Added some scripts I use to generate plots

r_scripts/clustering.R

+source("/home/rferon/work/analyses/radseq_all/r_scripts/utils.R")
+
+coverage_heatmap = function(coverage_file, popmap_file, output_file="none",
+                            remove_coverage_1=TRUE, sex_ratio_range=0.25,
+                            min_individuals_ratio=0.5, special_SD_system=FALSE,
+                            coverage_filtering_range=0.1, max_coverage=150,
+                            distance="binary", clustering="ward.D",
+                            x_axis = 'individuals', individuals.dendrogram = TRUE, loci.dendrogram = TRUE,
+                            males.color="steelblue3", females.color="orange1",
+                            palette=c("lightgrey", "royalblue2", "black", "gold2", "red3"),
+                            fasta_file_path="none", coverage_file_path="none", species="none") {
+
+    data = load_coverage_file(coverage_file)
+    popmap = load_popmap_file(popmap_file)
+
+    data = filter(data, popmap, min_ratio=min_individuals_ratio, remove_coverage_1 = remove_coverage_1, sex_ratio_range=sex_ratio_range,
+                  special=special_SD_system, ratio_range=coverage_filtering_range, max_coverage=max_coverage,
+                  fasta_file_path = fasta_file_path, coverage_file_path = coverage_file_path, species=species)
+
+    if (dim(data)[1] > 0) {
+        if(output_file != "none") { png(output_file, width=2400, height=1200, res=90) }
+        plot_heatmap(data, popmap,
+                     distance=distance, clustering=clustering,
+                     x_axis=x_axis, individuals.dendrogram=individuals.dendrogram, loci.dendrogram=loci.dendrogram,
+                     males.color=males.color, females.color=females.color, palette=palette)
+        if(output_file != "none") { o = dev.off() }
+    }
+}
+
+
+presence_heatmap = function(coverage_file, popmap_file, read.length=94,
+                            distance="binary", clustering="ward.D",
+                            x_axis='individuals', individuals.dendrogram=TRUE, loci.dendrogram=TRUE,
+                            males.color="steelblue3", females.color="orange1",
+                            absence.color="cornsilk", presence.color="skyblue3") {
+
+    data = load_coverage_file(coverage_file)
+    popmap = load_popmap_file(popmap_file)
+
+    data = convert_to_presence_data(data, read.length=read.length)
+
+    if (dim(data)[1] > 0) {
+        plot_heatmap(data, popmap,
+                     distance=distance, clustering=clustering,
+                     x_axis=x_axis, individuals.dendrogram=individuals.dendrogram, loci.dendrogram=loci.dendrogram,
+                     males.color=males.color, females.color=females.color,
+                     absence.color=absence.color, presence.color=presence.color)
+    }
+}
+
+
+
+export_to_fasta = function(data, file_path) {
+
+    output_file = file(file_path)
+    lines = c()
+    for (i in 1:dim(data)[1]) {
+        name = ">"
+        name = paste(name, data$Name[i], sep="")
+        sequence = as.character(data$Sequence[i])
+        lines = c(lines, name, sequence)
+    }
+    writeLines(lines, output_file)
+    close(output_file)
+}
+
+
+
+export_coverage = function(data, file_path) {
+    names(data)[1] = "Haplotype"
+    write.table(data, file=file_path, quote=FALSE, sep="\t", row.names=FALSE)
+}
+
+
+filter = function(data, popmap, min_ratio=0.5, special=FALSE, ratio_range=0.1, sex_ratio_range = 0.25, max_coverage=150, remove_coverage_1=TRUE,
+                  fasta_file_path="none", coverage_file_path="none", species="none") {
+
+    fasta = TRUE
+    if (fasta_file_path == "none") { fasta = FALSE }
+
+    depths = data[, 7:dim(data)[2]]
+    depths[depths == 0] = NA
+    if (remove_coverage_1){ depths[depths==1] = NA }
+
+    # REMOVE LOCI WITH LESS MIN.RATIO MALES AND LESS THAN MIN.RATIO FEMALES
+    min_males = floor(sum(popmap$Sex == 'M') * min_ratio)
+    min_females = floor(sum(popmap$Sex == 'F') * min_ratio)
+    males_col = which(names(depths) %in% popmap$Individual[which(popmap$Sex == 'M')])
+    females_col = which(names(depths) %in% popmap$Individual[which(popmap$Sex == 'F')])
+    too_few_individuals = which((rowSums(!is.na(depths[, males_col])) < min_males) & (rowSums(!is.na(depths[, females_col])) < min_females) )
+    data = data[-too_few_individuals,]
+    depths = depths[-too_few_individuals,]
+
+    # IF A VALUE IS > MAX.COVERAGE PERCENTILE, REPLACE WITH MAX.COVERAGE PERCENTILE
+    depths[depths > max_coverage] = max_coverage
+
+    if (!special) {
+        # Filter XY alleles
+        males_means = rowMeans(depths[, males_col], na.rm = TRUE)
+        females_means = rowMeans(depths[, females_col], na.rm = TRUE)
+        males_means[males_means=='NaN'] = 0
+        females_means[females_means=='NaN'] = 0
+
+        # Sequences in 50% more males than females
+        n_males = rowSums(!is.na(depths[, males_col]))
+        n_females = rowSums(!is.na(depths[, females_col]))
+        sex_ratio = (n_males / (n_males + n_females))
+        to_keep = which(sex_ratio > 1-sex_ratio_range | sex_ratio < sex_ratio_range)
+
+        # Sequences with coverage bias
+        unbiased_value = 0.5
+        ratio = (males_means) / (males_means + females_means)
+        to_keep = union(to_keep, which(ratio > unbiased_value + ratio_range | ratio < unbiased_value - ratio_range))
+
+        # Filtering
+        depths = depths[to_keep,]
+        data = data[to_keep,]
+    }
+
+    depths[is.na(depths)] = 0
+
+    data = data.frame(Origin=rep("", dim(data)[1]), Males=rowSums(depths[, males_col] > 0), Females=rowSums(depths[, females_col] > 0),
+                      Other_allele=rep("", dim(data)[1]), data[,1:6], depths)
+    individual_names = names(depths)
+    data$Origin = as.character(data$Origin)
+    data$Haplotype_ID = as.character(data$Haplotype_ID)
+    data$Stack_ID = as.character(data$Stack_ID)
+    data$Other_allele = as.character(data$Other_allele)
+
+    for (i in 1:dim(data)[1]) {
+        data$Stack_ID[i] = strsplit(as.character(data$Stack_ID[i]), "_")[[1]][1]
+        if (data$Haplotype_ID[i] == data$Stack_ID[i]) {
+            data$Origin[i] = "Heatmap"
+        } else {
+            data$Origin[i] = "Blasting"
+        }
+    }
+
+    heatmap = subset(data, data$Origin == 'Heatmap')
+    blasting = subset(data, data$Origin == 'Blasting' & !(data$Haplotype_ID %in% heatmap$Stack_ID))
+    blasting$Other_allele = blasting$Stack_ID
+
+    # When a haplotype was rescued from multiple haplotypes, only keep best matching scores
+    to_remove = c()
+    haplotypes = unique(blasting$Haplotype_ID)
+    for (i in 1:length(haplotypes)) {
+        temp = max(blasting$Matches[blasting$Haplotype_ID == haplotypes[i]])
+        to_remove = c(to_remove, which(blasting$Haplotype_ID == haplotypes[i] & blasting$Matches < temp))
+        max = which(blasting$Haplotype_ID == haplotypes[i] & blasting$Matches == temp)
+        if (length(max > 1)) {
+            to_remove = c(to_remove, max[-1])
+            blasting$Other_allele[max[1]] = paste("Complementary:", paste(blasting$Stack_ID[max], collapse=","), sep="")
+        }
+    }
+    if (length(to_remove) > 0) {blasting = blasting[-to_remove, ]}
+
+    for (i in 1:dim(heatmap)[1]) {
+        candidates = which(blasting$Stack_ID == heatmap$Haplotype_ID[i])
+        if (length(candidates) == 0) {
+            heatmap$Other_allele[i] = "Specific"
+        } else {
+            heatmap$Other_allele[i] = paste("Complementary:", paste(blasting$Haplotype_ID[candidates], collapse=","), sep="")
+        }
+    }
+
+    combined = rbind(heatmap, blasting)
+    new_ids = paste(species, "_", combined$Haplotype_ID, "_", combined$Males, "M_", combined$Females, "F_",
+                          combined$Origin, "_", combined$Other_allele, sep="")
+    combined = data.frame(Name=new_ids, combined)
+    names(combined)[12:dim(combined)[2]] = individual_names
+
+    export_to_fasta(subset(combined, select=c("Name", "Sequence")), fasta_file_path)
+    export_coverage(combined[,c(1, 12:dim(combined)[2])], coverage_file_path)
+
+
+    return(combined[, c(7, 12:dim(combined)[2])])
+}
+
+
+convert_to_presence_data = function(data, read.length=94) {
+
+    data = subset(data, data$Matches == read.length)
+    depths = data[, 7:dim(data)[2]]
+    depths[depths>0] = 1
+
+    new_data = data.frame(data[,1], depths)
+    names(new_data) = c("Haplotype_ID", names(data[, 7:dim(data)[2]]))
+
+    return(new_data)
+}
+
+
+plot_heatmap = function(data, popmap=data.frame(), x_axis='individuals',
+                        distance="binary", clustering="ward.D",
+                        individuals.dendrogram=TRUE, loci.dendrogram=TRUE,
+                        males.color="steelblue3", females.color="orange1",
+                        palette=c("lightgrey", "royalblue2", "black", "gold2", "red3"),
+                        absence.color="cornsilk", presence.color="skyblue3") {
+
+    x = as.matrix(data[,-1], rownames.force = TRUE)
+    rownames(x) =  data$Haplotype_ID
+
+    if (dim(data)[1] > 200) {
+        tile_separation_size = 0.05
+    } else {
+        tile_separation_size = 0.1
+    }
+
+    individuals.d = dist(t(x), method=distance)
+    individuals.clusters = hclust(individuals.d, method=clustering)
+    ordered = individuals.clusters$labels[individuals.clusters$order]
+    temp = c(1)
+    for (i in 1:length(ordered)) {
+        temp = c(temp, which(names(data) == ordered[i]))
+    }
+    individuals.order = temp
+
+    loci.d = dist(x, method=distance)
+    loci.clusters = hclust(loci.d, method=clustering)
+    ordered = loci.clusters$labels[loci.clusters$order]
+    temp = c()
+    for (i in 1:length(ordered)) {
+        temp = c(temp, which(data$Haplotype_ID == ordered[i]))
+    }
+    loci.order = temp
+
+    data = data[,individuals.order]
+    data = data[loci.order,]
+
+    melted = suppressMessages(melt(data, id.vars = c("Haplotype_ID"), variable.name = "Individuals", value.name = "Values"))
+    melted$Haplotype_ID = factor(melted$Haplotype_ID, levels = data$Haplotype_ID)
+    melted$Individuals = factor(as.character(melted$Individuals), levels = as.character(names(data)[-1]))
+
+    if (length(unique(melted$Values)) == 2) {
+        melted$Values = factor(melted$Values, levels = unique(melted$Values))
+    } else {
+        melted$Values = as.numeric(melted$Values)
+    }
+
+    all_values = melted$Values
+    all_values[all_values==0] = NA
+    stats = summary(all_values, na.rm=TRUE)
+
+    if (length(popmap) > 0) {
+        sexes = c(popmap$Sex)
+        names(sexes) = c(popmap$Individual)
+    }
+
+    sex_palette = c("M"=males.color, "F"=females.color)
+
+    if (x_axis == "individuals") {
+
+        g = ggplot(melted, aes(x=Individuals, y=Haplotype_ID, fill=Values)) +
+            geom_tile(color="grey60", size=tile_separation_size) +
+            theme(axis.text.x = element_text(angle=60, hjust = 1)) +
+            ylab("Loci") + xlab("Individuals")
+
+        if (length(unique(all_values)) != 2) {
+            g = g + scale_fill_gradientn(name = "Coverage", colours = palette, values = c(0, 0.001, stats[3]/stats[6], stats[5]/stats[6], 1))
+        } else {
+            g = g + scale_fill_manual(breaks = c("0", "1"), labels = c("Absent", "Present"), values = c("0"=absence.color, "1"=presence.color)) +
+                theme(legend.title=element_blank())
+        }
+
+        if (length(popmap) > 0) {
+            g = g + theme(axis.text.x = element_text(colour=sex_palette[sexes[names(data)[-1]]]))
+        }
+
+        a = ggplotGrob(g)
+        loci_start = 4
+
+        if (individuals.dendrogram == TRUE) {
+
+            h = suppressMessages(ggdendrogram(individuals.clusters, labels = FALSE, leaf_labels = FALSE, theme_dendro = TRUE, rotate = FALSE) +
+                                     theme(plot.margin = unit(c(0.1, 0, 0, 0), 'cm'), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x=element_blank()) +
+                                     scale_y_continuous(expand = c(0,0.5)) + scale_x_continuous(expand=c(0,0.5)))
+            b = ggplotGrob(h)
+            a = gtable_add_rows(a, unit(1.1, "cm"), pos=3)
+            a = gtable_add_grob(a, b, t = 3, l=4, b=5, r=5)
+            loci_start = loci_start + 1
+        }
+
+        if (loci.dendrogram == TRUE) {
+
+            i = suppressMessages(ggdendrogram(loci.clusters, labels = FALSE, leaf_labels = FALSE, theme_dendro = TRUE, rotate = FALSE) +
+                                     theme(plot.margin = unit(c(0.1, 0, 0, 0), 'cm'), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x=element_blank()) +
+                                     coord_flip() + scale_y_continuous(expand = c(0,0.5)) + scale_x_continuous(expand=c(0,0)))
+            c = ggplotGrob(i)
+            a = gtable_add_cols(a, unit(1.1, "cm"), pos=5)
+            a = gtable_add_grob(a, c, t = loci_start, l=5, b=loci_start + 2, r=6)
+        }
+
+        grid.newpage()
+        grid.draw(a)
+
+    } else if (x_axis == "loci") {
+
+        g = ggplot(melted, aes(x=Haplotype_ID, y=Individuals, fill=Values)) +
+            geom_tile(color="grey60", size=tile_separation_size) +
+            theme(axis.text.x = element_text(angle=60, hjust = 1)) +
+            xlab("Loci") + ylab("Individuals")
+
+        if (length(unique(all_values)) != 2) {
+            g = g + scale_fill_gradientn(name="Coverage", colours = palette, values = c(0, 0.001, stats[4]/stats[6], stats[5]/stats[6], 1))
+        } else {
+            g = g + scale_fill_manual(breaks = c("0", "1"), labels = c("Absent", "Present"), values = c("0"=absence.color, "1"=presence.color)) +
+                theme(legend.title=element_blank())
+        }
+
+        if (length(popmap) > 0) {
+            g = g + theme(axis.text.y = element_text(colour=sex_palette[sexes[names(data)[-1]]]))
+        }
+
+        a = ggplotGrob(g)
+        loci_start = 4
+
+        if (loci.dendrogram == TRUE) {
+
+            h = suppressMessages(ggdendrogram(individuals.clusters, labels = FALSE, leaf_labels = FALSE, theme_dendro = TRUE, rotate = FALSE) +
+                                     theme(plot.margin = unit(c(0.1, 0, 0, 0), 'cm'), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x=element_blank()) +
+                                     scale_y_continuous(expand = c(0,0.5)) + scale_x_continuous(expand=c(0,0.5)))
+            b = ggplotGrob(h)
+            a = gtable_add_rows(a, unit(1.1, "cm"), pos=3)
+            a = gtable_add_grob(a, b, t = 3, l=4, b=5, r=5)
+            loci_start = loci_start + 1
+        }
+
+        if (individuals.dendrogram == TRUE) {
+
+            i = suppressMessages(ggdendrogram(loci.clusters, labels = FALSE, leaf_labels = FALSE, theme_dendro = TRUE, rotate = FALSE) +
+                                     theme(plot.margin = unit(c(0.1, 0, 0, 0), 'cm'), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x=element_blank()) +
+                                     coord_flip() + scale_y_continuous(expand = c(0,0.5)) + scale_x_continuous(expand=c(0,0)))
+            c = ggplotGrob(i)
+            a = gtable_add_cols(a, unit(1.1, "cm"), pos=5)
+            a = gtable_add_grob(a, c, t = loci_start, l=5, b=loci_start + 2, r=6)
+        }
+
+        grid.newpage()
+        grid.draw(a)
+    }
+}
+
+
+plot_clustering_clean = function(data, popmap=data.frame(),
+                        distance="binary", clustering="ward.D",
+                        males.color="steelblue3", females.color="orange1",
+                        palette=c("white", "royalblue2", "black", "gold2", "red3")) {
+
+    x = as.matrix(data[,-1], rownames.force = TRUE)
+    rownames(x) =  data$Haplotype_ID
+
+    tile_separation_size = 0.02
+
+    individuals.d = dist(t(x), method=distance)
+    individuals.clusters = hclust(individuals.d, method=clustering)
+    ordered = individuals.clusters$labels[individuals.clusters$order]
+    temp = c(1)
+    for (i in 1:length(ordered)) {
+        temp = c(temp, which(names(data) == ordered[i]))
+    }
+    data = data[,temp]
+
+    loci.d = dist(x, method=distance)
+    loci.clusters = hclust(loci.d, method=clustering)
+    ordered = loci.clusters$labels[loci.clusters$order]
+    temp = c()
+    for (i in 1:length(ordered)) {
+        temp = c(temp, which(data$Haplotype_ID == ordered[i]))
+    }
+    data = data[temp,]
+
+    melted = suppressMessages(melt(data, id.vars = c("Haplotype_ID"), variable.name = "Individuals", value.name = "Values"))
+    melted$Haplotype_ID = factor(melted$Haplotype_ID, levels = data$Haplotype_ID)
+    melted$Individuals = factor(as.character(melted$Individuals), levels = as.character(names(data)[-1]))
+    melted$Values = as.numeric(melted$Values)
+
+    stats = summary(replace(melted$Values, which(melted$Values==0), NA), na.rm=TRUE)
+
+    sexes = c(popmap$Sex)
+    names(sexes) = c(popmap$Individual)
+    sex_palette = c("M"=males.color, "F"=females.color)
+
+    g = ggplot(melted, aes(x=Individuals, y=Haplotype_ID, fill=Values)) +
+        geom_tile(color="grey30", size=tile_separation_size) +
+        theme_bw() +
+        theme(axis.text.x = element_text(angle=90, vjust=0.5, colour=sex_palette[sexes[names(data)[-1]]], size=10),
+              axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.y = element_blank(),
+              axis.title.x = element_blank(), plot.margin = margin(15, 25, 15, 15),
+              panel.border = element_rect(size=0.75, color="black"),
+              legend.position=c(0.85, 0.15), legend.margin = margin(0, 0, 0, 0),
+              legend.title = element_text(size=14, face="bold"), legend.text = element_text(size=11),
+              legend.key.height = unit(0.06, "npc"), legend.key.width = unit(0.04, "npc")) +
+        scale_fill_gradientn(name="Coverage", colours = palette, values = c(0, 0.001, stats[4]/stats[6], stats[5]/stats[6], 1))
+
+    a = ggplotGrob(g)
+
+    h = suppressMessages(ggdendrogram(individuals.clusters, labels = FALSE, leaf_labels = FALSE, theme_dendro = TRUE, rotate = FALSE) +
+                         theme(plot.margin = unit(c(0.1, 0, 0, 0), 'cm'), axis.text.x = element_blank(),
+                         axis.text.y = element_blank(), axis.title.x=element_blank()) +
+                         scale_y_reverse(expand = c(0,0.5)) + scale_x_continuous(expand=c(0,0)))
+
+    a = gtable_add_rows(a, unit(0.06, "npc"), pos=8)
+    a = gtable_add_grob(a, ggplotGrob(h), t = 8, l=4, b=9, r=5)
+    # grid.newpage()
+    # grid.draw(a)
+    return(a)
+}

r_scripts/clustering_species.R

+source("/home/rferon/work/analyses/radseq_all/r_scripts/clustering.R")
+
+species = "esox_masquinongy_1"
+
+coverage_file = paste("~/work/analyses/radseq_all/results/rescue/", species, ".tsv", sep="")
+popmap_file = paste("~/work/analyses/radseq_all/data/popmaps/", species, "_popmap.csv", sep="")
+presence_output_file = paste("~/work/analyses/radseq_all/figures/clustering/", species, "_presence.png", sep="")
+coverage_output_file = paste("~/work/analyses/radseq_all/figures/clustering/", species, "_coverage.png", sep="")
+
+# png(coverage_output_file, width=2400, height=1200, res=100)
+coverage_heatmap(coverage_file, popmap_file, distance="euclidian", clustering="ward.D")
+# dev.off()
+
+# png(presence_output_file, width=2400, height=1200, res=100)
+# presence_heatmap(coverage_file, popmap_file, distance="binary", clustering="ward.D", read.length=96)
+# dev.off()
+

r_scripts/frequencies.R

+source("/home/rferon/work/analyses/radseq_all/r_scripts/utils.R")
+
+
+aggregate_frequencies = function(input_folder, output_file="none", title = "none", species="none") {
+
+    input_folder = paste(sub("/$", "", input_folder))
+    data = parse_folder(input_folder)
+    aggregate_frequencies_plot(data, output_file=output_file, title=title, species=species)
+}
+
+
+
+parse_folder = function(folder) {
+
+    output = list()
+    plot_m_2 = single_plot(folder, 2)
+    plot_m_3 = single_plot(folder, 3)
+    plot_m_5 = single_plot(folder, 5)
+    plot_m_10 = single_plot(folder, 10)
+
+    n_plots = 0
+    if (is.ggplot(plot_m_2)) {n_plots = n_plots + 1}
+    if (is.ggplot(plot_m_3)) {n_plots = n_plots + 1}
+    if (is.ggplot(plot_m_5)) {n_plots = n_plots + 1}
+    if (is.ggplot(plot_m_10)) {n_plots = n_plots + 1}
+
+    output$plot_m_2 = plot_m_2
+    output$plot_m_3 = plot_m_3
+    output$plot_m_5 = plot_m_5
+    output$plot_m_10 = plot_m_10
+    output$n_plots = n_plots
+
+    return(output)
+}
+
+
+
+single_plot = function(folder, m_value) {
+
+    frequencies_file_path = paste(folder, paste("m_", m_value, "_frequencies.tsv", sep=""), sep="/")
+
+    if (file.exists(frequencies_file_path)){
+        g = generate_plot(frequencies_file_path, title = get_title(m_value=m_value))
+    } else {
+        print(paste("Frequencies file", frequencies_file_path, "does not exist.", sep=" "))
+        g = "none"
+    }
+
+    return(g)
+}
+
+
+
+aggregate_frequencies_plot = function(data, output_file="none", title="none", species="none") {
+
+    width_c = 1600
+    height_c = 500
+
+    if (title == "none" & species == "none") {title="Haplotypes frequencies"}
+    title = get_title(species=species, base=title)
+
+    if (output_file != "none") { png(output_file, width=width_c, height=height_c*data$n_plots, res=160) }
+
+    if (is.ggplot(data$plot_m_5)) {
+        if (is.ggplot(data$plot_m_10)) {
+            if (is.ggplot(data$plot_m_3)) {
+                if(is.ggplot(data$plot_m_2)) {
+                    grid.arrange(data$plot_m_2 + theme(legend.position="none"),
+                                 data$plot_m_3 + theme(legend.position="none"),
+                                 data$plot_m_5 + theme(legend.position="none"),
+                                 data$plot_m_10 + theme(legend.position="none"),
+                                 ncol=1, nrow=4, top=textGrob(title, gp=gpar(fontsize=15)),
+                                 bottom="Number of individuals")
+                } else {
+                    grid.arrange(data$plot_m_3 + theme(legend.position="none"),
+                                 data$plot_m_5 + theme(legend.position="none"),
+                                 data$plot_m_10 + theme(legend.position="none"),
+                                 ncol=1, nrow=3, top=textGrob(title, gp=gpar(fontsize=15)),
+                                 bottom="Number of individuals")
+                }
+            } else {
+                if (is.ggplot(data$plot_m_2)) {
+                    grid.arrange(data$plot_m_2 + theme(legend.position="none"),
+                                 data$plot_m_5 + theme(legend.position="none"),
+                                 data$plot_m_10 + theme(legend.position="none"),
+                                 ncol=1, nrow=3, top=textGrob(title, gp=gpar(fontsize=15)),
+                                 bottom="Number of individuals")
+                } else {
+                    grid.arrange(data$plot_m_5 + theme(legend.position="none"),
+                                 data$plot_m_10 + theme(legend.position="none"),
+                                 ncol=1, nrow=2, top=textGrob(title, gp=gpar(fontsize=15)),
+                                 bottom="Number of individuals")
+                }
+            }
+        } else {
+            if (is.ggplot(data$plot_m_3)) {
+                if (is.ggplot(data$plot_m_2)) {
+                    grid.arrange(data$plot_m_2 + theme(legend.position="none"),
+                                 data$plot_m_3 + theme(legend.position="none"),
+                                 data$plot_m_5 + theme(legend.position="none"),
+                                 ncol=1, nrow=3, top=textGrob(title, gp=gpar(fontsize=15)),
+                                 bottom="Number of individuals")
+                } else {
+                    grid.arrange(data$plot_m_3 + theme(legend.position="none"),
+                                 data$plot_m_5 + theme(legend.position="none"),
+                                 ncol=1, nrow=2, top=textGrob(title, gp=gpar(fontsize=15)),
+                                 bottom="Number of individuals")
+                }
+
+            } else {
+                if (is.ggplot(data$plot_m_2)) {
+                    grid.arrange(data$plot_m_2 + theme(legend.position="none"),
+                                 data$plot_m_5 + theme(legend.position="none"),
+                                 ncol=1, nrow=2, top=textGrob(title, gp=gpar(fontsize=15)),
+                                 bottom="Number of individuals")
+                } else {
+                    print(data$plot_m_5)
+                }
+            }
+        }
+    } else {
+        if (is.ggplot(data$plot_m_10)) {
+            if (is.ggplot(data$plot_m_3)) {
+                if (is.ggplot(data$plot_m_2)) {
+                    grid.arrange(data$plot_m_2 + theme(legend.position="none"),
+                                 data$plot_m_3 + theme(legend.position="none"),