Deep Dive Expression: Figure edits

E5-coral
Author

Kathleen Durkin

Published

December 8, 2025

Trying out a circos plot via the circlize R package. Hoping to get a figure something like this:

Final plot stored here

Load libraries

library(dplyr)
Warning: package 'dplyr' was built under R version 4.2.3

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(tidyr)
Warning: package 'tidyr' was built under R version 4.2.3
library(readr)
library(stringr)
library(circlize)
========================================
circlize version 0.4.17
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))
========================================

Load edges files and select only miR-100 targets

Apul_edges <- read.csv("https://github.com/urol-e5/deep-dive-expression/raw/refs/heads/main/D-Apul/output/33-Apul-miRNA-mRNA-lncRNA-network/edges_miRNA_mRNA_lncRNA_network_p0.05_unique.csv", header=TRUE) %>% dplyr::select(-X) %>% filter(source == "apul-mir-100")

Peve_edges <- read.csv("https://github.com/urol-e5/deep-dive-expression/raw/refs/heads/main/E-Peve/output/31-Peve-miRNA-mRNA-lncRNA-network/edges_miRNA_mRNA_lncRNA_network_p0.05_unique.csv", header=TRUE) %>% dplyr::select(-X) %>% filter(source == "peve-mir-100")

Ptuh_edges <- read.csv("https://github.com/urol-e5/deep-dive-expression/raw/refs/heads/main/F-Ptuh/output/31-Ptuh-miRNA-mRNA-lncRNA-network/edges_miRNA_mRNA_lncRNA_network_p0.05_unique.csv", header=TRUE) %>% dplyr::select(-X) %>% filter(source == "ptuh-mir-100")

Read in Hollie’s orthogroup analysis output:

h_ortho <- readr::read_tsv("https://github.com/urol-e5/timeseries_molecular/raw/1b3da407f438e9ecf6fbc43b083b4b94897f2f99/M-multi-species/output/33-orthologs/shared_orthologs_all_species.tsv")
Rows: 52280 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (3): Orthogroup, Species, Proteins

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Add column showing whether a given orthogroup contains proteins from all species or only a subset.

# Standardize species names for simplicity
h_ortho <- h_ortho %>%
  mutate(
    simple_species = case_when(
      grepl("Apulchra", Species) ~ "apul",
      grepl("Porites_evermanni", Species) ~ "peve",
      grepl("Pocillopora_meandrina", Species) ~ "ptuh",
      TRUE ~ "other"
    )
  )

# Summarize species presence per orthogroup
h_ortho_summary <- h_ortho %>%
  group_by(Orthogroup) %>%
  summarise(
    type = case_when(
      all(c("apul", "peve", "ptuh") %in% simple_species) ~ "three_way",
      all(c("apul", "peve") %in% simple_species) & !("ptuh" %in% simple_species) ~ "apul_peve",
      all(c("apul", "ptuh") %in% simple_species) & !("peve" %in% simple_species) ~ "apul_ptuh",
      all(c("peve", "ptuh") %in% simple_species) & !("apul" %in% simple_species) ~ "peve_ptuh",
      TRUE ~ "other"
    ),
    .groups = "drop"
  )

# Join back to original
h_ortho <- h_ortho %>%
  left_join(h_ortho_summary, by = "Orthogroup")

Huh, looks like Hollie’s analysis only outputs orthogroups which contain at least one protein from each species

Read in Steven’s orthogroup analysis output:

s_ortho <- read.csv("https://github.com/urol-e5/timeseries_molecular/raw/refs/heads/main/M-multi-species/output/11-orthology-analysis/ortholog_groups.csv", header=TRUE)

Annotate each edges file with orthogroup info from both analyses

# Annotate each target with orthogroup type
Apul_edges_ortho <- Apul_edges %>%
  rowwise() %>%
  mutate(
    h_ortho_type = {
      match_row <- h_ortho %>%
        filter(str_detect(Proteins, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$type[1]
      } else {
        "none"
      }
    },
    s_ortho_type = {
      match_row <- s_ortho %>%
        filter(str_detect(apul, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$type[1]
      } else {
        "none"
      }
    }
  ) %>%
  mutate(species = "Apul") %>%
  ungroup()

Peve_edges_ortho <- Peve_edges %>%
  rowwise() %>%
  mutate(
    h_ortho_type = {
      match_row <- h_ortho %>%
        filter(str_detect(Proteins, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$type[1]
      } else {
        "none"
      }
    },
    s_ortho_type = {
      match_row <- s_ortho %>%
        filter(str_detect(peve, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$type[1]
      } else {
        "none"
      }
    }
  ) %>%
  mutate(species = "Peve") %>%
  ungroup()

Ptuh_edges_ortho <- Ptuh_edges %>%
  rowwise() %>%
  mutate(
    h_ortho_type = {
      match_row <- h_ortho %>%
        filter(str_detect(Proteins, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$type[1]
      } else {
        "none"
      }
    },
    s_ortho_type = {
      match_row <- s_ortho %>%
        filter(str_detect(ptua, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$type[1]
      } else {
        "none"
      }
    }
  ) %>%
  mutate(species = "Ptuh") %>%
  ungroup()

Since Steven’s includes the possibility of orthologs in species subsets (e.g. genes shared between only A. pulchra and P. evermanni), I’ll use his ortholog set now for plotting.

Prelim CIRCOS plot

Note: Holy crappoly this plot was exceedingly difficult to put together, even when using ChatGPT and Claude Sonnet 4.5 for help – I was troubleshooting problems for hours. But, once I tried using Lucas’s paid version (Claude Opus 4.5, integrated into VS code terminal, which he has for his work as a software developer), the agent was able to immediately generate exactly what I was envisioning and make requested changes with no back-and-forth or error handling on my end – holy jinkies Claude Opus is powerful!

data <- rbind(Apul_edges_ortho, Peve_edges_ortho, Ptuh_edges_ortho)

# Add target_type column to distinguish lncRNA from genes
# Also fix any typos in s_ortho_type (ptua -> ptuh)
data <- data %>%
  mutate(
    target_type = ifelse(grepl("^lncRNA_", target), "lncRNA", "gene"),
    s_ortho_type = case_when(
      s_ortho_type == "apul_ptua" ~ "apul_ptuh",
      s_ortho_type == "peve_ptua" ~ "peve_ptuh",
      TRUE ~ s_ortho_type
    )
  )

# Define the segment order and which species appear in each
# We'll have segments for both lncRNA (left half) and genes (right half)
segment_info <- list(
  # lncRNA segments (left half of circle)
  "lnc_all_3" = list(
    label = "All 3",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "three_way",
    target_type = "lncRNA"
  ),
  "lnc_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_type = "apul_peve",
    target_type = "lncRNA"
  ),
  "lnc_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_type = "peve_ptuh",
    target_type = "lncRNA"
  ),
  "lnc_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_type = "apul_ptuh",
    target_type = "lncRNA"
  ),
  "lnc_apul_only" = list(
    label = "Apul",
    species = c("Apul"),
    ortho_type = "none",
    target_type = "lncRNA"
  ),
  "lnc_peve_only" = list(
    label = "Peve",
    species = c("Peve"),
    ortho_type = "none",
    target_type = "lncRNA"
  ),
  "lnc_ptuh_only" = list(
    label = "Ptuh",
    species = c("Ptuh"),
    ortho_type = "none",
    target_type = "lncRNA"
  ),
  # Gene segments (right half of circle)
  "gene_all_3" = list(
    label = "All 3",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "three_way",
    target_type = "gene"
  ),
  "gene_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_type = "apul_peve",
    target_type = "gene"
  ),
  "gene_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_type = "peve_ptuh",
    target_type = "gene"
  ),
  "gene_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_type = "apul_ptuh",
    target_type = "gene"
  ),
  "gene_apul_only" = list(
    label = "Apul",
    species = c("Apul"),
    ortho_type = "none",
    target_type = "gene"
  ),
  "gene_peve_only" = list(
    label = "Peve",
    species = c("Peve"),
    ortho_type = "none",
    target_type = "gene"
  ),
  "gene_ptuh_only" = list(
    label = "Ptuh",
    species = c("Ptuh"),
    ortho_type = "none",
    target_type = "gene"
  )
)

# Calculate segment sizes based on actual data
calc_segment_sizes <- function(data, segment_info) {
  sizes <- list()

  for (seg_name in names(segment_info)) {
    seg <- segment_info[[seg_name]]

    if (seg$ortho_type == "none") {
      # Species-specific: filter by species and target type
      n <- data %>%
        filter(s_ortho_type == "none",
               species %in% seg$species,
               target_type == seg$target_type) %>%
        select(target) %>% distinct() %>% nrow()
    } else {
      # Conserved: filter by ortho type and target type
      n <- data %>%
        filter(s_ortho_type == seg$ortho_type,
               target_type == seg$target_type) %>%
        select(target) %>% distinct() %>% nrow()
    }
    sizes[[seg_name]] <- max(n, 1)  # Minimum size of 1 for visual consistency
  }

  return(sizes)
}

# Create improved circos plot with lncRNA/gene split
create_circos_plot_v2 <- function(data) {

  # Species order (outer to inner): Apul, Peve, Ptuh
  species_order <- c("Apul", "Peve", "Ptuh")

  # Calculate segment sizes
  seg_sizes <- calc_segment_sizes(data, segment_info)

  # Define sector order: lncRNA segments first (will be left half), then gene segments (right half)
  lnc_segments <- c("lnc_all_3", "lnc_apul_peve", "lnc_peve_ptuh", "lnc_apul_ptuh",
                    "lnc_apul_only", "lnc_peve_only", "lnc_ptuh_only")
  gene_segments <- c("gene_all_3", "gene_apul_peve", "gene_peve_ptuh", "gene_apul_ptuh",
                     "gene_apul_only", "gene_peve_only", "gene_ptuh_only")

  # Filter to only segments with data
  lnc_segments <- lnc_segments[sapply(lnc_segments, function(s) {
    seg <- segment_info[[s]]
    if (seg$ortho_type == "none") {
      nrow(data %>% filter(s_ortho_type == "none", species %in% seg$species, target_type == "lncRNA")) > 0
    } else {
      nrow(data %>% filter(s_ortho_type == seg$ortho_type, target_type == "lncRNA")) > 0
    }
  })]

  gene_segments <- gene_segments[sapply(gene_segments, function(s) {
    seg <- segment_info[[s]]
    if (seg$ortho_type == "none") {
      nrow(data %>% filter(s_ortho_type == "none", species %in% seg$species, target_type == "gene")) > 0
    } else {
      nrow(data %>% filter(s_ortho_type == seg$ortho_type, target_type == "gene")) > 0
    }
  })]

  # All sectors in order
  all_sectors <- c(lnc_segments, gene_segments)

  if (length(all_sectors) == 0) {
    message("No data to plot")
    return(invisible(NULL))
  }

  # Build xlims
  xlims <- list()
  for (seg_name in all_sectors) {
    xlims[[seg_name]] <- c(0, seg_sizes[[seg_name]])
  }

  # Initialize circos
  circos.clear()

  # Calculate gap degrees - larger gaps between lncRNA and gene halves
  n_lnc <- length(lnc_segments)
  n_gene <- length(gene_segments)
  n_total <- n_lnc + n_gene

  # Create gap vector: small gaps within each half, large gap between halves
  gap_degrees <- rep(4, n_total)
  if (n_lnc > 0 && n_gene > 0) {
    gap_degrees[n_lnc] <- 25  # Gap after last lncRNA segment (before genes)
    gap_degrees[n_total] <- 25  # Gap after last gene segment (before lncRNA)
  }

  circos.par(
    start.degree = 90,
    gap.degree = gap_degrees,
    track.margin = c(0.02, 0.02),
    cell.padding = c(0.02, 0.1, 0.02, 0.1)
  )

  circos.initialize(
    factors = factor(all_sectors, levels = all_sectors),
    xlim = do.call(rbind, xlims[all_sectors])
  )

  # Colors
  highlight_col <- rgb(0.6, 0.85, 1, 0.6)  # Light blue for conserved
  no_highlight_col <- rgb(0.95, 0.95, 0.95, 1)  # Light grey for species-specific
  pos_col <- rgb(0.2, 0.7, 0.2, 0.9)  # Green for positive PCC
  neg_col <- rgb(0.9, 0.4, 0.6, 0.9)  # Pink for negative PCC
  border_col <- rgb(0.3, 0.5, 0.7, 1)  # Blue border

  # Create 3 tracks (one per species, outer to inner)
  for (sp_idx in 1:3) {
    sp <- species_order[sp_idx]

    circos.track(
      factors = factor(all_sectors, levels = all_sectors),
      ylim = c(0, 1),
      track.height = 0.15,
      bg.border = "grey60",
      bg.lwd = 1.5,
      panel.fun = function(x, y) {
        sector_name <- CELL_META$sector.index
        seg <- segment_info[[sector_name]]

        # Check if this species appears in this segment
        sp_in_segment <- sp %in% seg$species

        # Background color
        if (sp_in_segment && seg$ortho_type != "none") {
          bg_col <- highlight_col
          border <- border_col
        } else if (sp_in_segment) {
          bg_col <- no_highlight_col
          border <- "grey60"
        } else {
          bg_col <- "white"
          border <- "grey80"
        }

        # Draw background rectangle
        circos.rect(
          CELL_META$xlim[1], 0,
          CELL_META$xlim[2], 1,
          col = bg_col,
          border = border,
          lwd = 1.5
        )

        # Plot points if species is in segment
        if (sp_in_segment) {
          # Get targets for this segment
          if (seg$ortho_type == "none") {
            seg_data <- data %>%
              filter(species == sp,
                     s_ortho_type == "none",
                     target_type == seg$target_type) %>%
              distinct(target, .keep_all = TRUE)
          } else {
            seg_data <- data %>%
              filter(species == sp,
                     s_ortho_type == seg$ortho_type,
                     target_type == seg$target_type) %>%
              distinct(target, .keep_all = TRUE)
          }

          if (nrow(seg_data) > 0) {
            n_points <- nrow(seg_data)
            if (n_points == 1) {
              x_positions <- CELL_META$xlim[2] / 2
            } else {
              x_positions <- seq(0.5, CELL_META$xlim[2] - 0.5, length.out = n_points)
            }

            for (i in 1:n_points) {
              pt_col <- ifelse(seg_data$PCC_direction[i] > 0, pos_col, neg_col)
              circos.points(
                x_positions[i], 0.5,
                col = pt_col,
                pch = 21,
                bg = pt_col,
                cex = 1.5
              )
            }
          }
        }
      }
    )
  }

  # Add segment labels on the outside
  for (seg_name in all_sectors) {
    seg <- segment_info[[seg_name]]

    circos.text(
      x = mean(xlims[[seg_name]]),
      y = 1.8,
      sector.index = seg_name,
      track.index = 1,
      labels = seg$label,
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 0.7,
      font = 2,
      col = "grey30"
    )
  }

  # Add "lncRNA" and "Genes" labels for each half
  # These need to be positioned at the center of each half
  if (n_lnc > 0) {
    # Get middle lncRNA segment for positioning
    mid_lnc <- lnc_segments[ceiling(n_lnc / 2)]
    circos.text(
      x = mean(xlims[[mid_lnc]]),
      y = 2.8,
      sector.index = mid_lnc,
      track.index = 1,
      labels = "lncRNA Targets",
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 1.1,
      font = 2,
      col = "darkblue"
    )
  }

  if (n_gene > 0) {
    # Get middle gene segment for positioning
    mid_gene <- gene_segments[ceiling(n_gene / 2)]
    circos.text(
      x = mean(xlims[[mid_gene]]),
      y = 2.8,
      sector.index = mid_gene,
      track.index = 1,
      labels = "Gene Targets",
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 1.1,
      font = 2,
      col = "darkred"
    )
  }

  # Add species labels in the center
  text(0, 0.08, "Apul", font = 2, cex = 1.0, col = "grey30")
  text(0, 0, "Peve", font = 2, cex = 1.0, col = "grey30")
  text(0, -0.08, "Ptuh", font = 2, cex = 1.0, col = "grey30")

  # Add legend
  legend(
    "bottomright",
    legend = c("Positive PCC", "Negative PCC", "Conserved region"),
    pch = c(21, 21, 22),
    pt.bg = c(pos_col, neg_col, highlight_col),
    col = c(pos_col, neg_col, border_col),
    pt.cex = c(1.5, 1.5, 2.2),
    bty = "n",
    cex = 0.85
  )

  # Add title
  title("miR-100 Targets by Conservation", line = -1, cex.main = 1.3)

  circos.clear()
}

# Create the plot
create_circos_plot_v2(data)
Note: 1 point is out of plotting region in sector 'lnc_apul_only',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_apul_only',
track '1'.
Note: 4 points are out of plotting region in sector 'lnc_apul_only',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_peve_only',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_peve_only',
track '1'.
Note: 4 points are out of plotting region in sector 'lnc_peve_only',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_ptuh_only',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_ptuh_only',
track '1'.
Note: 4 points are out of plotting region in sector 'lnc_ptuh_only',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 5 points are out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_only',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_only',
track '1'.
Note: 4 points are out of plotting region in sector 'gene_apul_only',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_only',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_only',
track '1'.
Note: 4 points are out of plotting region in sector 'gene_peve_only',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_ptuh_only',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_ptuh_only',
track '1'.
Note: 4 points are out of plotting region in sector 'gene_ptuh_only',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_peve_only',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_peve_only',
track '1'.
Note: 14 points are out of plotting region in sector 'lnc_peve_only',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 12 points are out of plotting region in sector 'gene_apul_ptuh',
track '1'.

With Steven’s ortholog sets, v3

This version combines all species-specific (unconserved) targets into a single segment per target type, where each species’ targets overlap in the same angular space but are not shaded to indicate conservation.

# Set margins to allow room for outer labels
par(mar = c(2, 2, 4, 2))

# Define segments for compact version - unconserved targets share same segment
segment_info_v3 <- list(
  # lncRNA segments (left half of circle)
  "lnc_all_3" = list(
    label = "All 3",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "three_way",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_type = "apul_peve",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_type = "peve_ptuh",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_type = "apul_ptuh",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_unconserved" = list(
    label = "Unconserved",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "none",
    target_type = "lncRNA",
    conserved = FALSE
  ),
  # Gene segments (right half of circle)
  "gene_all_3" = list(
    label = "All 3",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "three_way",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_type = "apul_peve",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_type = "peve_ptuh",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_type = "apul_ptuh",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_unconserved" = list(
    label = "Unconserved",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "none",
    target_type = "gene",
    conserved = FALSE
  )
)

# Calculate segment sizes for v3 - unconserved segments need max across all species
calc_segment_sizes_v3 <- function(data, segment_info) {
  sizes <- list()

  for (seg_name in names(segment_info)) {
    seg <- segment_info[[seg_name]]

    if (seg$ortho_type == "none") {
      # Unconserved: find the max count across all species
      max_n <- 0
      for (sp in seg$species) {
        n <- data %>%
          filter(s_ortho_type == "none",
                 species == sp,
                 target_type == seg$target_type) %>%
          select(target) %>% distinct() %>% nrow()
        max_n <- max(max_n, n)
      }
      sizes[[seg_name]] <- max(max_n, 1)
    } else {
      # Conserved: filter by ortho type and target type
      n <- data %>%
        filter(s_ortho_type == seg$ortho_type,
               target_type == seg$target_type) %>%
        select(target) %>% distinct() %>% nrow()
      sizes[[seg_name]] <- max(n, 1)
    }
  }

  return(sizes)
}

# Create compact circos plot with overlapping unconserved targets
create_circos_plot_v3 <- function(data) {

 # Species order (outer to inner): Peve (most), Apul, Ptuh (fewest)
  species_order <- c("Peve", "Apul", "Ptuh")

  # Calculate segment sizes
  seg_sizes <- calc_segment_sizes_v3(data, segment_info_v3)

  # Define sector order: lncRNA segments first, then gene segments
  lnc_segments <- c("lnc_all_3", "lnc_apul_peve", "lnc_peve_ptuh", "lnc_apul_ptuh", "lnc_unconserved")
  gene_segments <- c("gene_all_3", "gene_apul_peve", "gene_apul_ptuh",  "gene_peve_ptuh", "gene_unconserved")

  # Filter to only segments with data
  lnc_segments <- lnc_segments[sapply(lnc_segments, function(s) {
    seg <- segment_info_v3[[s]]
    if (seg$ortho_type == "none") {
      nrow(data %>% filter(s_ortho_type == "none", target_type == "lncRNA")) > 0
    } else {
      nrow(data %>% filter(s_ortho_type == seg$ortho_type, target_type == "lncRNA")) > 0
    }
  })]

  gene_segments <- gene_segments[sapply(gene_segments, function(s) {
    seg <- segment_info_v3[[s]]
    if (seg$ortho_type == "none") {
      nrow(data %>% filter(s_ortho_type == "none", target_type == "gene")) > 0
    } else {
      nrow(data %>% filter(s_ortho_type == seg$ortho_type, target_type == "gene")) > 0
    }
  })]

  # All sectors in order
  all_sectors <- c(lnc_segments, gene_segments)

  if (length(all_sectors) == 0) {
    message("No data to plot")
    return(invisible(NULL))
  }

  # Build xlims
  xlims <- list()
  for (seg_name in all_sectors) {
    xlims[[seg_name]] <- c(0, seg_sizes[[seg_name]])
  }

  # Initialize circos
  circos.clear()

  # Calculate gap degrees - larger gaps between lncRNA and gene halves
  n_lnc <- length(lnc_segments)
  n_gene <- length(gene_segments)
  n_total <- n_lnc + n_gene

  # Create gap vector: small gaps within each half, large gap between halves
  gap_degrees <- rep(3, n_total)
  if (n_lnc > 0 && n_gene > 0) {
    gap_degrees[n_lnc] <- 10
    gap_degrees[n_total] <- 20
  }

  circos.par(
    start.degree = 90,
    gap.degree = gap_degrees,
    track.margin = c(0.02, 0.02),
    cell.padding = c(0.02, 0.1, 0.02, 0.1),
    canvas.xlim = c(-1.4, 1.4),
    canvas.ylim = c(-1.4, 1.4)
  )

  circos.initialize(
    factors = factor(all_sectors, levels = all_sectors),
    xlim = do.call(rbind, xlims[all_sectors])
  )

  # Colors
  highlight_col <- rgb(0.6, 0.85, 1, 0.6)  # Light blue for conserved
  no_highlight_col <- rgb(0.95, 0.95, 0.95, 1)  # Light grey for unconserved
  pos_col <- rgb(0.2, 0.7, 0.2, 0.9)  # Green for positive PCC
  neg_col <- rgb(0.9, 0.4, 0.6, 0.9)  # Pink for negative PCC
  border_col <- rgb(0.3, 0.5, 0.7, 1)  # Blue border

  # Create 3 tracks (one per species, outer to inner)
  for (sp_idx in 1:3) {
    sp <- species_order[sp_idx]

    circos.track(
      factors = factor(all_sectors, levels = all_sectors),
      ylim = c(0, 1),
      track.height = 0.15,
      bg.border = "grey60",
      bg.lwd = 1.5,
      panel.fun = function(x, y) {
        sector_name <- CELL_META$sector.index
        seg <- segment_info_v3[[sector_name]]

        # For conserved segments, check if species is in the conservation group
        # For unconserved, all species appear but no highlighting
        if (seg$conserved) {
          sp_in_segment <- sp %in% seg$species
        } else {
          sp_in_segment <- TRUE  # All species appear in unconserved segment
        }

        # Background color
        if (seg$conserved && sp_in_segment) {
          bg_col <- highlight_col
          border <- border_col
        } else if (sp_in_segment) {
          bg_col <- no_highlight_col
          border <- "grey60"
        } else {
          bg_col <- "white"
          border <- "grey80"
        }

        # Draw background rectangle
        circos.rect(
          CELL_META$xlim[1], 0,
          CELL_META$xlim[2], 1,
          col = bg_col,
          border = border,
          lwd = 1.5
        )

        # Plot points
        if (seg$ortho_type == "none") {
          # Unconserved: each species plots its own targets
          seg_data <- data %>%
            filter(species == sp,
                   s_ortho_type == "none",
                   target_type == seg$target_type) %>%
            distinct(target, .keep_all = TRUE)
        } else if (sp_in_segment) {
          # Conserved: filter by ortho type
          seg_data <- data %>%
            filter(species == sp,
                   s_ortho_type == seg$ortho_type,
                   target_type == seg$target_type) %>%
            distinct(target, .keep_all = TRUE)
        } else {
          seg_data <- data.frame()
        }

        if (nrow(seg_data) > 0) {
          n_points <- nrow(seg_data)
          if (n_points == 1) {
            x_positions <- CELL_META$xlim[2] / 2
          } else {
            x_positions <- seq(0.5, CELL_META$xlim[2] - 0.5, length.out = n_points)
          }

          for (i in 1:n_points) {
            pt_col <- ifelse(seg_data$PCC_direction[i] > 0, pos_col, neg_col)
            circos.points(
              x_positions[i], 0.5,
              col = pt_col,
              pch = 21,
              bg = pt_col,
              cex = 0.75
            )
          }

          # Add count label above the feature points
          circos.text(
            x = mean(x_positions),
            y = 0.85,
            labels = as.character(n_points),
            facing = "bending.inside",
            niceFacing = TRUE,
            cex = 0.75,
            col = "black",
            font = 2
          )
        }
      }
    )
  }

  # Add segment labels on the outside
  for (seg_name in all_sectors) {
    seg <- segment_info_v3[[seg_name]]

    circos.text(
      x = mean(xlims[[seg_name]]),
      y = 1.8,
      sector.index = seg_name,
      track.index = 1,
      labels = seg$label,
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 0.7,
      font = 2,
      col = "grey30"
    )
  }

  # Add "lncRNA" and "Genes" labels for each half
  if (n_lnc > 0) {
    mid_lnc <- lnc_segments[ceiling(n_lnc / 2)]
    circos.text(
      x = mean(xlims[[mid_lnc]]),
      y = 2.5,
      sector.index = mid_lnc,
      track.index = 1,
      labels = "lncRNA Targets",
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 1.0,
      font = 2,
      col = "darkblue"
    )
  }

  if (n_gene > 0) {
    mid_gene <- gene_segments[ceiling(n_gene / 2)]
    circos.text(
      x = mean(xlims[[mid_gene]]),
      y = 2.5,
      sector.index = mid_gene,
      track.index = 1,
      labels = "Gene Targets",
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 1.0,
      font = 2,
      col = "darkred"
    )
  }

  # Add title and species labels in the center (outer to inner: Peve, Apul, Ptuh)
  text(0, 0.04, "miR-100 Targets", font = 2, cex = 1.1, col = "black")
  text(0, -0.04, "by Conservation", font = 2, cex = 1.1, col = "black")
  text(-0.09, 0.9, "Peve", font = 2, cex = 0.9, col = "grey30")
  text(-0.09, 0.71, "Apul", font = 2, cex = 0.9, col = "grey30")
  text(-0.09, 0.53, "Ptuh", font = 2, cex = 0.9, col = "grey30")

  # Add legend
  legend(
    "bottomright",
    legend = c("Positive PCC", "Negative PCC", "Conserved region"),
    pch = c(21, 21, 22),
    pt.bg = c(pos_col, neg_col, highlight_col),
    col = c(pos_col, neg_col, border_col),
    pt.cex = c(1.5, 1.5, 2.2),
    bty = "n",
    cex = 0.85
  )

  circos.clear()
}

# Create the compact plot
create_circos_plot_v3(data)
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 5 points are out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector
'gene_unconserved', track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 14 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 12 points are out of plotting region in sector 'gene_apul_ptuh',
track '1'.

# Save to png
png("output/2025_12_08_dde_plot_edits/s_orthos_circos_plot.png", width = 2000, height = 2000, res = 300)
create_circos_plot_v3(data)
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 5 points are out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 9 points are out of plotting region in sector 'gene_peve_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector
'gene_unconserved', track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 14 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_ptuh',
track '1'.
Note: 12 points are out of plotting region in sector 'gene_apul_ptuh',
track '1'.
dev.off()
png 
  2

With Hollie’s ortholog sets

# Set margins to allow room for outer labels
par(mar = c(2, 2, 4, 2))

# Define segments for compact version - unconserved targets share same segment
segment_info_v3 <- list(
  # lncRNA segments (left half of circle)
  "lnc_all_3" = list(
    label = "All 3",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "three_way",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_type = "apul_peve",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_type = "peve_ptuh",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_type = "apul_ptuh",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_unconserved" = list(
    label = "Unconserved",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "none",
    target_type = "lncRNA",
    conserved = FALSE
  ),
  # Gene segments (right half of circle)
  "gene_all_3" = list(
    label = "All 3",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "three_way",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_type = "apul_peve",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_type = "peve_ptuh",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_type = "apul_ptuh",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_unconserved" = list(
    label = "Unconserved",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_type = "none",
    target_type = "gene",
    conserved = FALSE
  )
)

# Calculate segment sizes for v3 - unconserved segments need max across all species
calc_segment_sizes_v3 <- function(data, segment_info) {
  sizes <- list()

  for (seg_name in names(segment_info)) {
    seg <- segment_info[[seg_name]]

    if (seg$ortho_type == "none") {
      # Unconserved: find the max count across all species
      max_n <- 0
      for (sp in seg$species) {
        n <- data %>%
          filter(h_ortho_type == "none",
                 species == sp,
                 target_type == seg$target_type) %>%
          select(target) %>% distinct() %>% nrow()
        max_n <- max(max_n, n)
      }
      sizes[[seg_name]] <- max(max_n, 1)
    } else {
      # Conserved: filter by ortho type and target type
      n <- data %>%
        filter(h_ortho_type == seg$ortho_type,
               target_type == seg$target_type) %>%
        select(target) %>% distinct() %>% nrow()
      sizes[[seg_name]] <- max(n, 1)
    }
  }

  return(sizes)
}

# Create compact circos plot with overlapping unconserved targets
create_circos_plot_v3 <- function(data) {

 # Species order (outer to inner): Peve (most), Apul, Ptuh (fewest)
  species_order <- c("Peve", "Apul", "Ptuh")

  # Calculate segment sizes
  seg_sizes <- calc_segment_sizes_v3(data, segment_info_v3)

  # Define sector order: lncRNA segments first, then gene segments
  lnc_segments <- c("lnc_all_3", "lnc_apul_peve", "lnc_peve_ptuh", "lnc_apul_ptuh", "lnc_unconserved")
  gene_segments <- c("gene_all_3", "gene_apul_peve", "gene_apul_ptuh",  "gene_peve_ptuh", "gene_unconserved")

  # Filter to only segments with data
  lnc_segments <- lnc_segments[sapply(lnc_segments, function(s) {
    seg <- segment_info_v3[[s]]
    if (seg$ortho_type == "none") {
      nrow(data %>% filter(h_ortho_type == "none", target_type == "lncRNA")) > 0
    } else {
      nrow(data %>% filter(h_ortho_type == seg$ortho_type, target_type == "lncRNA")) > 0
    }
  })]

  gene_segments <- gene_segments[sapply(gene_segments, function(s) {
    seg <- segment_info_v3[[s]]
    if (seg$ortho_type == "none") {
      nrow(data %>% filter(h_ortho_type == "none", target_type == "gene")) > 0
    } else {
      nrow(data %>% filter(h_ortho_type == seg$ortho_type, target_type == "gene")) > 0
    }
  })]

  # All sectors in order
  all_sectors <- c(lnc_segments, gene_segments)

  if (length(all_sectors) == 0) {
    message("No data to plot")
    return(invisible(NULL))
  }

  # Build xlims
  xlims <- list()
  for (seg_name in all_sectors) {
    xlims[[seg_name]] <- c(0, seg_sizes[[seg_name]])
  }

  # Initialize circos
  circos.clear()

  # Calculate gap degrees - larger gaps between lncRNA and gene halves
  n_lnc <- length(lnc_segments)
  n_gene <- length(gene_segments)
  n_total <- n_lnc + n_gene

  # Create gap vector: small gaps within each half, large gap between halves
  gap_degrees <- rep(3, n_total)
  if (n_lnc > 0 && n_gene > 0) {
    gap_degrees[n_lnc] <- 10
    gap_degrees[n_total] <- 20
  }

  circos.par(
    start.degree = 90,
    gap.degree = gap_degrees,
    track.margin = c(0.02, 0.02),
    cell.padding = c(0.02, 0.1, 0.02, 0.1),
    canvas.xlim = c(-1.4, 1.4),
    canvas.ylim = c(-1.4, 1.4)
  )

  circos.initialize(
    factors = factor(all_sectors, levels = all_sectors),
    xlim = do.call(rbind, xlims[all_sectors])
  )

  # Colors
  highlight_col <- rgb(0.6, 0.85, 1, 0.6)  # Light blue for conserved
  no_highlight_col <- rgb(0.95, 0.95, 0.95, 1)  # Light grey for unconserved
  pos_col <- rgb(0.2, 0.7, 0.2, 0.9)  # Green for positive PCC
  neg_col <- rgb(0.9, 0.4, 0.6, 0.9)  # Pink for negative PCC
  border_col <- rgb(0.3, 0.5, 0.7, 1)  # Blue border

  # Create 3 tracks (one per species, outer to inner)
  for (sp_idx in 1:3) {
    sp <- species_order[sp_idx]

    circos.track(
      factors = factor(all_sectors, levels = all_sectors),
      ylim = c(0, 1),
      track.height = 0.15,
      bg.border = "grey60",
      bg.lwd = 1.5,
      panel.fun = function(x, y) {
        sector_name <- CELL_META$sector.index
        seg <- segment_info_v3[[sector_name]]

        # For conserved segments, check if species is in the conservation group
        # For unconserved, all species appear but no highlighting
        if (seg$conserved) {
          sp_in_segment <- sp %in% seg$species
        } else {
          sp_in_segment <- TRUE  # All species appear in unconserved segment
        }

        # Background color
        if (seg$conserved && sp_in_segment) {
          bg_col <- highlight_col
          border <- border_col
        } else if (sp_in_segment) {
          bg_col <- no_highlight_col
          border <- "grey60"
        } else {
          bg_col <- "white"
          border <- "grey80"
        }

        # Draw background rectangle
        circos.rect(
          CELL_META$xlim[1], 0,
          CELL_META$xlim[2], 1,
          col = bg_col,
          border = border,
          lwd = 1.5
        )

        # Plot points
        if (seg$ortho_type == "none") {
          # Unconserved: each species plots its own targets
          seg_data <- data %>%
            filter(species == sp,
                   h_ortho_type == "none",
                   target_type == seg$target_type) %>%
            distinct(target, .keep_all = TRUE)
        } else if (sp_in_segment) {
          # Conserved: filter by ortho type
          seg_data <- data %>%
            filter(species == sp,
                   h_ortho_type == seg$ortho_type,
                   target_type == seg$target_type) %>%
            distinct(target, .keep_all = TRUE)
        } else {
          seg_data <- data.frame()
        }

        if (nrow(seg_data) > 0) {
          n_points <- nrow(seg_data)
          if (n_points == 1) {
            x_positions <- CELL_META$xlim[2] / 2
          } else {
            x_positions <- seq(0.5, CELL_META$xlim[2] - 0.5, length.out = n_points)
          }

          for (i in 1:n_points) {
            pt_col <- ifelse(seg_data$PCC_direction[i] > 0, pos_col, neg_col)
            circos.points(
              x_positions[i], 0.5,
              col = pt_col,
              pch = 21,
              bg = pt_col,
              cex = 0.75
            )
          }

          # Add count label above the feature points
          circos.text(
            x = mean(x_positions),
            y = 0.85,
            labels = as.character(n_points),
            facing = "bending.inside",
            niceFacing = TRUE,
            cex = 0.75,
            col = "black",
            font = 2
          )
        }
      }
    )
  }

  # Add segment labels on the outside
  for (seg_name in all_sectors) {
    seg <- segment_info_v3[[seg_name]]

    circos.text(
      x = mean(xlims[[seg_name]]),
      y = 1.8,
      sector.index = seg_name,
      track.index = 1,
      labels = seg$label,
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 0.7,
      font = 2,
      col = "grey30"
    )
  }

  # Add "lncRNA" and "Genes" labels for each half
  if (n_lnc > 0) {
    mid_lnc <- lnc_segments[ceiling(n_lnc / 2)]
    circos.text(
      x = mean(xlims[[mid_lnc]]),
      y = 2.5,
      sector.index = mid_lnc,
      track.index = 1,
      labels = "lncRNA Targets",
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 1.0,
      font = 2,
      col = "darkblue"
    )
  }

  if (n_gene > 0) {
    mid_gene <- gene_segments[ceiling(n_gene / 2)]
    circos.text(
      x = mean(xlims[[mid_gene]]),
      y = 2.5,
      sector.index = mid_gene,
      track.index = 1,
      labels = "Gene Targets",
      facing = "bending.outside",
      niceFacing = TRUE,
      cex = 1.0,
      font = 2,
      col = "darkred"
    )
  }

  # Add title and species labels in the center (outer to inner: Peve, Apul, Ptuh)
  text(0, 0.04, "miR-100 Targets", font = 2, cex = 1.1, col = "black")
  text(0, -0.04, "by Conservation", font = 2, cex = 1.1, col = "black")
  text(-0.09, 0.9, "Peve", font = 2, cex = 0.9, col = "grey30")
  text(-0.09, 0.71, "Apul", font = 2, cex = 0.9, col = "grey30")
  text(-0.09, 0.53, "Ptuh", font = 2, cex = 0.9, col = "grey30")

  # Add legend
  legend(
    "bottomright",
    legend = c("Positive PCC", "Negative PCC", "Conserved region"),
    pch = c(21, 21, 22),
    pt.bg = c(pos_col, neg_col, highlight_col),
    col = c(pos_col, neg_col, border_col),
    pt.cex = c(1.5, 1.5, 2.2),
    bty = "n",
    cex = 0.85
  )

  circos.clear()
}

# Create the compact plot
create_circos_plot_v3(data)
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 5 points are out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector
'gene_unconserved', track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 14 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 12 points are out of plotting region in sector 'gene_all_3',
track '1'.

# Save to png
png("output/2025_12_08_dde_plot_edits/h_orthos_circos_plot.png", width = 2000, height = 2000, res = 300)
create_circos_plot_v3(data)
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 5 points are out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_unconserved',
track '1'.
Note: 11 points are out of plotting region in sector
'gene_unconserved', track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 14 points are out of plotting region in sector 'lnc_unconserved',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 1 point is out of plotting region in sector 'gene_all_3', track
'1'.
Note: 12 points are out of plotting region in sector 'gene_all_3',
track '1'.
dev.off()
png 
  2