Add relative distances in plots

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: EasyMareyMap
 Type: Package
 Title: Estimate local recombination rates using the Marey map method
-Version: 0.2.0
+Version: 0.3.0
 Author: Thomas Brazier
 Maintainer: Thomas Brazier <[email protected]>
 Description: Estimate local recombination rates using the Marey map method. Recombination rates are estimated by interpolating a mathematical function over the Marey map (genetic distances in cM expressed as a function of the genomic position in Mb).

R/broken_stick.R

@@ -57,7 +57,7 @@ brokenstick = function(x, k = 10, method = "strict", plot = TRUE) {
   # Tidy data frame
   # brokenstick = brokenstick[!(is.na(df$p1) | is.na(df$p2) | is.na(df$p3) | is.na(df$p4) | is.na(df$p5) | df(brokenstick$p6) | is.na(df$p7) | is.na(df$p8) | is.na(df$p9) | is.na(df$p10)),]
   brokenstick = melt(df)
-  brokenstick$sample = paste(brokenstick$set, brokenstick$chromosome, sep = "_")
+  brokenstick$sample = paste(brokenstick$set, brokenstick$name, sep = "_")
   colnames(brokenstick)=c("set","chromosome","segment","proportion.length","sample")
 
   # Set a vector of gradient color

R/comparative_marey_map.R

@@ -155,6 +155,8 @@ compute_stats_marey = function(x,
 #'
 #' @param x a `comparative_marey_map` object
 #' @param group the grouping factor in `ggplot`, either `set`, `map` or `set + map`
+#' @param relative_distance_x logical, whether to plot the X axis as relative distances (0,1) to compare different genomes
+#' @param relative_distance_y logical, whether to plot the Y axis as relative distances (0,1) to compare different genetic maps
 #' @param ... arguments passed to the generic summary function.
 #'
 #' @import ggplot2
@@ -164,49 +166,84 @@ compute_stats_marey = function(x,
 #' @return a `ggplot2` object of Marey maps
 #' @export
 #'
-plot_comparative_marey = function(x, group = 'set + map', ...) {
+plot_comparative_marey = function(x,
+                                  group = 'set + map',
+                                  relative_distance_x = FALSE,
+                                  relative_distance_y = FALSE, ...) {
 
   df = comparative_marey_to_dataframe(x)
+
+  scaling = 10^6 # Convert bp to Mb
+
+  # Relative distances
+  if (relative_distance_x) {
+    df$max_phys = unlist(lapply(1:nrow(df), function(x) {max(df$phys[which(df$set == df$set[x] & df$map == df$map[x])], na.rm = T)}))
+    df$phys = df$phys / df$max_phys
+    scaling = 1
+    lab = labs(x = "Relative genomic position", y = "Genetic distance (cM)")
+  }
+  if (relative_distance_y) {
+    df$max_gen = unlist(lapply(1:nrow(df), function(x) {max(df$gen[which(df$set == df$set[x] & df$map == df$map[x])], na.rm = T)}))
+    df$gen = df$gen / df$max_gen
+    lab = labs(x = "Genomic position (Mb)", y = "Relative genetic distance")
+  }
+  if (relative_distance_x & relative_distance_y){
+    lab = labs(x = "Relative genomic position", y = "Relative genetic distance")
+  }
+  if (!relative_distance_x & !relative_distance_y){
+    lab = labs(x = "Genomic position (Mb)", y = "Genetic distance (cM)")
+  }
 
   # Add the Marey interpolated function
   marey = comparative_interpolation_to_dataframe(x)
+  if (relative_distance_x) {
+    marey$max_phys = unlist(lapply(1:nrow(marey), function(x) {max(marey$physicalPosition[which(marey$set == marey$set[x] & marey$map == marey$map[x])], na.rm = T)}))
+    marey$physicalPosition = marey$physicalPosition / marey$max_phys
+  }
+  if (relative_distance_y) {
+    marey$max_gen = unlist(lapply(1:nrow(marey), function(x) {max(marey$upperGeneticPositioncM[which(marey$set == marey$set[x] & marey$map == marey$map[x])], na.rm = T)}))
+    marey$geneticPositioncM = marey$geneticPositioncM / marey$max_gen
+    marey$upperGeneticPositioncM = marey$upperGeneticPositioncM / marey$max_gen
+    marey$lowerGeneticPositioncM = marey$lowerGeneticPositioncM / marey$max_gen 
+  }
+
 
   if (group == 'set') {
     grouping = as.formula(~as.factor(.data$set))
     facet = facet_wrap(grouping, scales = "free")
     point_rec = geom_point(aes(colour = as.factor(.data$map), fill = as.factor(.data$map)), alpha = 0.2)
-    line_rec = geom_line(data = marey, aes(x = .data$physicalPosition/10^6, y = .data$geneticPositioncM, group = as.factor(.data$map)), fill = "black")
-    ribbon_rec = geom_ribbon(data = marey, aes(x = .data$physicalPosition/10^6, y = .data$geneticPositioncM, ymin = .data$lowerGeneticPositioncM, ymax = .data$upperGeneticPositioncM, group = as.factor(.data$map)),
+    line_rec = geom_line(data = marey, aes(x = .data$physicalPosition/scaling, y = .data$geneticPositioncM, group = as.factor(.data$map)), fill = "black")
+    ribbon_rec = geom_ribbon(data = marey, aes(x = .data$physicalPosition/scaling, y = .data$geneticPositioncM, ymin = .data$lowerGeneticPositioncM, ymax = .data$upperGeneticPositioncM, group = as.factor(.data$map)),
                              alpha = 0.4)
 
   }
   if (group == 'map') {
     grouping = as.formula(~ as.factor(.data$map))
     facet = facet_wrap(grouping, scales = "free")
     point_rec = geom_point(aes(colour = as.factor(.data$set), fill = as.factor(.data$set)), alpha = 0.2)
-    line_rec = geom_line(data = marey, aes(x = .data$physicalPosition/10^6, y = .data$geneticPositioncM, group = as.factor(.data$set)), fill = "black")
-    ribbon_rec = geom_ribbon(data = marey, aes(x = .data$physicalPosition/10^6, y = .data$geneticPositioncM, ymin = .data$lowerGeneticPositioncM, ymax = .data$upperGeneticPositioncM, group = as.factor(.data$set)),
+    line_rec = geom_line(data = marey, aes(x = .data$physicalPosition/scaling, y = .data$geneticPositioncM, group = as.factor(.data$set)), fill = "black")
+    ribbon_rec = geom_ribbon(data = marey, aes(x = .data$physicalPosition/scaling, y = .data$geneticPositioncM, ymin = .data$lowerGeneticPositioncM, ymax = .data$upperGeneticPositioncM, group = as.factor(.data$set)),
                              alpha = 0.4)
   }
   if (group == 'set + map') {
     grouping = as.formula(~as.factor(.data$map) + as.factor(.data$set))
     facet = facet_grid(grouping, scales = "free")
     point_rec = geom_point(alpha = 0.2)
-    line_rec = geom_line(data = marey, aes(x = .data$physicalPosition/10^6, y = .data$geneticPositioncM), colour = "black")
-    ribbon_rec = geom_ribbon(data = marey, aes(x = .data$physicalPosition/10^6, y = .data$geneticPositioncM, ymin = .data$lowerGeneticPositioncM, ymax = .data$upperGeneticPositioncM),
+    line_rec = geom_line(data = marey, aes(x = .data$physicalPosition/scaling, y = .data$geneticPositioncM), colour = "black")
+    ribbon_rec = geom_ribbon(data = marey, aes(x = .data$physicalPosition/scaling, y = .data$geneticPositioncM, ymin = .data$lowerGeneticPositioncM, ymax = .data$upperGeneticPositioncM),
                                fill = "darkorange", colour = "darkorange", alpha = 0.3)
   }
 
 
   if (nrow(marey) > 0) {
     marey$vld = TRUE
 
-    p = ggplot2::ggplot(data = df, aes(x = .data$phys/10^6, y = .data$gen)) +
+    p = ggplot2::ggplot(data = df, aes(x = .data$phys/scaling, y = .data$gen)) +
       point_rec +
       line_rec +
       ribbon_rec +
       facet +
-      labs(x = "Genomic position (Mb)", y = "Genetic distance (cM)") +
+      lab +
       theme(axis.line = element_line(),
             panel.grid.major = element_blank(),
             panel.grid.minor = element_blank(),
@@ -219,7 +256,7 @@ plot_comparative_marey = function(x, group = 'set + map', ...) {
             axis.text=element_text(colour="black"),
             legend.position = "right")
   } else {
-    p = ggplot2::ggplot(data = df, aes(x = .data$phys/10^6, y = .data$gen)) +
+    p = ggplot2::ggplot(data = df, aes(x = .data$phys/scaling, y = .data$gen)) +
       point_rec +
       facet +
       labs(x = "Genomic position (Mb)", y = "Genetic distance (cM)", colour = "dataset") +
@@ -245,13 +282,18 @@ plot_comparative_marey = function(x, group = 'set + map', ...) {
 #'
 #' @param x a `comparative_marey_map` object
 #' @param group the grouping factor in `ggplot`, either `set`, `map` or `set + map`
+#' @param relative_distance_x logical, whether to plot the X axis as relative distances (0,1) to compare different genomes
+#' @param relative_distance_y logical, whether to plot the Y axis as relative distances (0,1) to compare maps with different average recombination rates
 #' 
 #' @return a `ggplot2` object of Marey maps
 #' @export
 #' 
 #' @import ggplot2
 #'
-plot_comparative_recmap = function(x, group = 'set + map') {
+plot_comparative_recmap = function(x,
+                                   group = 'set + map',
+                                   relative_distance_x = FALSE,
+                                   relative_distance_y = FALSE) {
 
   x = comparative_recmap_to_dataframe(x)
 
@@ -261,6 +303,26 @@ plot_comparative_recmap = function(x, group = 'set + map') {
   x$upperRecRate = x$upperRecRate * 10^6
   x$lowerRecRate = x$lowerRecRate * 10^6
 
+  # Relative distances
+  if (relative_distance_x) {
+    x$max_phys = unlist(lapply(1:nrow(x), function(y) {max(x$end[which(x$set == x$set[y] & x$map == x$map[y])], na.rm = T)}))
+    x$point = x$point / x$max_phys
+    lab = labs(x = "Relative genomic position", y = "Recombination rate (cM/Mb)")
+  }
+  if (relative_distance_y) {
+    x$max_rec = unlist(lapply(1:nrow(x), function(y) {max(x$upperRecRate[which(x$set == x$set[y] & x$map == x$map[y])], na.rm = T)}))
+    x$recRate = x$recRate / x$max_rec
+    x$upperRecRate = x$upperRecRate / x$max_rec
+    x$lowerRecRate = x$lowerRecRate / x$max_rec
+    lab = labs(x = "Genomic position (Mb)", y = "Relative recombination rate")
+  }
+  if (relative_distance_x & relative_distance_y){
+    lab = labs(x = "Relative genomic position", y = "Recombination rate")
+  }
+  if (!relative_distance_x & !relative_distance_y){
+    lab = labs(x = "Genomic position (Mb)", y = "Recombination rate (cM/Mb)")
+  }
+
   if (group == 'set') {
     grouping = as.formula(~as.factor(.data$set))
     facet = facet_wrap(grouping, scales = "free")
@@ -286,7 +348,7 @@ plot_comparative_recmap = function(x, group = 'set + map') {
     # facet_grid(~as.factor(set)) +
     # facet_wrap(grouping, scales = "free") +
     facet +
-    labs(x = "Genomic position (Mb)", y = "Recombination rate (cM/Mb)") +
+    lab +
     theme(axis.line = element_line(),
           panel.grid.major = element_blank(),
           panel.grid.minor = element_blank(),

vignettes/RecombinationMap.Rmd

@@ -640,52 +640,22 @@ lorenz(compar)
 The Broken stick model, as described in Brazier and Glémin (2022), is also an appropriate representation to compare the distribution of recombination rates along the genome across a large set of Marey maps.
 
 
-```{r brokenStick, message=FALSE, warning=FALSE, echo=TRUE, fig.height = 4, fig.width = 8, fig.align="center", fig.cap = "The broken stick model applied to *Arabidopsis thaliana* and maize."}
+```{r brokenStick, message=FALSE, warning=FALSE, echo=TRUE, fig.height = 6, fig.width = 8, fig.align="center", fig.cap = "The broken stick model applied to *Arabidopsis thaliana* and maize."}
 brokenstick(compar)
 ```
 
 
 Moreover, to compare between species, it can be interesting to use relative genomic positions and relative recombination rates, instead of absolute ones.
 
 
-```{r relativeCompare, message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE, fig.height = 4, fig.width = 8, fig.align="center", fig.cap = "Comparative recombination landscapes between Arabidopsis chromosome 1 and maize chromosome 1. Genomic distances are relative distances scaled by the total chromosome size."}
-relative_arabido = res$recMap
-relative_maize = maize$recMap
-
-relative_arabido$relative_start = relative_arabido$start / res$chromosomeLength
-relative_arabido$relative_end = relative_arabido$end / res$chromosomeLength
-relative_arabido$relative_recRate = relative_arabido$recRate / max(relative_arabido$recRate, na.rm = TRUE)
-relative_arabido$relative_lowerRecRate = relative_arabido$lowerRecRate / max(relative_arabido$lowerRecRate, na.rm = TRUE)
-relative_arabido$relative_upperRecRate = relative_arabido$upperRecRate / max(relative_arabido$upperRecRate, na.rm = TRUE)
-
-
-relative_maize$relative_start = relative_maize$start / maize$chromosomeLength
-relative_maize$relative_end = relative_maize$end / maize$chromosomeLength
-relative_maize$relative_recRate = relative_maize$recRate / max(relative_maize$recRate, na.rm = TRUE)
-relative_maize$relative_lowerRecRate = relative_maize$lowerRecRate / max(relative_maize$lowerRecRate, na.rm = TRUE)
-relative_maize$relative_upperRecRate = relative_maize$upperRecRate / max(relative_maize$upperRecRate, na.rm = TRUE)
-
-
-df = rbind(relative_arabido,
-           relative_maize)
-
-df$point = (df$relative_start + df$relative_end)/2
-
-
-ggplot(data = df, aes(x = point, y = recRate)) +
-    geom_line(aes(group = set, color = set)) +
-    geom_ribbon(aes(x = point, ymin = lowerRecRate, ymax = upperRecRate, fill = set), alpha = 0.2) +
-    labs(x = "Relative genomic position", y = "Recombination rate (cM/Mb)") 
-
-
-ggplot(data = df, aes(x = point, y = relative_recRate)) +
-    geom_line(aes(group = set, color = set)) +
-    geom_ribbon(aes(x = point, ymin = relative_lowerRecRate, ymax = relative_upperRecRate, fill = set), alpha = 0.2) +
-    labs(x = "Relative genomic position", y = "Relative recombination rate") 
+```{r relativeCompareMarey, message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE, fig.height = 4, fig.width = 8, fig.align="center", fig.cap = "Comparative marey maps between *Arabidopsis* and maize. Genomic distances are relative distances scaled by the total chromosome size. Genetic distances are scaled by the max genetic distance."}
+plot_comparative_marey(compar, group = 'set', relative_distance_x = T, relative_distance_y = T)
 ```
 
 
-
+```{r relativeCompareRec, message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE, fig.height = 4, fig.width = 8, fig.align="center", fig.cap = "Comparative recombination landscapes between Arabidopsis chromosome 1 and maize chromosome 1. Genomic distances are relative distances scaled by the total chromosome size. Recombination rates are scaled by the max recombination rate."}
+plot_comparative_recmap(compar, group = 'set', relative_distance_x = T, relative_distance_y = T)
+```