I was trying to make a heatmap and hierarchical clustering with a dendrogram of samples.

I’m trying to follow this particular thread from StackOverflow(Merging multiple hclust objects (or dendrograms))

I have a large dataframe,

head(joined_df_sorted)
# A tibble: 6 × 24
  chrom   start     end EE85756 EE85757 EE85770 EE85775 EE85784 EE87786 EE87787 EE87788 EE87789 EE87790 EE87811 EE87812 EE87813 EE87814 EE87815
  <chr>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 chr1  1000001 2000000       3       4       3       3       3       3       3       3       3       3       3       3       3       3       3
2 chr1  3000001 4000000       3       4       3       3       3       8       3       3       3       3       3       3       3       3       3
3 chr1  4000001 5000000       3       4       3       3       3      13       3       3       8       3       3       3       3       3       6
4 chr1  5000001 6000000       3       4       3       3       3      10       3       3       7       3       3       3       3       3       3
5 chr1  6000001 7000000       3       4       3       3       3       3       3       3       3       3       3       3       3       3       3
6 chr1  7000001 8000000       3       4       3       3       3       7       3       3       3       3       3       3       3       3       3 

What I have done till now is,

    #Joining first 2 columns for making unique row name
    joined_df_sorted$chromid <- paste0(joined_df_sorted$chrom, "_", joined_df_sorted$start)

#Keeping only required columns
joined_df_sorted2 <- as.data.frame(joined_df_sorted[,c(24,4:23)])


#making first column as row name
joined_df_sorted3<-joined_df_sorted2
joined_df_sorted3X <- joined_df_sorted3[,-1]
rownames(joined_df_sorted3X) <- joined_df_sorted3[,1]

## transposing the dataframe without changing datatype
install.packages("sjmisc")
library(sjmisc)

joined_df_sorted3X_t<-joined_df_sorted3X %>% 
  rotate_df(cn = FALSE)

CanCohortDat<-joined_df_sorted3X_t

BileDuct  <- c("EE87786",   "EE87787",  "EE87788",  "EE87789",  "EE87790")
Breast <-  c("EE87811", "EE87812",  "EE87813",  "EE87814",  "EE87815")
Gastric <- c("EE87893", "EE87894",  "EE87895",  "EE87896",  "EE87897")
Healthy <- c("EE85756", "EE85757",  "EE85770","EE85775",    "EE85784")

#Separate clustering of 4 distinct datasource
h1 <- hclust(dist(CanCohortDat[BileDuct,]))
h2 <- hclust(dist(CanCohortDat[Breast,]))
h3 <- hclust(dist(CanCohortDat[Gastric,]))
h4 <- hclust(dist(CanCohortDat[Healthy,]))

#merge 4 clusters
hc <- as.hclust(merge(merge(merge(
  as.dendrogram(h1), as.dendrogram(h2)), as.dendrogram(h3)), 
  as.dendrogram(h4)))

CanCoh <-CanCohortDat[c(BileDuct, Breast, Gastric, Healthy),]


cohort_annotation <- data.frame(Region = c(rep("BileDuct", length(BileDuct)), 
                                           rep("Breast", length(Breast)),
                                           rep("Gastric", length(Gastric)),
                                           rep("Healthy", length(Healthy))),
                                row.names = c(BileDuct, Breast, Gastric, Healthy))


#Heatmap
pheatmap(CanCoh , cluster_rows = hc, 
         annotation_row = cohort_annotation)


#############ERROR##############
`use_raster` is automatically set to TRUE for a matrix with more than 2000
columns You can control `use_raster` argument by explicitly setting TRUE/FALSE
to it.

Set `ht_opt$message = FALSE` to turn off this message.
'magick' package is suggested to install to give better rasterization.

Set `ht_opt$message = FALSE` to turn off this message.
Error in hclust(get_dist(t(submat), distance), method = method) : 
  NA/NaN/Inf in foreign function call (arg 10)
In addition: Warning message:
The input is a data frame, convert it to the matrix. 

My Output should look like this: EE* are the different samples that should be at y axis and grouped by the merged dendrogram and x-axis should display the chromosome location.