4_circular_read_plot.Rmd

---
title: "whole genome read visualization"
author: "Jörg Wennmann"
date: "2024-11-09"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Required Libraries

```{r include=FALSE}
library(circlize)
library(Biostrings)
library(stringr)
library(reshape2)
library(ggplot2)
```

#-------------

## Set color for circular plot

```{r}
my_col_fun <- colorRamp2(c(75, 90, 100), c("red", "yellow", "blue"))
```

#-------------

## BmNPV-Th2 Circular Plot

### Read and filter data

```{r}
wide_format <- read.csv(file = "output/CDS_per_read/cds_per_read_Th2.csv")

non_na_counts <- sapply(wide_format, function(col) sum(!is.na(col)))
print(table(non_na_counts))

#take only the reads with the most CDS
wide_format <- wide_format[, order(non_na_counts, decreasing = TRUE)]
wide_format <- wide_format[,1:5]
```

### Create Circos Plot

```{r include=FALSE}
f <- 2
h <- 7.5
w <- h

options(bitmapType='cairo')
png(filename = "output/circular_CDS_plot/circlize_plot_Th2.png", width = w*f, height = h*f, units = "cm", res = 300)
  

gene_annotation <-   c("polh", "orf1629", "pk-1", "", "", "lef-1", "egt", "odv-e26", "", "", "", "arif-1", "pif-2", "f-protein", "pkip", "dbp", "", "iap-1", "lef-6", "", "", "bro-a", "sod", "fgf", "", "ubiquitin", "pp31", "lef-11", "bv-e31", "p43", "p47", "lef-12", "gta", "", "", "", "odv-e66", "ets", "lef-8", "", "", "ac53", "lef-10", "vp1054", "", "", "", "", "", "fp25", "lef-9", "", "gp37", "dnapol", "desmo", "lef-3", "pif-6", "", "iap-2", "", "", "", "ac75", "", "vlf-1", "ac78", "", "gp41", "ac81", "tlp", "vp91", "vp15", "cg30", "vp39", "lef-4", "", "p33", "p18", "odv-e25", "helicase", "pif-4", "bro-c", "38k", "lef-5", "p6.9", "p40", "p12", "p48", "vp80", "he65", "ac106/107", "ac108", "odv-ec43", "ac110", "", "", "pif-3", "", "", "pif-1", "", "", "", "gcn2", "", "lef-7", "chitinase", "v-cath", "gp64", "p24", "gp16", "pp34", "", "alkexo", "p35", "p26", "p10", "p74", "me53", "", "ie-0", "p49", "odv-e18", "odv-ec27", "ac145", "ac146", "ie-1", "pif-5", "", "", "ie-2", "pe38", "", "ptp", "bro-d", "", "", "lef-2")

result_df <- wide_format

result_df <- result_df[, !names(result_df) %in% "extracted_numbers"]

mat <- as.matrix(result_df)

sequence_names <- colnames(mat)
sequence_names <- substr(sequence_names, 1, 9)

### TH2   = 126,575
### 01729 = 125,647
### 3dcd8 = 125,536
### ac848 = 125,594
### c8130 = 125,346
genome_length <- c("126,575", "125,647", "125,346", "125,594", "125,536")
names(genome_length) <- sequence_names

# Vektor zur Bestimmung der hervorgehobenen Zellen (Lokalisation des Adapters)
### TH2   = 0
### 01729 = 84
### ac848 = 3
### 3dcd8 = 119
### c8130 = 4
highlight_cells <- c(0, 84, 4, 3, 119)
names(highlight_cells) <- colnames(mat)

# Rownames von gene_annotation$annotation zuweisen
rownames(mat) = gene_annotation

# Farbfunktion für den Gradienten von Rot über Orange zu Gelb
#my_col_fun <- colorRamp2(c(50, 90, 100), c("blue", "red", "yellow"))
#my_col_fun <- colorRamp2(c(50, 90, 100), c("red", "yellow", "blue"))
my_col_fun <- my_col_fun

# Ein einziger Sektor
sectors = "a"

# Erstellung der fünf einzelnen Matrizen
mat_list <- lapply(1:5, function(i) mat[, i, drop = FALSE])

# Circopar Einstellungen und Initialisierung
circos.par(cell.padding = c(0.01, 0.01, 0.01, 0.01), gap.degree = 60, start.degree = 89,
            canvas.xlim = c(-1.07, 1.07), canvas.ylim = c(-1.07, 1.07))
circos.initialize(sectors, xlim = c(0, 138))

track_order <- c(1, 2, 4, 5, 3)

# Zeichnen der circos tracks
for (j in track_order) {
  circos.track(ylim = c(0, 1), bg.border = NA, track.height = 0.1, panel.fun = function(x, y) {
    col_mat = my_col_fun(mat_list[[j]])
    nr = nrow(mat_list[[j]])
    for (i in 1:nr) {
      circos.rect(i - 1, 0, i, 1, border = "black", col = col_mat[i, 1], lwd = 0.2)
      
      # Hervorheben der Zellen durch ein Kreuz
      if (i == highlight_cells[j]) {
        circos.lines(c(i - 1, i), c(0, 1), col = "black", lwd = 1)
        circos.lines(c(i - 1, i), c(1, 0), col = "black", lwd = 1)
      }
    }

    text_size <- 0.5
    font_style = 1

    # Hinzufügen der Beschriftungen nur im ersten Track
    if (j == 1) {
      gene_names = rownames(mat_list[[j]])
      half = floor(nr / 1.7)
      for (i in seq_len(nr)) {
        if (i <= half) {
          circos.text(i - 0.5, 1.2, gene_names[i], cex = text_size, adj = c(0, 0.5),
                      facing = "clockwise", font = font_style)
        } else {
          circos.text(i - 0.5, 1.2, gene_names[i], cex = text_size, adj = c(1, 0.5),
                      facing = "reverse.clockwise", font = font_style)
        }
      }
    }

    # Anfang: Zeilenbeschriftungen hinzufügen
    row_labels <- names(genome_length)
    row_labels <- substr(colnames(mat), 1, 9)
    circos.text(-1, 0.5, row_labels[j], cex = text_size, adj = c(1, 0.5), facing = "inside", niceFacing = TRUE)

    # Ende: Zeilenbeschriftungen hinzufügen
    circos.text(nr + 1, 0.5, genome_length[j], cex = text_size, adj = c(0, 0.5), facing = "inside", niceFacing = TRUE)
  })
}

# Beenden der circlize Zeichnung
circos.clear()

text(0, 0, paste("BmNPV-Th2\nmean nt identity = ", round(mean(mat[, 2:5], na.rm = TRUE), digits = 1), "%", sep = ""), cex = 0.7, adj = c(0.5, 0.5), font = 1)

dev.off()
```

#-------------

## OrNV Circular Plot

### Read and filter data

```{r}
wide_format <- read.csv(file = "output/CDS_per_read/cds_per_read_DUG42.csv")

non_na_counts <- sapply(wide_format, function(col) sum(!is.na(col)))
print(table(non_na_counts))

#take only the reads with the most CDS
wide_format <- wide_format[, order(non_na_counts, decreasing = TRUE)]
```

### Create Circos Plot

```{r include=FALSE}
f <- 2
h <- 7.5
w <- h

options(bitmapType='cairo')
png(filename = "output/circular_CDS_plot/circlize_plot_DUG42.png", width = w*f, height = h*f, units = "cm", res = 300)

dug42_annotation <- c(
  "dnapol",
  "tlsp",
  "gp13",
  "ac81",
  "",
  "ts",
  #"thymidylate synthase",
  "CaN",
  #"calcineurin phosphoesterase",
  "",
  "nese52",
  "",
  "mc",
  #"mitochondrial carrier",
  "odv-e66",
  "",
  "",
  "vp39",
  "polh/gran",
  "pif-2",
  "gp67",
  "ac146",
  "p47",
  "",
  "gp72",
  "guanylate kinase",
  "gp75",
  "gp76",
  "",
  "gp78",
  "",
  "gp81",
  "vlf-1",
  "",
  "",
  "19kda",
  "dnahel 1",
  "",
  "",
  "61k/ac9",
  "",
  "gp93",
  "gp94",
  "gp95",
  "lef-4",
  "",
  "gp97",
  "gp23",
  "gp22",
  "gp19",
  "",
  "",
  "",
  "rr1",
  "lef-5",
  "gp84",
  "gp83",
  "",
  "",
  "pnpla",
  #"patatin phospholipase",
  "hzv115",
  "lef-3",
  "pif-1",
  "gp51",
  "",
  "",
  "lef-8",
  "",
  "",
  "",
  "",
  "fic",
  "",
  "",
  "ac68",
  "",
  "",
  "DNAint/rec",
  "gp58",
  "semaphorin 1",
  "semaphorin 2",
  "gp59",
  "gp60",
  "",
  "",
  "",
  "",
  "",
  "gp61",
  "38k",
  "",
  "",
  "gp28",
  "",
  "",
  "",
  "",
  "gp09",
  "lef-9",
  "mRNA decap 2",
  "",
  "ac120",
  "",
  "",
  "rr2",
  "",
  "gp62",
  "gp43",
  "vp91",
  "pif-3",
  "dnahel 2",
  "",
  "",
  "",
  "",
  "ac92",
  "gp06",
  "odv-e56",
  "gp33",
  "gp34",
  "gp35",
  "gp36",
  "gp37",
  "dnalig",
  "gp39",
  "gp41",
  "",
  "gp44",
  "p74",
  "",
  "",
  "",
  "",
  "",
  "gp48",
  "",
  "iap-3",
  "",
  "",
  "gp17",
  "",
  ""
)

result_df <- wide_format

result_df <- result_df[, !names(result_df) %in% "extracted_numbers"]
#sel <- c("32540/1", "32538/1", "29069/1", "30212/1")
result_df <- result_df[, c(1, 4, 5, 3, 6)]

no_acmnpv <- 5
#mat <- as.matrix(result_df[,1:no_acmnpv])

mat <- as.matrix(result_df)
sequence_names <- colnames(mat)
sequence_names <- substr(sequence_names, 1, 11)

#  read_name read_length no_cds   ISO    top
#1   29069/1      126830    139 DUG42    Top
#2   30212/1      125949    139 DUG42    Top
#3   32538/1      127638    139 DUG42    Top
#4   32540/1      128307    139 DUG42    Top
#5   34828/1      149290    139 DUG42 Others
#7    4536/1      124841    138 DUG42 Others

genome_length <- c("127,615", "128,307", "127,638", "126,830", "125,949")
names(genome_length) <- sequence_names
genome_length <- genome_length[1:no_acmnpv]


# Rownames von gene_annotation$annotation zuweisen
rownames(mat)  <-  dug42_annotation

# Farbfunktion für den Gradienten von Rot über Orange zu Gelb
#my_col_fun <- colorRamp2(c(50, 90, 100), c("blue", "red", "yellow"))
#my_col_fun <- colorRamp2(c(50, 90, 100), c("red", "yellow", "blue"))
my_col_fun <- my_col_fun

# Ein einziger Sektor
sectors = "a"

# Erstellung der fünf einzelnen Matrizen
mat_list <- lapply(1:no_acmnpv, function(i) mat[, i, drop = FALSE])

# Vektor zur Bestimmung der hervorgehobenen Zellen
highlight_cells <- c(0, 0, 0, 0, 0, 0, 0, 0, 0)
names(highlight_cells) <- colnames(mat)

# Circopar Einstellungen und Initialisierung
circos.par(cell.padding = c(0.001, 0.001, 0.001, 0.001), gap.degree = 67, start.degree = 89,
           canvas.xlim = c(-1.07, 1.07), canvas.ylim = c(-1.07, 1.07))
circos.initialize(sectors, xlim = c(0, 139))

# Zeichnen der circos tracks
for (j in 1:no_acmnpv) {
  circos.track(ylim = c(0, 1), bg.border = NA, track.height = 0.08, panel.fun = function(x, y) {
    nr <- nrow(mat_list[[j]])
    for (i in 1:nr) {
      if (is.na(mat_list[[j]][i, 1])) {
        col <- "lightgrey"
      } else {
        col <- my_col_fun(mat_list[[j]][i, 1])
      }
      circos.rect(i - 1, 0, i, 1, border = "black", col = col, lwd = 0.2)
      
      # Hervorheben der Zellen durch ein Kreuz
      if (i == highlight_cells[j]) {
        circos.lines(c(i - 1, i), c(0, 1), col = "black", lwd = 1)
        circos.lines(c(i - 1, i), c(1, 0), col = "black", lwd = 1)
      }
    }

    text_size <- 0.5
    font_style = 1

    # Hinzufügen der Beschriftungen nur im ersten Track
    if (j == 1) {
      gene_names = rownames(mat_list[[j]])
      half = floor(nr / 1.7)
      for (i in seq_len(nr)) {
        if (i <= half) {
          circos.text(i - 0.5, 1.2, gene_names[i], cex = text_size, adj = c(0, 0.5),
                      facing = "clockwise", font = font_style)
        } else {
          circos.text(i - 0.5, 1.2, gene_names[i], cex = text_size, adj = c(1, 0.5),
                      facing = "reverse.clockwise", font = font_style)
        }
      }
    }

    # Anfang: Zeilenbeschriftungen hinzufügen
    row_labels <- names(genome_length)
    #row_labels <- substr(colnames(mat), 1, no_acmnpv)
    circos.text(-1, 0.5, row_labels[j], cex = text_size, adj = c(1, 0.5), facing = "inside", niceFacing = TRUE)

    # Ende: Zeilenbeschriftungen hinzufügen
    circos.text(nr + 1, 0.5, genome_length[j], cex = text_size, adj = c(0, 0.5), facing = "inside", niceFacing = TRUE)
  })
}

# Beenden der circlize Zeichnung
circos.clear()

text(0, 0, paste("OrNV-DUG42\nmean nt identity = ", round(mean(mat[, 2:5], na.rm = TRUE), digits = 1), "%", sep = ""), cex = 0.7, adj = c(0.5, 0.5), font = 1)

dev.off()
```

#-------------

## WSSV Circular Plot

### Read and filter data

```{r}
wide_format <- read.csv(file = "output/CDS_per_read/cds_per_read_JP04.csv")

non_na_counts <- sapply(wide_format, function(col) sum(!is.na(col)))
print(table(non_na_counts))

#take only the reads with the most CDS
wide_format <- wide_format[, order(non_na_counts, decreasing = TRUE)]
```
### Create Circos Plot

```{r include=FALSE}
f <- 2
h <- 7.5
w <- h

options(bitmapType='cairo')
png(filename = "output/circular_CDS_plot/circlize_plot_JP04.png", width = w*f, height = h*f, units = "cm", res = 300)

jp04_annotation <- c(
  "vp180",
  "vp24",
  "",
  "",
  "vp12",
  "env protein",           # Keine Abkürzung vorhanden
  "vp150",
  "dbd",                  # DNA-binding domain
  "gcp",                  # Keine Abkürzung vorhanden
  "",
  "",
  "vp110",
  "vp160b",
  "",
  "ie",                   # Immediate early
  "ie",                   # Immediate early
  "",
  "",
  "TS",                   # thymidylate synthetase
  "ie-1",                 # Immediate early 1
  "",
  "",
  "vp36a",
  "ie",                   # Immediate early
  "ie",                   # Immediate early
  "pk-2",                 # Keine Abkürzung vorhanden
  "",
  "",
  "ie",                   # Immediate early
  "CREB-binding",         # Keine kürzere Abkürzung
  "",
  "",
  "proPO inhibitor",      # prophenoloxidase inhibitor
  "dUTPase",              # dUTP pyrophosphatase
  "vp53b",
  "",
  "",
  "",
  "",
  "",
  "capsid wsv131",        # Keine Abkürzung vorhanden
  "",
  "struc protein",         # structural wsv134
  "struc protein",         # structural wsv136
  "vp337",
  "latency-assoc",        # latency-associated
  "",
  "non-struc",     # non-structural wsv143
  "phosphoest",  # putative phosphoesterase
  "",
  "",
  "latency-rel",          # latency-related
  "",
  "",
  "rr-1",                 # Ribonucleotide reductase 1
  "",
  "ie-2",                 # Immediate early 2
  "",
  "",
  "",
  "rr-2",                 # Ribonucleotide reductase 2
  "endonuclease",         # Keine Abkürzung vorhanden
  "",
  "",
  "vp32",
  "RING",                 # RING finger
  "",
  "",
  "",
  "",
  "vp187",
  "dbd vp15",             # DNA-binding domain vp15
  "vp124",
  "vp76",
  "e3 ligase",            # Keine Abkürzung vorhanden
  "",
  "icp11",                # Keine Abkürzung vorhanden
  "",
  "",
  "vp41a",
  "vp52a",
  "vp41b",
  "",
  "",
  "e3 ligase",            # Keine Abkürzung vorhanden
  "DUF1335",              # Domain of unknown function 1335
  "vp281",
  "vp52b",
  "vp38",
  "",
  "AAP",                  # Apoptosis-associated protein
  "struc vp53c",          # structural vp53c
  "",
  "vp136",
  "",
  "",
  "",
  "struc vp13a",          # structural vp13a
  "",
  "vp190",
  "vp14",
  "",
  "env protein",          # Keine Abkürzung vorhanden
  "",
  "",
  "TBP",                  # TATA-binding protein
  "vp39a",
  "vp51c",
  "",
  "vp26",
  "",
  "vp16",
  "",
  "",
  "vp56",
  "vp90",
  "",
  "vp11",
  "vp39",
  "vp31",
  "",
  "",
  "vp664",
  "vp12b",
  "",
  "AAP-1",                # Apoptosis-associated protein 1
  "tk-tmk",               # Keine Abkürzung vorhanden
  "",
  "",
  "tegument prot",        # tegument protein
  "e3 ligase",            # Keine Abkürzung vorhanden
  "",
  "",
  "",
  "env protein",          # Keine Abkürzung vorhanden
  "vp19",
  "vp60b",
  "struc protein",        # structural protein
  "",
  "vp28",
  "pk-1",                 # Keine Abkürzung vorhanden
  "lat-assoc prot",       # latency-associated protein
  "env protein",          # Keine Abkürzung vorhanden
  "",
  "",
  "vp95",
  "",
  "putative dnahel",      # Keine Abkürzung vorhanden
  "",
  "",
  "",
  "",
  "",
  "",
  "",
  "ie",                   # Immediate early
  "",
  "",
  "GTP",                  # Keine Abkürzung vorhanden
  "",
  "",
  "",
  "",
  "",
  "trx vp362",            # thioredoxin vp362
  "",
  "DNApol",               # DNA polymerase
  "",
  "vp55"
)

result_df <- wide_format

result_df <- result_df[, !names(result_df) %in% "extracted_numbers"]
result_df <- result_df[, c(1:3)]

no_acmnpv <- 3
#mat <- as.matrix(result_df[,1:no_acmnpv])

mat <- as.matrix(result_df)
sequence_names <- c("WSSV-JP04", "56297/1", "149931/1")

genome_length <- c("301,054", "138,184", "132,543")
names(genome_length) <- sequence_names
genome_length <- genome_length[1:no_acmnpv]


# Rownames von gene_annotation$annotation zuweisen
rownames(mat)  <-  jp04_annotation

my_col_fun <- my_col_fun

# Ein einziger Sektor
sectors = "a"

# Erstellung der fünf einzelnen Matrizen
mat_list <- lapply(1:no_acmnpv, function(i) mat[, i, drop = FALSE])

# Vektor zur Bestimmung der hervorgehobenen Zellen
highlight_cells <- c(0, 0, 0, 0, 0, 0, 0, 0, 0)
names(highlight_cells) <- colnames(mat)

# Circopar Einstellungen und Initialisierung
circos.par(cell.padding = c(0.001, 0.001, 0.001, 0.001), gap.degree = 67, start.degree = 89,
           canvas.xlim = c(-1.15, 1.15), canvas.ylim = c(-1.15, 1.15))
circos.initialize(sectors, xlim = c(0, 169))

# Zeichnen der circos tracks
for (j in 1:no_acmnpv) {
  circos.track(ylim = c(0, 1), bg.border = NA, track.height = 0.08, panel.fun = function(x, y) {
    nr <- nrow(mat_list[[j]])
    for (i in 1:nr) {
      if (is.na(mat_list[[j]][i, 1])) {
        col <- "lightgrey"
      } else {
        col <- my_col_fun(mat_list[[j]][i, 1])
      }
      circos.rect(i - 1, 0, i, 1, border = "black", col = col, lwd = 0.2)
      
      # Hervorheben der Zellen durch ein Kreuz
      if (i == highlight_cells[j]) {
        circos.lines(c(i - 1, i), c(0, 1), col = "black", lwd = 1)
        circos.lines(c(i - 1, i), c(1, 0), col = "black", lwd = 1)
      }
    }

    text_size <- 0.5
    font_style = 1

    # Hinzufügen der Beschriftungen nur im ersten Track
    if (j == 1) {
      gene_names = rownames(mat_list[[j]])
      half = floor(nr / 1.7)
      for (i in seq_len(nr)) {
        if (i <= half) {
          circos.text(i - 0.5, 1.2, gene_names[i], cex = text_size, adj = c(0, 0.5),
                      facing = "clockwise", font = font_style)
        } else {
          circos.text(i - 0.5, 1.2, gene_names[i], cex = text_size, adj = c(1, 0.5),
                      facing = "reverse.clockwise", font = font_style)
        }
      }
    }

    # Anfang: Zeilenbeschriftungen hinzufügen
    row_labels <- names(genome_length)
    #row_labels <- substr(colnames(mat), 1, no_acmnpv)
    circos.text(-1, 0.5, row_labels[j], cex = text_size, adj = c(1, 0.5), facing = "inside", niceFacing = TRUE)

    # Ende: Zeilenbeschriftungen hinzufügen
    circos.text(nr + 1, 0.5, genome_length[j], cex = text_size, adj = c(0, 0.5), facing = "inside", niceFacing = TRUE)
  })
}

# Beenden der circlize Zeichnung
circos.clear()

text(0, 0, paste("MAG: WSSV-JP04\nmean nt identity = ", round(mean(mat[, 2:3], na.rm = TRUE), digits = 1), "%", sep = ""), cex = 0.7, adj = c(0.5, 0.5), font = 1)

dev.off()
```


#-------------

# Manual color scale legend

```{r message=FALSE}
library(ggplot2)
library(cowplot)
library(grid)

# Erstellen einer Dummy-Datenstruktur nur für die Farbskala
df <- data.frame(
  x = c(75, 90, 100),
  y = c(1, 1, 1),
  label = c("75", "90", "100")
)

# Erstellen der Legende mit vergrößerter Farbskala, zentriertem Titel und größerem Abstand
p <- ggplot(df, aes(x = x, y = y, fill = x)) +
  geom_tile() +
  scale_fill_gradientn(colors = c("red", "yellow", "blue"), limits = c(75, 100)) +
  theme_void() +  # Entfernt die Achsen und andere unnötige Teile
  guides(fill = guide_colorbar(title = "ORF nt identity (%)", 
                               barwidth = unit(1, "cm"),  # Breiter machen
                               barheight = unit(4, "cm"),  # Höhe anpassen
                               title.position = "top",      # Titel oben platzieren
                               title.hjust = 0.5,           # Titel zentrieren
                               title.theme = element_text(size = 12, 
                                                          margin = margin(b = 10)),  
                               label.theme = element_text(size = 10))) +  # Beschriftungsgröße anpassen
  theme(legend.position = "right",
        legend.title = element_text(hjust = 0.5))  # Zentriert den Titel

# Zeigt nur die Legende an
legend <- cowplot::get_legend(p)

f = 2
# Erstellen einer Grafikdatei und Speichern der Legende als PNG
png("output/circular_CDS_plot/legend.png", width = 300*f, height = 500*f, res = 300, bg = "transparent")  # Passen Sie die Größe nach Bedarf an
grid.newpage()
grid.draw(legend)
dev.off()
```

#-------------

# Combine Plots with Legend

## BmNPV + OrNV

```{r}
library(magick)

# Lese die beiden PNG-Dateien ein
image1 <- image_read("output/circular_CDS_plot/circlize_plot_Th2.png")
image2 <- image_read("output/circular_CDS_plot/circlize_plot_DUG42.png")

# Kombiniere die beiden Bilder nebeneinander
combined_image <- image_append(c(image1, image2), stack = TRUE)

# Speichere das kombinierte Bild als neues PNG
image_write(combined_image, path = "output/circular_CDS_plot/circlize_plot_Th2_OrNV.png")
```


```{r}

# Lese die beiden PNG-Dateien ein
image1 <- image_read("output/circular_CDS_plot/circlize_plot_Th2_OrNV.png")
image2 <- image_read("output/circular_CDS_plot/legend.png")
image2 <- image_scale(image2, "70%")

# Berechne die Position für die zweite Grafik
# Mittig links, etwas versetzt vom Zentrum
offset_x <- 1350
offset_y <- 1370


# Überlagere die zweite Grafik auf der ersten
combined_image <- image_composite(image1, image2, offset = paste0("+", offset_x, "+", offset_y))

# Speichere das kombinierte Bild als neues PNG
image_write(combined_image, path = "output/circular_CDS_plot/circlize_plot_BmNPV_OrNV_legend.png")
```


```{r}
text_size = 75
xoff <- 200
yoff <- 150
xoffloc <- paste("+", xoff, "+", yoff, sep = "")

# Lese die PNG-Datei ein
image <- image_read("output/circular_CDS_plot/circlize_plot_BmNPV_OrNV_legend.png")

# Füge Text A links oben hinzu
image_with_text <- image_annotate(
  image,
  text = "A",
  location = xoffloc, # Positioniere Text A 10 Pixel von oben und 10 Pixel von links
  size = text_size, # Schriftgröße
  color = "black" # Textfarbe
)

# Bestimme die Abmessungen der Grafik
image_info <- image_info(image_with_text)
image_width <- image_info$width
image_height <- image_info$height

# Berechne die Position für Text B (mittig links)
offset_x <- xoff # 10 Pixel vom linken Rand
offset_y <- (image_height / 2) + yoff# Vertikale Mitte

# Füge Text B mittig links hinzu
image_with_text <- image_annotate(
  image_with_text,
  text = "B",
  location = paste0("+", offset_x, "+", offset_y),
  size = text_size, # Schriftgröße
  color = "black" # Textfarbe
)

# Speichere das Bild mit dem Text als neues PNG
image_write(image_with_text, path = "output/circular_CDS_plot/circlize_plot_BmNPV_OrNV_legend_AB.png")
```