plots/', 
                                 gsub(":", "_", df_cutoff$name, fixed = TRUE),
                                 '_boxplot.html'),
                          '">', df_cutoff$name,'</a>')


dtable <- DT::datatable(dtable,
                        escape = FALSE, 
                        filter = 'bottom',
                        extensions = 'Buttons', 
                        options = list(dom = 'Bfrtip', 
                                       buttons = c('colvis','csv'),
                                       columnDefs = list(list(visible = FALSE,
                                                              targets = 4:(length(df_cutoff) - 2))))) # prepare table

dtable