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style(scripts): apply Air formatter
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‎01_PanPhylo_analysis/02_pangenome_visualization.R‎

Lines changed: 51 additions & 35 deletions
Original file line numberDiff line numberDiff line change
@@ -9,13 +9,16 @@ setwd(main_dir)
99

1010
# Load/install required packages
1111

12-
if (!require("pacman"))
12+
if (!require("pacman")) {
1313
install.packages("pacman")
14+
}
1415

1516
pacman::p_load(ggplot2, dplyr, tidyr, ggnewscale, ggrepel, patchwork)
1617

1718
# Read the gene_countsset
18-
pangenome_df <- read.csv("pangenome/Pangenome/PanGenome-LeMy.All.prt-clust-0.9-mode1.lst.summary.txt")
19+
pangenome_df <- read.csv(
20+
"pangenome/Pangenome/PanGenome-LeMy.All.prt-clust-0.9-mode1.lst.summary.txt"
21+
)
1922

2023
#######################
2124
# Part 1: Donut chart #
@@ -26,10 +29,14 @@ pangenome_df_1 <- pangenome_df %>%
2629
mutate(
2730
Core = ifelse(nb_members == 24, "Core", NA),
2831
# Core genes (present in all genomes)
29-
Shared = ifelse(nb_members > 1 &
30-
nb_members < 24, "Shell", NA),
32+
Shared = ifelse(
33+
nb_members > 1 &
34+
nb_members < 24,
35+
"Shell",
36+
NA
37+
),
3138
# Shared genes (present in some genomes)
32-
Unique = ifelse(nb_members == 1, "Cloud", NA) # Unique genes (present in only one genome)
39+
Unique = ifelse(nb_members == 1, "Cloud", NA) # Unique genes (present in only one genome)
3340
)
3441

3542
# Count the number of genes in each category
@@ -51,12 +58,14 @@ gene_counts$ymin = c(0, head(gene_counts$ymax, n = -1))
5158
gene_counts$labelPosition <- (gene_counts$ymax + gene_counts$ymin) / 2
5259

5360
# Compute a good label
54-
gene_counts$label <- paste0(gene_counts$Type,
55-
"\n value: ",
56-
gene_counts$n,
57-
"\n",
58-
round((gene_counts$fraction * 100), 2),
59-
"%")
61+
gene_counts$label <- paste0(
62+
gene_counts$Type,
63+
"\n value: ",
64+
gene_counts$n,
65+
"\n",
66+
round((gene_counts$fraction * 100), 2),
67+
"%"
68+
)
6069

6170
colors <- c(
6271
"Core" = "#d9e7f1",
@@ -65,18 +74,22 @@ colors <- c(
6574
)
6675

6776
# Make the plot
68-
pangenome_donut <- ggplot(gene_counts,
69-
aes(
70-
ymax = ymax,
71-
ymin = ymin,
72-
xmax = 4,
73-
xmin = 3,
74-
fill = Type
75-
)) +
77+
pangenome_donut <- ggplot(
78+
gene_counts,
79+
aes(
80+
ymax = ymax,
81+
ymin = ymin,
82+
xmax = 4,
83+
xmin = 3,
84+
fill = Type
85+
)
86+
) +
7687
geom_rect() +
77-
geom_text(x = 1.5,
78-
aes(y = labelPosition, label = label, color = Type),
79-
size = 6) + # x here controls label position (inner / outer)
88+
geom_text(
89+
x = 1.5,
90+
aes(y = labelPosition, label = label, color = Type),
91+
size = 6
92+
) + # x here controls label position (inner / outer)
8093
scale_fill_manual(values = colors) +
8194
scale_color_manual(values = colors) +
8295
coord_polar(theta = "y") +
@@ -98,7 +111,7 @@ ggsave(
98111

99112
# Create the base plot
100113
pangenome_scatter <- ggplot(pangenome_df, aes(x = num_fam, y = nb_members)) +
101-
114+
102115
# Highlight cloud genome
103116
geom_point(
104117
data = pangenome_df[pangenome_df$nb_members == 1, ],
@@ -109,18 +122,20 @@ pangenome_scatter <- ggplot(pangenome_df, aes(x = num_fam, y = nb_members)) +
109122
shape = 19,
110123
stroke = 2
111124
) +
112-
125+
113126
# Highlight shell genome
114127
geom_point(
115-
data = pangenome_df[pangenome_df$nb_members > 1 & pangenome_df$nb_members < 16, ],
128+
data = pangenome_df[
129+
pangenome_df$nb_members > 1 & pangenome_df$nb_members < 16,
130+
],
116131
aes(x = num_fam, y = nb_members),
117132
color = "#743f96",
118133
alpha = 0.75,
119134
size = 1,
120135
shape = 19,
121136
stroke = 2
122137
) +
123-
138+
124139
# Highlight gene family 314
125140
geom_point(
126141
data = pangenome_df[pangenome_df$nb_members >= 16, ],
@@ -131,7 +146,7 @@ pangenome_scatter <- ggplot(pangenome_df, aes(x = num_fam, y = nb_members)) +
131146
shape = 21,
132147
stroke = 2
133148
) +
134-
149+
135150
geom_text_repel(
136151
data = pangenome_df[pangenome_df$nb_members >= 16, ],
137152
aes(
@@ -144,24 +159,24 @@ pangenome_scatter <- ggplot(pangenome_df, aes(x = num_fam, y = nb_members)) +
144159
nudge_x = c(0, 0, 0, 0),
145160
nudge_y = c(1, -1, 1, 1)
146161
) +
147-
162+
148163
# Add a horizontal dashed line at y = 70
149164
geom_hline(
150165
yintercept = 15,
151166
color = "black",
152167
linetype = "dashed",
153168
alpha = 0.5
154169
) +
155-
170+
156171
# Customize axis labels and breaks
157-
scale_x_continuous(breaks = seq(0, 614, by = 75)) + # Display every x-axis label
158-
scale_y_continuous(breaks = seq(0, 24, by = 5)) + # Display y-axis labels every 10 units
159-
172+
scale_x_continuous(breaks = seq(0, 614, by = 75)) + # Display every x-axis label
173+
scale_y_continuous(breaks = seq(0, 24, by = 5)) + # Display y-axis labels every 10 units
174+
160175
# Remove title and legend
161176
theme_minimal() +
162177
theme(legend.position = "none") +
163178
labs(x = "Gene Family ID", y = "Number of Genomes Sharing the Gene") +
164-
179+
165180
# Ensure everything fits into the plot area
166181
theme(plot.margin = unit(c(1, 1, 1, 1), "cm"))
167182

@@ -177,11 +192,12 @@ ggsave(
177192
###### COMBINED ######
178193
######################
179194

180-
everything <- (pangenome_donut + pangenome_scatter) + plot_annotation(tag_levels = list(c("A", "B")))
195+
everything <- (pangenome_donut + pangenome_scatter) +
196+
plot_annotation(tag_levels = list(c("A", "B")))
181197
ggsave(
182198
"imgs/combined_pangenome.png",
183199
plot = everything,
184200
width = 18,
185201
height = 8,
186202
dpi = 600
187-
)
203+
)

‎01_PanPhylo_analysis/04_ggtree_journal.R‎

Lines changed: 61 additions & 34 deletions
Original file line numberDiff line numberDiff line change
@@ -7,8 +7,9 @@ setwd(main_dir)
77

88
#install.packages('BiocManager')
99

10-
if (!require("pacman"))
10+
if (!require("pacman")) {
1111
install.packages("pacman")
12+
}
1213

1314
pacman::p_load(
1415
ggtree,
@@ -52,7 +53,9 @@ colnames(meta.loc) <- 'Country'
5253
rownames(meta.loc) <- metadata$Name
5354
meta.loc$Country[meta.loc$Country == "ND"] <- NA
5455
meta.loc$Country[meta.loc$Country == "USA: New York, Williams Hotel"] <- "USA"
55-
meta.loc$Country[meta.loc$Country == "Switzerland: Boedmerenwald"] <- "Switzerland"
56+
meta.loc$Country[
57+
meta.loc$Country == "Switzerland: Boedmerenwald"
58+
] <- "Switzerland"
5659
colnames(meta.loc) <- c("Location")
5760

5861

@@ -70,7 +73,8 @@ midpoint.root(nad4l_tree)
7073

7174
# Draft tree
7275

73-
nad4l_tree_fig <- ggtree(nad4l_tree) %<+% metadata +
76+
nad4l_tree_fig <- ggtree(nad4l_tree) %<+%
77+
metadata +
7478
xlim(0, 0.1) +
7579
geom_hilight(
7680
mapping = aes(subset = node %in% c(37), fill = S),
@@ -102,11 +106,14 @@ nad4l_tree_boot$bootstrap[is.na(nad4l_tree_boot$label)] <- '1'
102106

103107
# Add bootstrap values to the tree (black branches = bootstrap >70; grey branches = bootstrap <70)
104108

105-
nad4l_tree_fig <- nad4l_tree_fig + new_scale_color() +
109+
nad4l_tree_fig <- nad4l_tree_fig +
110+
new_scale_color() +
106111
geom_tree(data = nad4l_tree_boot, aes(color = bootstrap == '1')) +
107-
scale_color_manual(name = 'Bootstrap',
108-
values = setNames(c("black", "grey"), c(T, F)),
109-
guide = "none")
112+
scale_color_manual(
113+
name = 'Bootstrap',
114+
values = setNames(c("black", "grey"), c(T, F)),
115+
guide = "none"
116+
)
110117

111118
ggsave(
112119
'imgs/nad4l.png',
@@ -130,7 +137,8 @@ midpoint.root(cox2_tree)
130137

131138
# Draft tree
132139

133-
cox2_tree_fig <- ggtree(cox2_tree) %<+% metadata +
140+
cox2_tree_fig <- ggtree(cox2_tree) %<+%
141+
metadata +
134142
xlim(0, 0.1) +
135143
geom_hilight(
136144
mapping = aes(subset = node %in% c(31), fill = S),
@@ -162,11 +170,14 @@ cox2_tree_boot$bootstrap[is.na(cox2_tree_boot$label)] <- '1'
162170

163171
# Add bootstrap values to the tree (black branches = bootstrap >70; grey branches = bootstrap <70)
164172

165-
cox2_tree_fig <- cox2_tree_fig + new_scale_color() +
173+
cox2_tree_fig <- cox2_tree_fig +
174+
new_scale_color() +
166175
geom_tree(data = cox2_tree_boot, aes(color = bootstrap == '1')) +
167-
scale_color_manual(name = 'Bootstrap',
168-
values = setNames(c("black", "grey"), c(T, F)),
169-
guide = "none")
176+
scale_color_manual(
177+
name = 'Bootstrap',
178+
values = setNames(c("black", "grey"), c(T, F)),
179+
guide = "none"
180+
)
170181

171182
ggsave(
172183
'imgs/cox2.png',
@@ -190,7 +201,8 @@ midpoint.root(cob_tree)
190201

191202
# Draft tree
192203

193-
cob_tree_fig <- ggtree(cob_tree) %<+% metadata +
204+
cob_tree_fig <- ggtree(cob_tree) %<+%
205+
metadata +
194206
xlim(0, 0.1) +
195207
geom_hilight(
196208
mapping = aes(subset = node %in% c(29), fill = S),
@@ -222,11 +234,14 @@ cob_tree_boot$bootstrap[is.na(cob_tree_boot$label)] <- '1'
222234

223235
# Add bootstrap values to the tree (black branches = bootstrap >70; grey branches = bootstrap <70)
224236

225-
cob_tree_fig <- cob_tree_fig + new_scale_color() +
237+
cob_tree_fig <- cob_tree_fig +
238+
new_scale_color() +
226239
geom_tree(data = cob_tree_boot, aes(color = bootstrap == '1')) +
227-
scale_color_manual(name = 'Bootstrap',
228-
values = setNames(c("black", "grey"), c(T, F)),
229-
guide = "none")
240+
scale_color_manual(
241+
name = 'Bootstrap',
242+
values = setNames(c("black", "grey"), c(T, F)),
243+
guide = "none"
244+
)
230245

231246
ggsave(
232247
'imgs/cob.png',
@@ -250,7 +265,8 @@ midpoint.root(cox1_tree)
250265

251266
# Draft tree
252267

253-
cox1_tree_fig <- ggtree(cox1_tree) %<+% metadata +
268+
cox1_tree_fig <- ggtree(cox1_tree) %<+%
269+
metadata +
254270
xlim(0, 0.2) +
255271
geom_hilight(
256272
mapping = aes(subset = node %in% c(28), fill = S),
@@ -282,11 +298,14 @@ cox1_tree_boot$bootstrap[is.na(cox1_tree_boot$label)] <- '1'
282298

283299
# Add bootstrap values to the tree (black branches = bootstrap >70; grey branches = bootstrap <70)
284300

285-
cox1_tree_fig <- cox1_tree_fig + new_scale_color() +
301+
cox1_tree_fig <- cox1_tree_fig +
302+
new_scale_color() +
286303
geom_tree(data = cox1_tree_boot, aes(color = bootstrap == '1')) +
287-
scale_color_manual(name = 'Bootstrap',
288-
values = setNames(c("black", "grey"), c(T, F)),
289-
guide = "none")
304+
scale_color_manual(
305+
name = 'Bootstrap',
306+
values = setNames(c("black", "grey"), c(T, F)),
307+
guide = "none"
308+
)
290309

291310
ggsave(
292311
'imgs/cox1.png',
@@ -310,7 +329,8 @@ midpoint.root(ALL_tree)
310329

311330
# Draft tree
312331

313-
ALL_tree_fig <- ggtree(ALL_tree) %<+% metadata +
332+
ALL_tree_fig <- ggtree(ALL_tree) %<+%
333+
metadata +
314334
xlim(0, 0.3) +
315335
geom_hilight(
316336
mapping = aes(subset = node %in% c(29), fill = S),
@@ -342,11 +362,14 @@ ALL_tree_boot$bootstrap[is.na(ALL_tree_boot$label)] <- '1'
342362

343363
# Add bootstrap values to the tree (black branches = bootstrap >70; grey branches = bootstrap <70)
344364

345-
ALL_tree_fig <- ALL_tree_fig + new_scale_color() +
365+
ALL_tree_fig <- ALL_tree_fig +
366+
new_scale_color() +
346367
geom_tree(data = ALL_tree_boot, aes(color = bootstrap == '1')) +
347-
scale_color_manual(name = 'Bootstrap',
348-
values = setNames(c("black", "grey"), c(T, F)),
349-
guide = "none")
368+
scale_color_manual(
369+
name = 'Bootstrap',
370+
values = setNames(c("black", "grey"), c(T, F)),
371+
guide = "none"
372+
)
350373

351374
ggsave(
352375
'imgs/ALL.png',
@@ -360,7 +383,9 @@ ggsave(
360383
###### COMBINED ######
361384
######################
362385

363-
conserved <- (nad4l_tree_fig + cox2_tree_fig) / (cob_tree_fig + cox1_tree_fig) + plot_annotation(tag_levels = list(c("A", "B", "C", "D")))
386+
conserved <- (nad4l_tree_fig + cox2_tree_fig) /
387+
(cob_tree_fig + cox1_tree_fig) +
388+
plot_annotation(tag_levels = list(c("A", "B", "C", "D")))
364389
ggsave(
365390
"imgs/conserved_tree.png",
366391
plot = conserved,
@@ -369,13 +394,15 @@ ggsave(
369394
dpi = 600
370395
)
371396

372-
everything <- ALL_tree_fig + (nad4l_tree_fig + cox2_tree_fig) / (cob_tree_fig + cox1_tree_fig) + plot_annotation(tag_levels = list(c("A", "B", "C", "D", "E")))
397+
everything <- ALL_tree_fig +
398+
(nad4l_tree_fig + cox2_tree_fig) / (cob_tree_fig + cox1_tree_fig) +
399+
plot_annotation(tag_levels = list(c("A", "B", "C", "D", "E")))
373400

374401
everything <- ALL_tree_fig +
375402
(nad4l_tree_fig + cox2_tree_fig) /
376-
(cob_tree_fig + cox1_tree_fig) +
403+
(cob_tree_fig + cox1_tree_fig) +
377404
plot_annotation(tag_levels = list(c("A", "B", "C", "D", "E"))) +
378-
plot_layout(heights = c(12, 1), widths = c(0.6, 1.6)) # Make second row wider
405+
plot_layout(heights = c(12, 1), widths = c(0.6, 1.6)) # Make second row wider
379406

380407
ggsave(
381408
"imgs/everything_tree.png",
@@ -385,15 +412,15 @@ ggsave(
385412
dpi = 600
386413
)
387414

388-
everything_v2 <- (nad4l_tree_fig + cox2_tree_fig) /
415+
everything_v2 <- (nad4l_tree_fig + cox2_tree_fig) /
389416
(cob_tree_fig + cox1_tree_fig) +
390417
plot_annotation(tag_levels = list(c("A", "B", "C", "D"))) #+
391-
#plot_layout(heights = c(12, 1), widths = c(0.6, 1.6)) # Make second row wider
418+
#plot_layout(heights = c(12, 1), widths = c(0.6, 1.6)) # Make second row wider
392419

393420
ggsave(
394421
"imgs/everything_v2_tree.png",
395422
plot = everything_v2,
396423
width = 28,
397424
height = 16,
398425
dpi = 600
399-
)
426+
)

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