@@ -14,15 +14,18 @@ plot_aggregate_regions <- function(x, regions, groups = NULL, flank = 2000, stra

     assert_that(is_df_or_granges(regions) || is.list(regions))

+    # convert GRanges to tibble

     if (is(regions, "GRanges")) {

         regions <- tibble::as_tibble(regions) %>%

             dplyr::rename(chr = "seqnames")

     }

+    # convert data.frame regions to single element list

     if (!is.null(dim(regions))) {

         regions <- list(regions)

     }

+    # query methylation data

     methy_data <- purrr::map(

         regions,

         function(features) {

@@ -42,43 +45,43 @@ plot_aggregate_regions <- function(x, regions, groups = NULL, flank = 2000, stra

     methy_data <- methy_data %>%

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

-    methy_data <- methy_data %>%

+    if (!is.null(groups)) {

+        methy_data <- methy_data %>%

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

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

-        dplyr::mutate(methy_prob = e1071::sigmoid(statistic)) %>%

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

         dplyr::ungroup()

-

-    methy_data_before <- filter(methy_data, rel_pos <= 0)

-    if (nrow(methy_data_before) > 10000) {

-        methy_data_before <- dplyr::sample_n(methy_data_before, 10000)

-    }

-

-    methy_data_after <- filter(methy_data, rel_pos >= 1)

-    if (nrow(methy_data_after) > 10000) {

-        methy_data_after <- dplyr::sample_n(methy_data_after, 10000)

-    }

-

-    methy_data <- filter(methy_data, between(rel_pos, 0, 1))

-    if (nrow(methy_data) > 10000) {

-        methy_data <- dplyr::sample_n(methy_data, 10000)

+    } else {

+        methy_data <- methy_data %>%

+            dplyr::group_by(.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 <- 2^10

+    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(rel_pos, breaks = grid_size)) %>%

+        group_by(interval, .drop = TRUE) %>%

+        summarise(methy_prob = mean(methy_prob)) %>%

+        ungroup() %>%

+        mutate(rel_pos = binned_pos)

+

+    span <- 0.10

+

     if (!is.null(groups)) {

         p <- p +

-            .geom_smooth(methy_data, span = 0.3, group = TRUE) +

-            .geom_smooth(methy_data_before, span = 0.35, group = TRUE) +

-            .geom_smooth(methy_data_after, span = 0.35, group = TRUE) +

+            .geom_smooth(methy_data, sspan = span, group = TRUE) +

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

             ggplot2::coord_cartesian(clip = "off")

     } else {

         p <- p +

-            .geom_smooth(methy_data, span = 0.3, group = FALSE) +

-            .geom_smooth(methy_data_before, span = 0.35, group = FALSE) +

-            .geom_smooth(methy_data_after, span = 0.35, group = FALSE) +

+            .geom_smooth(methy_data, span = span, group = FALSE) +

             ggplot2::coord_cartesian(clip = "off")

     }

@@ -91,8 +94,8 @@ plot_aggregate_regions <- function(x, regions, groups = NULL, flank = 2000, stra

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

         ggplot2::scale_x_continuous(

             name = "Relative Position",

-            breaks = c(-.25, 0, 1, 1.25),

-            limits = c(-0.25, 1.25),

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

+            limits = c(-0.33, 1.33),

             labels = labels) +

         ggplot2::ylab("Average Methylation Probability")

 }

@@ -110,8 +113,8 @@ plot_aggregate_regions <- function(x, regions, groups = NULL, flank = 2000, stra

         out <- numeric(length(x))

         out[within] <- (x[within] - feature$start) / (feature$end - feature$start)

-        out[upstream] <- (x[upstream] - feature$start) / flank / 4

-        out[downstream] <- 1 + ((x[downstream] - feature$end) / flank / 4)

+        out[upstream] <- (x[upstream] - feature$start) / flank / 3

+        out[downstream] <- 1 + ((x[downstream] - feature$end) / flank / 3)

         if (stranded && length(feature$strand) > 0 && feature$strand == "-") {

             out <- 1 - out

         }

@@ -121,8 +124,8 @@ plot_aggregate_regions <- function(x, regions, groups = NULL, flank = 2000, stra

     out <- query_methy(

         methy,

         features$chr,

-        features$start - flank * 1.1,

-        features$end + flank * 1.1,

+        features$start - flank * 1.10,

+        features$end + flank * 1.10,

         simplify = FALSE)

     out <- purrr::map2(out, .split_rows(features), function(x, y) {

@@ -133,8 +136,8 @@ plot_aggregate_regions <- function(x, regions, groups = NULL, flank = 2000, stra

         x$rel_pos <- .scale_to_feature(x$pos, y, stranded = stranded)

         x %>%

-            group_by(chr, rel_pos) %>%

-            summarise(statistic = mean(statistic)) %>%

+            group_by(rel_pos) %>%

+            summarise(methy_prob = mean(e1071::sigmoid(statistic))) %>%

             ungroup()

     })