@@ -12,6 +12,11 @@

 #'

 #' @return a ggplot object containing the aggregate methylation trend.

 #'

+#' @examples

+#' nmr <- load_example_nanomethresult()

+#'

+#' plot_agg_regions(nmr, NanoMethViz::exons(nmr))

+#'

 #' @export

 plot_agg_regions <- function(

     x,

@@ -58,6 +63,7 @@ plot_agg_regions <- function(

     methy_data <- methy_data %>%

         dplyr::bind_rows(.id = "group")

+    # average methylation across relative coordinates

     if (!is.null(groups)) {

         methy_data <- methy_data %>%

         dplyr::group_by(.data$group, .data$rel_pos) %>%

@@ -70,39 +76,34 @@ plot_agg_regions <- function(

             dplyr::ungroup()

     }

+    # take binned means

+    grid_size <- 2^12

+    binned_pos_df <- .get_grid(grid_size = 2^12, rel_pos = methy_data$rel_pos)

+

+    kb_marker <- round(flank / 1000, 1)

+    labels <- c(glue::glue("-{kb_marker}kb"), "start", "end", glue::glue("+{kb_marker}kb"))

+

+    methy_data <- methy_data %>%

+        dplyr::mutate(interval = cut(.data$rel_pos, breaks = grid_size)) %>%

+        dplyr::group_by(.data$interval, .drop = TRUE) %>%

+        dplyr::summarise(methy_prob = mean(.data$methy_prob)) %>%

+        dplyr::ungroup() %>%

+        dplyr::left_join(binned_pos_df, by = "interval")

+

     # set up plot

     p <- ggplot2::ggplot() +

         ggplot2::ylim(c(0, 1)) +

-        ggplot2::theme_minimal()

+        ggplot2::theme_minimal() +

+        .agg_geom_smooth(methy_data, span = span, group = !is.null(groups))

-    # take binned means

-    grid_size <- 2^12

-    binned_pos <- seq(-1.1/3, 1 + 1.1/3, length.out = grid_size+ 2)[2:(1 + grid_size)]

-    methy_data <- methy_data %>%

-        mutate(interval = cut(.data$rel_pos, breaks = grid_size)) %>%

-        group_by(.data$interval, .drop = TRUE) %>%

-        summarise(methy_prob = mean(.data$methy_prob)) %>%

-        ungroup() %>%

-        mutate(rel_pos = .data$binned_pos)

     if (!is.null(groups)) {

-        p <- p +

-            .agg_geom_smooth(methy_data, span = span, group = TRUE) +

-            ggplot2::scale_colour_brewer(palette = "Set1") +

-            ggplot2::coord_cartesian(clip = "off")

-    } else {

-        p <- p +

-            .agg_geom_smooth(methy_data, span = span, group = FALSE) +

-            ggplot2::coord_cartesian(clip = "off")

+        p <- p + ggplot2::scale_colour_brewer(palette = "Set1")

     }

-    kb_marker <- round(flank / 1000, 1)

-

-    labels <- c(glue::glue("-{kb_marker}kb"), "start", "end", glue::glue("+{kb_marker}kb"))

-

-    p +

-        geom_vline(xintercept = 0, linetype = "dashed", color = "grey80") +

-        geom_vline(xintercept = 1, linetype = "dashed", color = "grey80") +

+    p + ggplot2::coord_cartesian(clip = "off") +

+        ggplot2::geom_vline(xintercept = 0, linetype = "dashed", color = "grey80") +

+        ggplot2::geom_vline(xintercept = 1, linetype = "dashed", color = "grey80") +

         ggplot2::scale_x_continuous(

             name = "Relative Position",

             breaks = c(-.33, 0, 1, 1.33),

@@ -117,6 +118,11 @@ plot_agg_regions <- function(

 #'

 #' @return a ggplot object containing the aggregate methylation trend.

 #'

+#' @examples

+#' nmr <- load_example_nanomethresult()

+#'

+#' plot_agg_regions_sample_grouped(nmr, NanoMethViz::exons(nmr))

+#'

 #' @export

 plot_agg_regions_sample_grouped <- function(

     x,

@@ -157,24 +163,14 @@ plot_agg_regions_sample_grouped <- function(

     )

     methy_data <- methy_data %>%

-        dplyr::bind_rows()

-

-    methy_data <- methy_data %>%

+        dplyr::bind_rows() %>%

         dplyr::group_by(sample, .data$rel_pos) %>%

         dplyr::summarise(methy_prob = mean(.data$methy_prob)) %>%

         dplyr::ungroup()

-    # set up plot

-    p <- ggplot2::ggplot() +

-        ggplot2::ylim(c(0, 1)) +

-        ggplot2::theme_minimal()

-

     # take binned means

     grid_size <- 2^12

-    binned_pos_df <- tibble::tibble(

-        interval = levels(cut(methy_data$rel_pos, breaks = grid_size)),

-        binned_pos = seq(-1.1/3, 1 + 1.1/3, length.out = grid_size+ 2)[2:(1 + grid_size)]

-    )

+    binned_pos_df <- .get_grid(grid_size = grid_size, rel_pos = methy_data$rel_pos)

     methy_data <- methy_data %>%

         dplyr::mutate(interval = cut(.data$rel_pos, breaks = grid_size)) %>%

@@ -185,24 +181,30 @@ plot_agg_regions_sample_grouped <- function(

         dplyr::mutate(rel_pos = .data$binned_pos) %>%

         dplyr::left_join(samples(x), by = "sample")

-    p <- p +

+    methy_data <- dplyr::left_join(methy_data, samples(x), by = c("sample", "group"))

+

+    kb_marker <- round(flank / 1000, 1)

+

+    labels <- c(glue::glue("-{kb_marker}kb"), "start", "end", glue::glue("+{kb_marker}kb"))

+

+    # set up plot

+    ggplot2::ggplot() +

+        ggplot2::ylim(c(0, 1)) +

+        ggplot2::theme_minimal() +

+        # smoothed line

         ggplot2::stat_smooth(

-            aes(x = .data$rel_pos, y = .data$methy_prob, group = .data$haplotype, col = .data$haplotype),

+            aes(x = .data$rel_pos, y = .data$methy_prob, group = .data$group, col = .data$group),

             method = "loess",

+            formula = "y ~ x",

             span = span,

             na.rm = TRUE,

             se = FALSE,

             data = methy_data) +

         ggplot2::scale_colour_brewer(palette = "Set1") +

-        ggplot2::coord_cartesian(clip = "off")

-

-    kb_marker <- round(flank / 1000, 1)

-

-    labels <- c(glue::glue("-{kb_marker}kb"), "start", "end", glue::glue("+{kb_marker}kb"))

-

-    p +

-        geom_vline(xintercept = 0, linetype = "dashed", color = "grey80") +

-        geom_vline(xintercept = 1, linetype = "dashed", color = "grey80") +

+        ggplot2::coord_cartesian(clip = "off") +

+        # start and end vertical dashes

+        ggplot2::geom_vline(xintercept = 0, linetype = "dashed", color = "grey80") +

+        ggplot2::geom_vline(xintercept = 1, linetype = "dashed", color = "grey80") +

         ggplot2::scale_x_continuous(

             name = "Relative Position",

             breaks = c(-.33, 0, 1, 1.33),

@@ -273,22 +275,35 @@ plot_agg_regions_sample_grouped <- function(

 .agg_geom_smooth <- function(data, span, group) {

     if (group) {

         ggplot2::stat_smooth(

-            aes(x = .data$rel_pos,

+            aes(x = .data$binned_pos,

                 y = .data$methy_prob,

                 group = .data$group,

                 col = .data$group),

             method = "loess",

+            formula = "y ~ x",

             span = span,

             na.rm = TRUE,

             se = FALSE,

             data = data)

     } else {

         ggplot2::stat_smooth(

-            aes(x = .data$rel_pos, y = .data$methy_prob),

+            aes(x = .data$binned_pos, y = .data$methy_prob),

             method = "loess",

+            formula = "y ~ x",

             span = span,

             na.rm = TRUE,

             se = FALSE,

             data = data)

     }

 }

+

+.get_grid <- function(grid_size, rel_pos) {

+    grid_size <- 2^12

+    binned_pos <- seq(-1.1/3, 1 + 1.1/3, length.out = grid_size + 2)[-c(1, grid_size + 2)]

+    binned_intervals <- levels(cut(rel_pos, breaks = grid_size))

+

+    tibble::tibble(

+        interval = levels(cut(rel_pos, breaks = grid_size)),

+        binned_pos = seq(-1.1/3, 1 + 1.1/3, length.out = grid_size+ 2)[2:(1 + grid_size)]

+    )

+}