R/functions.R
In VincentAlcazer/hemRNA: A package for quick RNAseq data analysis
Defines functions
fast_map
circos_plot
depth_approx
cpm_approx
tpm_approx
Documented in
circos_plot
fast_map
tpm_approx <- function(data,length){
RPK = data / (length/1000)
TPM = RPK/(colSums(RPK)/1e6)
return(TPM)
}
cpm_approx <- function(data){
CPM = (data/colSums(data))* 1e6
return(CPM)
}
depth_approx <- function(data){
CPM = data / colSums(data)
return(CPM)
}
#' Draw final circle plot
#'
circos_plot <- function(expression,cnv,variant, arriba, bed,
SNP_max_rad = 1.145, SNP_min_rad =1,
exp_max_rad = 0.97, exp_min_rad = 0.80,
CNV_max_rad = 0.75, CNV_min_rad = 0.4,
fusion_max_rad = 0.35,
CNV_cutoff = 1.5
){
########## ========== Global parameters
### Set genes coords
gene_order <- gsub("chr","",unique(bed$chr))
genes_coords <- bed %>%
#mutate(chr = factor(chr, levels = unique(bed$chr))) %>%
distinct(gene_name, .keep_all = T) %>%
group_by(chr) %>%
mutate(order = seq(1:n()),
chr = factor(gsub("chr","",chr), levels = gene_order))
### Set custom genome coords
custom_genome <- genes_coords %>%
group_by(chr) %>%
summarise(length = length(unique(gene_name))) %>%
column_to_rownames("chr") %>% t() %>% as.data.frame(check.names = F) %>% as.list()
########## ========== Prepare data
##### ===== Expression
salmon_res <- expression %>%
#select(gene_name, expression = all_of(input$sample)) %>% # DEV TO REMOVE
inner_join(genes_coords, by = "gene_name") %>%
arrange(chr)
##### ===== CNV
cnv_res <- cnv %>% left_join(genes_coords, by = c("gene"="gene_name"),
suffix = c("_cnv","_coords"))
cnv_res_notsig <- cnv_res %>% filter(abs(CNV) < CNV_cutoff)
cnv_res_sig <- cnv_res %>% filter(abs(CNV) >= CNV_cutoff)
##### ===== Mutations
RNAmut_res <- variant %>% filter(Info == "Oncogenic", type == "Variant") %>%
#filter(sample_id == input$sample) %>% # DEV TO REMOVE
arrange(desc(VAF2)) %>%
distinct(gene_mut, .keep_all = T) %>%
left_join(genes_coords, by = c("Gene"="gene_name"))
##### ===== Fusions
fusion_res <- arriba %>% select(-read_identifiers, -fusion_transcript) %>%
#filter(sample_id == input$sample) %>% # DEV TO REMOVE
filter(confidence == "high") %>%
left_join(genes_coords, by = c("gene1"="gene_name")) %>%
left_join(genes_coords, by = c("gene2"="gene_name"), suffix = c("_g1","_g2")) %>%
na.omit()
# separate(breakpoint1, into = c("chr1","linkpos1")) %>%
# separate(breakpoint2, into = c("chr2","linkpos2"))
########## ========== Draw tracks
##### ===== SNP tracks
if(nrow(RNAmut_res) == 0){
tracks = BioCircosTextTrack("text1",
'No variants found',
weight = "normal",
x = -0.2, y = -1.07)
tracks = tracks + BioCircosTextTrack("text2",
'by RNAMut',
weight = "normal",
x = -0.1, y = -1.02)
} else {
tracks = BioCircosArcTrack("RNAmut_variants",
chromosome = as.character(RNAmut_res$chr),
starts = RNAmut_res$order,
ends = RNAmut_res$order+4,
labels = RNAmut_res$gene_mut,
maxRadius = SNP_max_rad, minRadius = SNP_min_rad,
color = "black")
}
##### ===== Expression track
tracks = tracks + BioCircosHeatmapTrack("heatmap1",
as.character(salmon_res$chr),
salmon_res$order-1,
salmon_res$order,
round(salmon_res$expression,2),
labels = salmon_res$gene_name,
minRadius = exp_min_rad,
maxRadius = exp_max_rad)
##### ===== CNV tracks
tracks = tracks + BioCircosCNVTrack("cnv_backm2",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(-2,length(as.character(cnv_res$chromosome))),
color = "black",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_backm1",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(-1,length(as.character(cnv_res$chromosome))),
color = "lightgrey",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_back0",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(0,length(as.character(cnv_res$chromosome))),
color = "grey",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_back1",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(1,length(as.character(cnv_res$chromosome))),
color = "lightgrey",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_back2",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(2,length(as.character(cnv_res$chromosome))),
color = "black",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_notsig",width = 2,
as.character(cnv_res_notsig$chromosome),
cnv_res_notsig$order_coords-1,
cnv_res_notsig$order_coords,
cnv_res_notsig$CNV,
color = "cornflowerblue",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_sig", width = 5,
as.character(cnv_res_sig$chromosome),
cnv_res_sig$order_coords-1,
cnv_res_sig$order_coords,
cnv_res_sig$CNV,
color = "firebrick",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
##### ===== Fusions track
if(nrow(fusion_res) == 0){
tracks = tracks + BioCircosTextTrack("text3",
'No fusions found',
weight = "normal",
x = -0.2, y = 0)
tracks = tracks + BioCircosTextTrack("text4",
'by Arriba',
weight = "normal",
x = -0.10, y = 0.1)
} else {
tracks = tracks + BioCircosLinkTrack("fusions",
gene1Chromosomes = as.character(fusion_res$chr_g1),
gene1Starts = as.numeric(fusion_res$order_g1),
gene1Ends = as.numeric(fusion_res$order_g1)+4,
gene2Chromosomes = as.character(fusion_res$chr_g2),
gene2Starts = as.numeric(fusion_res$order_g2),
gene2Ends = as.numeric(fusion_res$order_g2)+4,
labels = fusion_res$fusion,
displayAxis = F, axisPadding = 6,
color = "firebrick", width = "0.5em",
displayLabel = F,
maxRadius = fusion_max_rad)
}
##### ===== Backgrounds
#
# tracks = tracks + BioCircosBackgroundTrack("BG_SNP",
# borderColors = "black",
# fillColor = "white", borderSize = 0.6,
# minRadius = CNV_max_rad/2, maxRadius = CNV_max_rad)
# tracks = tracks + BioCircosBackgroundTrack("BG_SNP_2",
# borderColors = "black",
# fillColor = "white", borderSize = 0.6,
# minRadius = CNV_min_rad, maxRadius = CNV_max_rad/2)
#
##### ===== Final viz
p <- BioCircos(tracks,
genome = custom_genome,
genomeFillColor = "Spectral",
yChr = F,
chrPad = 0,
displayGenomeBorder = F,
genomeTicksLen = 3,
genomeTicksTextSize = 0,
genomeTicksScale = 50000000,
genomeLabelTextSize = 18,
genomeLabelDy = 0)
return(p)
}
#' Input = df with samples as rownames and features as cols
#' Annotation = vector of annotation (in the same order as input's rownames!!)
#' anno_pal = viridis_pal, hue_pal, grey_pal...
#'
fast_map <- function(df, center_scale = T,
heat_colors=colorRampPalette(c('#3361A5','#248AF3','#14B3FF','#88CEEF','#C1D5DC',
'#EAD397','#FDB31A','#E42A2A','#A31D1D'))(100),
anno_pal = viridis_pal(),
Zlim = 3, method = "ward.D2", metric = "euclidean", title = "title",
Data_input = "Z-score expression",
anno_1 = NULL, anno_1_name = NULL, anno_1_cols = NULL,
cluster_rows = T, cluster_cols = T,
reorder_rows = T, reorder_cols = T,
row_names = T,col_names=T,
row_fontsize = 12, col_fontsize = 12){
#### Parameters
message("Parameters")
heat_col <- circlize::colorRamp2(seq(-Zlim, +Zlim, by =(2*Zlim)/99),heat_colors)
sup_leg = list(legend_1 = Legend(col_fun = heat_col,
title = Data_input,
labels_gp = gpar(fontsize = 12),
direction = c("horizontal"),
title_gp = gpar(fontsize = 12, fontface="bold"))
)
message("Anno if ")
if(is.null(anno_1)){
sup_ha = NULL
} else {
if(is.numeric(anno_1) | is.double(anno_1)){
##### ===== 1 unique continuous annotation
col_list <- list(supha_1 = circlize::colorRamp2(c(min(anno_1, na.rm=T),
median(anno_1,na.rm=T),
max(anno_1,na.rm=T)),
c(anno_pal(100)[1],
anno_pal(100)[50],
anno_pal(100)[100])))
sup_ha = HeatmapAnnotation(supha_1 = anno_1,
show_annotation_name = F,
col = col_list,
show_legend = F,
na_col = "grey")
sup_leg$legend_2 = Legend(col_fun = col_list$supha_1,
title=anno_1_name,
direction = c("horizontal"), ncol=2,
labels_gp = gpar(fontsize = 12),
title_gp = gpar(fontsize = 12, fontface="bold"))
} else {
##### ===== 1 unique categorical annotation
ha_1 <- anno_pal(length(unique(na.omit(anno_1))))
names(ha_1) <- unique(na.omit(anno_1))
col_list <- list(supha_1 = ha_1)
sup_ha = HeatmapAnnotation(supha_1 = anno_1,
show_annotation_name = F,
col = col_list,
show_legend = F)
sup_leg$legend_2 = Legend(labels=names(col_list[[1]]),
title=anno_1_name,
direction = c("horizontal"), ncol=2,
labels_gp = gpar(fontsize = 12),
title_gp = gpar(fontsize = 12, fontface="bold"),
legend_gp = gpar(fill = as.vector(col_list[[1]])))
}
}
##### ===== Prepare df
message("Prepare df ")
matrix <- df
if(center_scale == T){
matrix <- scale(matrix)
}
message("set heatmap ")
ht <- ComplexHeatmap::Heatmap(t(matrix),
name = "heat",
col = heat_col,
show_column_names = col_names,
show_row_names = row_names,
#column_split = k,
cluster_columns = cluster_cols,
show_column_dend = cluster_cols,
column_dend_reorder = reorder_cols,
clustering_distance_columns = metric,
clustering_method_column = method,
show_heatmap_legend = F,
show_row_dend = cluster_rows,
cluster_rows = cluster_rows,
row_dend_reorder = reorder_rows,
clustering_distance_rows = metric,
clustering_method_rows = method,
use_raster = TRUE,
raster_device = c("png"),
raster_quality = 2,
top_annotation = sup_ha,
column_title = title,
column_title_gp = gpar(fontsize = 14, fontface = "bold"),
row_title_gp = gpar(fontsize = 12),
row_names_gp = gpar(fontsize = row_fontsize),
column_names_gp = gpar(fontsize = col_fontsize)
)
### draw
message("Plot heatmap")
draw(ht, annotation_legend_list = sup_leg, annotation_legend_side = "bottom", newpage = T)
}
VincentAlcazer/hemRNA documentation built on Aug. 26, 2022, 4:50 a.m.
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Defines functions fast_map circos_plot depth_approx cpm_approx tpm_approx
Documented in circos_plot fast_map
tpm_approx <- function(data,length){
RPK = data / (length/1000)
TPM = RPK/(colSums(RPK)/1e6)
return(TPM)
}
cpm_approx <- function(data){
CPM = (data/colSums(data))* 1e6
return(CPM)
}
depth_approx <- function(data){
CPM = data / colSums(data)
return(CPM)
}
#' Draw final circle plot
#'
circos_plot <- function(expression,cnv,variant, arriba, bed,
SNP_max_rad = 1.145, SNP_min_rad =1,
exp_max_rad = 0.97, exp_min_rad = 0.80,
CNV_max_rad = 0.75, CNV_min_rad = 0.4,
fusion_max_rad = 0.35,
CNV_cutoff = 1.5
){
########## ========== Global parameters
### Set genes coords
gene_order <- gsub("chr","",unique(bed$chr))
genes_coords <- bed %>%
#mutate(chr = factor(chr, levels = unique(bed$chr))) %>%
distinct(gene_name, .keep_all = T) %>%
group_by(chr) %>%
mutate(order = seq(1:n()),
chr = factor(gsub("chr","",chr), levels = gene_order))
### Set custom genome coords
custom_genome <- genes_coords %>%
group_by(chr) %>%
summarise(length = length(unique(gene_name))) %>%
column_to_rownames("chr") %>% t() %>% as.data.frame(check.names = F) %>% as.list()
########## ========== Prepare data
##### ===== Expression
salmon_res <- expression %>%
#select(gene_name, expression = all_of(input$sample)) %>% # DEV TO REMOVE
inner_join(genes_coords, by = "gene_name") %>%
arrange(chr)
##### ===== CNV
cnv_res <- cnv %>% left_join(genes_coords, by = c("gene"="gene_name"),
suffix = c("_cnv","_coords"))
cnv_res_notsig <- cnv_res %>% filter(abs(CNV) < CNV_cutoff)
cnv_res_sig <- cnv_res %>% filter(abs(CNV) >= CNV_cutoff)
##### ===== Mutations
RNAmut_res <- variant %>% filter(Info == "Oncogenic", type == "Variant") %>%
#filter(sample_id == input$sample) %>% # DEV TO REMOVE
arrange(desc(VAF2)) %>%
distinct(gene_mut, .keep_all = T) %>%
left_join(genes_coords, by = c("Gene"="gene_name"))
##### ===== Fusions
fusion_res <- arriba %>% select(-read_identifiers, -fusion_transcript) %>%
#filter(sample_id == input$sample) %>% # DEV TO REMOVE
filter(confidence == "high") %>%
left_join(genes_coords, by = c("gene1"="gene_name")) %>%
left_join(genes_coords, by = c("gene2"="gene_name"), suffix = c("_g1","_g2")) %>%
na.omit()
# separate(breakpoint1, into = c("chr1","linkpos1")) %>%
# separate(breakpoint2, into = c("chr2","linkpos2"))
########## ========== Draw tracks
##### ===== SNP tracks
if(nrow(RNAmut_res) == 0){
tracks = BioCircosTextTrack("text1",
'No variants found',
weight = "normal",
x = -0.2, y = -1.07)
tracks = tracks + BioCircosTextTrack("text2",
'by RNAMut',
weight = "normal",
x = -0.1, y = -1.02)
} else {
tracks = BioCircosArcTrack("RNAmut_variants",
chromosome = as.character(RNAmut_res$chr),
starts = RNAmut_res$order,
ends = RNAmut_res$order+4,
labels = RNAmut_res$gene_mut,
maxRadius = SNP_max_rad, minRadius = SNP_min_rad,
color = "black")
}
##### ===== Expression track
tracks = tracks + BioCircosHeatmapTrack("heatmap1",
as.character(salmon_res$chr),
salmon_res$order-1,
salmon_res$order,
round(salmon_res$expression,2),
labels = salmon_res$gene_name,
minRadius = exp_min_rad,
maxRadius = exp_max_rad)
##### ===== CNV tracks
tracks = tracks + BioCircosCNVTrack("cnv_backm2",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(-2,length(as.character(cnv_res$chromosome))),
color = "black",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_backm1",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(-1,length(as.character(cnv_res$chromosome))),
color = "lightgrey",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_back0",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(0,length(as.character(cnv_res$chromosome))),
color = "grey",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_back1",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(1,length(as.character(cnv_res$chromosome))),
color = "lightgrey",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_back2",
as.character(cnv_res$chromosome),
cnv_res$order_coords-1,
cnv_res$order_coords,
rep(2,length(as.character(cnv_res$chromosome))),
color = "black",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_notsig",width = 2,
as.character(cnv_res_notsig$chromosome),
cnv_res_notsig$order_coords-1,
cnv_res_notsig$order_coords,
cnv_res_notsig$CNV,
color = "cornflowerblue",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
tracks = tracks + BioCircosCNVTrack("cnv_sig", width = 5,
as.character(cnv_res_sig$chromosome),
cnv_res_sig$order_coords-1,
cnv_res_sig$order_coords,
cnv_res_sig$CNV,
color = "firebrick",
range = c(-2,2),
minRadius = CNV_min_rad,
maxRadius = CNV_max_rad)
##### ===== Fusions track
if(nrow(fusion_res) == 0){
tracks = tracks + BioCircosTextTrack("text3",
'No fusions found',
weight = "normal",
x = -0.2, y = 0)
tracks = tracks + BioCircosTextTrack("text4",
'by Arriba',
weight = "normal",
x = -0.10, y = 0.1)
} else {
tracks = tracks + BioCircosLinkTrack("fusions",
gene1Chromosomes = as.character(fusion_res$chr_g1),
gene1Starts = as.numeric(fusion_res$order_g1),
gene1Ends = as.numeric(fusion_res$order_g1)+4,
gene2Chromosomes = as.character(fusion_res$chr_g2),
gene2Starts = as.numeric(fusion_res$order_g2),
gene2Ends = as.numeric(fusion_res$order_g2)+4,
labels = fusion_res$fusion,
displayAxis = F, axisPadding = 6,
color = "firebrick", width = "0.5em",
displayLabel = F,
maxRadius = fusion_max_rad)
}
##### ===== Backgrounds
#
# tracks = tracks + BioCircosBackgroundTrack("BG_SNP",
# borderColors = "black",
# fillColor = "white", borderSize = 0.6,
# minRadius = CNV_max_rad/2, maxRadius = CNV_max_rad)
# tracks = tracks + BioCircosBackgroundTrack("BG_SNP_2",
# borderColors = "black",
# fillColor = "white", borderSize = 0.6,
# minRadius = CNV_min_rad, maxRadius = CNV_max_rad/2)
#
##### ===== Final viz
p <- BioCircos(tracks,
genome = custom_genome,
genomeFillColor = "Spectral",
yChr = F,
chrPad = 0,
displayGenomeBorder = F,
genomeTicksLen = 3,
genomeTicksTextSize = 0,
genomeTicksScale = 50000000,
genomeLabelTextSize = 18,
genomeLabelDy = 0)
return(p)
}
#' Input = df with samples as rownames and features as cols
#' Annotation = vector of annotation (in the same order as input's rownames!!)
#' anno_pal = viridis_pal, hue_pal, grey_pal...
#'
fast_map <- function(df, center_scale = T,
heat_colors=colorRampPalette(c('#3361A5','#248AF3','#14B3FF','#88CEEF','#C1D5DC',
'#EAD397','#FDB31A','#E42A2A','#A31D1D'))(100),
anno_pal = viridis_pal(),
Zlim = 3, method = "ward.D2", metric = "euclidean", title = "title",
Data_input = "Z-score expression",
anno_1 = NULL, anno_1_name = NULL, anno_1_cols = NULL,
cluster_rows = T, cluster_cols = T,
reorder_rows = T, reorder_cols = T,
row_names = T,col_names=T,
row_fontsize = 12, col_fontsize = 12){
#### Parameters
message("Parameters")
heat_col <- circlize::colorRamp2(seq(-Zlim, +Zlim, by =(2*Zlim)/99),heat_colors)
sup_leg = list(legend_1 = Legend(col_fun = heat_col,
title = Data_input,
labels_gp = gpar(fontsize = 12),
direction = c("horizontal"),
title_gp = gpar(fontsize = 12, fontface="bold"))
)
message("Anno if ")
if(is.null(anno_1)){
sup_ha = NULL
} else {
if(is.numeric(anno_1) | is.double(anno_1)){
##### ===== 1 unique continuous annotation
col_list <- list(supha_1 = circlize::colorRamp2(c(min(anno_1, na.rm=T),
median(anno_1,na.rm=T),
max(anno_1,na.rm=T)),
c(anno_pal(100)[1],
anno_pal(100)[50],
anno_pal(100)[100])))
sup_ha = HeatmapAnnotation(supha_1 = anno_1,
show_annotation_name = F,
col = col_list,
show_legend = F,
na_col = "grey")
sup_leg$legend_2 = Legend(col_fun = col_list$supha_1,
title=anno_1_name,
direction = c("horizontal"), ncol=2,
labels_gp = gpar(fontsize = 12),
title_gp = gpar(fontsize = 12, fontface="bold"))
} else {
##### ===== 1 unique categorical annotation
ha_1 <- anno_pal(length(unique(na.omit(anno_1))))
names(ha_1) <- unique(na.omit(anno_1))
col_list <- list(supha_1 = ha_1)
sup_ha = HeatmapAnnotation(supha_1 = anno_1,
show_annotation_name = F,
col = col_list,
show_legend = F)
sup_leg$legend_2 = Legend(labels=names(col_list[[1]]),
title=anno_1_name,
direction = c("horizontal"), ncol=2,
labels_gp = gpar(fontsize = 12),
title_gp = gpar(fontsize = 12, fontface="bold"),
legend_gp = gpar(fill = as.vector(col_list[[1]])))
}
}
##### ===== Prepare df
message("Prepare df ")
matrix <- df
if(center_scale == T){
matrix <- scale(matrix)
}
message("set heatmap ")
ht <- ComplexHeatmap::Heatmap(t(matrix),
name = "heat",
col = heat_col,
show_column_names = col_names,
show_row_names = row_names,
#column_split = k,
cluster_columns = cluster_cols,
show_column_dend = cluster_cols,
column_dend_reorder = reorder_cols,
clustering_distance_columns = metric,
clustering_method_column = method,
show_heatmap_legend = F,
show_row_dend = cluster_rows,
cluster_rows = cluster_rows,
row_dend_reorder = reorder_rows,
clustering_distance_rows = metric,
clustering_method_rows = method,
use_raster = TRUE,
raster_device = c("png"),
raster_quality = 2,
top_annotation = sup_ha,
column_title = title,
column_title_gp = gpar(fontsize = 14, fontface = "bold"),
row_title_gp = gpar(fontsize = 12),
row_names_gp = gpar(fontsize = row_fontsize),
column_names_gp = gpar(fontsize = col_fontsize)
)
### draw
message("Plot heatmap")
draw(ht, annotation_legend_list = sup_leg, annotation_legend_side = "bottom", newpage = T)
}
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