Deep Dive Expression: Figure edits part 2

E5-coral
Author

Kathleen Durkin

Published

April 8, 2026

Correcting CIRCOES plot generated here for DDE manuscript. Old plot shows which targets are orthologus genes (present in more than one species) – I need the plot to be showing orthologs that are targeted by miR-100 in more than one species.

TLDR: Only one orthogroup is targeted by miR-100 in more than one species, targeted in A. pulchra and P. evermanni. This ortholg is not functionally annotated.

Updated plot showing this conservation is stored here

Load libraries

library(dplyr)

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)
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$type[1]
      } else {
        "none"
      }
    },
    h_ortho_group = {
      match_row <- h_ortho %>%
        filter(str_detect(Proteins, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$Orthogroup[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$type[1]
      } else {
        "none"
      }
    },
    s_ortho_group = {
      match_row <- s_ortho %>%
        filter(str_detect(apul, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$group_id[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$type[1]
      } else {
        "none"
      }
    },
    h_ortho_group = {
      match_row <- h_ortho %>%
        filter(str_detect(Proteins, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$Orthogroup[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$type[1]
      } else {
        "none"
      }
    },
    s_ortho_group = {
      match_row <- s_ortho %>%
        filter(str_detect(peve, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$group_id[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$type[1]
      } else {
        "none"
      }
    },
    h_ortho_group = {
      match_row <- h_ortho %>%
        filter(str_detect(Proteins, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$Orthogroup[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$type[1]
      } else {
        "none"
      }
    },
    s_ortho_group = {
      match_row <- s_ortho %>%
        filter(str_detect(ptua, fixed(target)))
      if(nrow(match_row) > 0) {
        # If multiple matches, pick the first
        match_row$group_id[1]
      } else {
        "none"
      }
    }
  ) %>%
  mutate(species = "Ptuh") %>%
  ungroup()

Combine the three species, and identify which orthologs are putatively targeted in multiple species

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
    )
  )

# Identify cross-species ortholg targets

get_conserv <- function(data, ortho_col) {
  # Only look at rows with actual orthogroup assignments
  species_map <- data %>%
    filter(.data[[ortho_col]] != "none") %>%
    group_by(across(all_of(ortho_col))) %>%
    summarise(species_set = list(sort(unique(species))), .groups = "drop") %>%
    mutate(conserv = sapply(species_set, function(spp) {
      if (length(spp) >= 3) {
        "three-way"
      } else if (length(spp) == 2) {
        paste(tolower(spp), collapse = "_")
      } else {
        "none"
      }
    }))
  
  setNames(species_map$conserv, species_map[[ortho_col]])
}

h_lookup <- get_conserv(data, "h_ortho_group")
s_lookup <- get_conserv(data, "s_ortho_group")

data <- data %>%
  mutate(
    h_ortho_conserv = ifelse(h_ortho_group == "none", "none", h_lookup[h_ortho_group]),
    s_ortho_conserv = ifelse(s_ortho_group == "none", "none", s_lookup[s_ortho_group])
  )

Check

data %>% filter(h_ortho_conserv != "none" | s_ortho_conserv != "none") %>% select(target, species, h_ortho_group, h_ortho_conserv, s_ortho_group, s_ortho_conserv)
# A tibble: 2 × 6
  target     species h_ortho_group h_ortho_conserv s_ortho_group s_ortho_conserv
  <chr>      <chr>   <chr>         <chr>           <chr>         <chr>          
1 FUN_035913 Apul    OG0007080     apul_peve       OG_08144      apul_peve      
2 Peve_0000… Peve    OG0007080     apul_peve       OG_08144      apul_peve

Hmm, so it turns out there’s only one orthogroup that is targeted by miR-100 in more than one species – the orthogroup contains both FUN_035913 in A. pulchra and Peve_00006734 in P. evermanni. On top of that, this single conserved target is also not functionally annotated (does not closely match any previously functionall described sequence – checked here). On the bright side, this conserved targeting shows up in the outputs of both Hollie’s and Steven’s ortholog analyses, so I don’t need to worry about reconciling/picking between divergent results.

This isn’t particularly useful/exciting, so I’m not sure its even worth displaying in the planned plot, but I’ll go ahead and do it for posterity.

Plot

# 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_conserv = "three_way",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_conserv = "apul_peve",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_conserv = "peve_ptuh",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_conserv = "apul_ptuh",
    target_type = "lncRNA",
    conserved = TRUE
  ),
  "lnc_unconserved" = list(
    label = "Unconserved",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_conserv = "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_conserv = "three_way",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_apul_peve" = list(
    label = "Apul+Peve",
    species = c("Apul", "Peve"),
    ortho_conserv = "apul_peve",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_peve_ptuh" = list(
    label = "Peve+Ptuh",
    species = c("Peve", "Ptuh"),
    ortho_conserv = "peve_ptuh",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_apul_ptuh" = list(
    label = "Apul+Ptuh",
    species = c("Apul", "Ptuh"),
    ortho_conserv = "apul_ptuh",
    target_type = "gene",
    conserved = TRUE
  ),
  "gene_unconserved" = list(
    label = "Unconserved",
    species = c("Apul", "Peve", "Ptuh"),
    ortho_conserv = "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$ortho_conserv == "none") {
      # Unconserved: find the max count across all species
      max_n <- 0
      for (sp in seg$species) {
        n <- data %>%
          filter(s_ortho_conserv == "none",
                 species == sp,
                 target_type == seg$target_type) %>%
          select(target) %>% distinct() %>% nrow()
        max_n <- max(max_n, n)
      }
      sizes[[seg_name]] <- max(max_n, 1)
    } else {
      # Conserved: filter by ortho type and target type
      n <- data %>%
        filter(s_ortho_conserv == seg$ortho_conserv,
               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$ortho_conserv == "none") {
      nrow(data %>% filter(s_ortho_conserv == "none", target_type == "lncRNA")) > 0
    } else {
      nrow(data %>% filter(s_ortho_conserv == seg$ortho_conserv, target_type == "lncRNA")) > 0
    }
  })]

  gene_segments <- gene_segments[sapply(gene_segments, function(s) {
    seg <- segment_info_v3[[s]]
    if (seg$ortho_conserv == "none") {
      nrow(data %>% filter(s_ortho_conserv == "none", target_type == "gene")) > 0
    } else {
      nrow(data %>% filter(s_ortho_conserv == seg$ortho_conserv, 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$ortho_conserv == "none") {
          # Unconserved: each species plots its own targets
          seg_data <- data %>%
            filter(species == sp,
                   s_ortho_conserv == "none",
                   target_type == seg$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_conserv == seg$ortho_conserv,
                   target_type == seg$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_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_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_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 12 points are out of plotting region in sector 'gene_apul_peve',
track '1'.

# Save to png
png("output/2026_04_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_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_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_peve',
track '1'.
Note: 1 point is out of plotting region in sector 'gene_apul_peve',
track '1'.
Note: 12 points are out of plotting region in sector 'gene_apul_peve',
track '1'.
dev.off()
quartz_off_screen 
                2