Single heatmap on two symetric matrices with different colours and scales R

You can subset the relevant parts of the matrix in the data argument of a layer, and use {ggnewscale} to assign different fill scales to different layers. The trick is to declare a fill scale before adding new_scale_fill(), otherwise the order of operations goes wrong (which usually doesn't matter a lot, but here they do).

You can then tweak every individual scale. In the example below I just tweaked the palettes, but you can also adjust limits, breaks, labels etc.

# Assuming code from question has been executed and we have a 'merged' in memory
library(ggplot2)
library(ggnewscale)

# Wide matrix to long dataframe
# Later, we'll be relying on the notion that the dimnames have been
# converted to factor variables to separate out the upper from the lower
# matrix.
df <- reshape2::melt(merged)


ggplot(df, aes(Var1, Var2)) +
  # The first layer, with its own fill scale
  geom_raster(
    data = ~ subset(.x, as.numeric(Var1) > as.numeric(Var2)),
    aes(fill = value)
  ) +
  scale_fill_distiller(palette = "Blues") +
  # Declare new fill scale for the second layer
  new_scale_fill() +
  geom_raster(
    data = ~ subset(.x, as.numeric(Var1) < as.numeric(Var2)),
    aes(fill = value)
  ) +
  scale_fill_distiller(palette = "Reds") +
  # I'm not sure what to do with the diagonal. Make it grey?
  new_scale_fill() +
  geom_raster(
    data = ~ subset(.x, as.numeric(Var1) == as.numeric(Var2)),
    aes(fill = value)
  ) +
  scale_fill_distiller(palette = "Greys", guide = "none")

In my opinion @teunbrand's answer is what you're looking for, but another potential option is to use the ComplexHeatmap package, e.g. based on one of the examples in the docs:

library(Matrix)
set.seed(123)

s1<-forceSymmetric(matrix(round(rnorm(25),2),5))
colnames(s1)<-LETTERS[1:5]
rownames(s1)<-LETTERS[6:10]
diag(s1)<-1

s2<-forceSymmetric(matrix(round(rbinom(25,25,0.3),2),5))
colnames(s2)<-LETTERS[1:5]
rownames(s2)<-LETTERS[6:10]
diag(s2)<-1

#Get upper diagonal
reverse = s1[,ncol(s1):1]
diag(reverse) = 0
reverse[lower.tri(reverse, diag = FALSE)] <- 0
upper = reverse[,ncol(reverse):1]

# Get lower diagonal
reverse1 = s2[,ncol(s2):1]
diag(reverse1) = 0
reverse1[upper.tri(reverse1, diag = FALSE)] <- 0
upper1 = reverse1[,ncol(reverse1):1]

# Add them together
m = as.matrix(upper+upper1)
m
#>      A    B     C     D E
#> F 1.00 1.72  1.22  1.79 0
#> G 1.72 1.00  0.36  0.00 8
#> H 1.22 0.36  0.00 10.00 9
#> I 1.79 0.00 10.00  1.00 1
#> J 0.00 8.00  9.00  1.00 1

library(ComplexHeatmap)
#> Loading required package: grid
#> ========================================
#> ComplexHeatmap version 2.8.0
#> Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
#> Github page: https://github.com/jokergoo/ComplexHeatmap
#> Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
#> 
#> If you use it in published research, please cite:
#> Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
#>   genomic data. Bioinformatics 2016.
#> 
#> The new InteractiveComplexHeatmap package can directly export static 
#> complex heatmaps into an interactive Shiny app with zero effort. Have a try!
#> 
#> This message can be suppressed by:
#>   suppressPackageStartupMessages(library(ComplexHeatmap))
#> ========================================
library(circlize)
#> ========================================
#> circlize version 0.4.13
#> CRAN page: https://cran.r-project.org/package=circlize
#> Github page: https://github.com/jokergoo/circlize
#> Documentation: https://jokergoo.github.io/circlize_book/book/
#> 
#> If you use it in published research, please cite:
#> Gu, Z. circlize implements and enhances circular visualization
#>   in R. Bioinformatics 2014.
#> 
#> This message can be suppressed by:
#>   suppressPackageStartupMessages(library(circlize))
#> ========================================
col1 = colorRamp2(c(-1, 10), c("white", "red"))
col2 = colorRamp2(c(-1, 10), c("white", "blue3"))

# here reordering the symmetric matrix is necessary
od = hclust(dist(m))$order
m = m[od, od]

ht = Heatmap(m, rect_gp = gpar(type = "none"), show_heatmap_legend = FALSE,
             cluster_rows = FALSE, cluster_columns = FALSE,
             layer_fun = function(j, i, x, y, w, h, fill) {
               l = i > j
               grid.rect(x[l], y[l], w[l], h[l], 
                         gp = gpar(fill = col1(pindex(m, i[l], j[l])), col = NA))
               l = i < j
               grid.rect(x[l], y[l], w[l], h[l], 
                         gp = gpar(fill = col2(pindex(m, i[l], j[l])), col = NA))
             })
draw(ht, heatmap_legend_list = list(
  Legend(title = "Group_A", col_fun = col1),
  Legend(title = "Group_B", col_fun = col2)
))

^{Created on 2022-03-07 by the reprex package (v2.0.1)}