This repository has been archived by the owner on Jun 23, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Eckert_et_al_2023_RScript_Figure2.R
344 lines (332 loc) · 15.6 KB
/
Eckert_et_al_2023_RScript_Figure2.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
# PACKAGES ====================================================================
library(here)
library(dplyr)
library(readr)
library(ggplot2)
library(cowplot)
library(tibble)
library(performance)
library(fitdistrplus)
library(bestNormalize)
library(car)
library(agricolae)
library(mosaic)
# INFO ========================================================================
# This R script is part of the statistical analysis published in
# Eckert et al. (2023) in the scientific journal Metabolomics
# To load the corresponding raw data use the following R code:
# install.packages(zen4R)
# library(zen4R)
# zen4R::download_zenodo(doi = "10.5281/zenodo.7687634",
# path = here(".")) # add target directory to save files
# # unzip downloaded files
# utils::unzip("Eckert_et_al_2023_RawData.zip")
# DATA ========================================================================
pheno_data <- read_table(here(".", # replace with working directory
"Eckert_et_al_2023_RawData_PhenotypicData.txt"),
na="NA",
col_types=cols(unique_ID="c",
donor_lab="f",
chemotype="f",
plant_ID="i",
plant_height_cm="d",
expanded_leaves_n="d",
FW_shoot_g="d",
FW_leaf_g="d",
FW_biomass_g="d")); pheno_data # 50 x 9
# STATISTICS ==================================================================
## PLANT HEIGHT ===============================================================
# check distribution of response variable using Cullen-Frey graph
descdist(pheno_data$plant_height_cm, discrete=F)
# code interaction explicitly for later post-hoc testing
pheno_data$dlab_chemotype <- with(pheno_data, interaction(donor_lab, chemotype))
# run linear model
height_mod <- lm(plant_height_cm ~ donor_lab +
chemotype +
donor_lab:chemotype,
data=pheno_data,
contrasts=list(donor_lab=contr.sum,
chemotype=contr.sum)); height_mod
# check assumptions visually
check_model(height_mod)
# calculate type-III analysis-of-variance table
height_mod_sum <- Anova(height_mod, type=3); height_mod_sum
# post-hoc testing
set.seed(123); height_mod.HSD <- HSD.test(update(height_mod, .~.
-donor_lab:chemotype +
dlab_chemotype),
"dlab_chemotype"); height_mod.HSD
## EXPANDED LEAVES ============================================================
# check distribution of response variable using Cullen-Frey graph
descdist(pheno_data$expanded_leaves_n, discrete=T)
# normalize response
set.seed(123); leaves_norm <- bestNormalize(pheno_data$expanded_leaves_n); leaves_norm
pheno_data$expanded_leaves_ORDERNORM <- leaves_norm$x.t
# run linear model
leaves_mod <- lm(expanded_leaves_ORDERNORM ~ donor_lab+chemotype,
data=pheno_data,
contrasts=list(donor_lab=contr.sum,
chemotype=contr.sum)); leaves_mod
# check assumptions visually
check_model(leaves_mod)
# calculate type-II analysis-of-variance table
leaves_mod_sum <- Anova(leaves_mod, type=2); leaves_mod_sum
# post-hoc testing
set.seed(123); leaves_mod.HSD <- HSD.test(leaves_mod, "donor_lab"); leaves_mod.HSD
## FW BIOMASS =================================================================
# check distribution of response variable using Cullen-Frey graph
descdist(pheno_data$FW_biomass_g, discrete=F)
# run linear model
FW_shoot_mod <- lm(FW_biomass_g ~ donor_lab+chemotype,
data=pheno_data,
contrasts=list(donor_lab=contr.sum,
chemotype=contr.sum)); FW_shoot_mod
# check assumptions visually
check_model(FW_shoot_mod)
# calculate type-II analysis-of-variance table
FW_shoot_mod_sum <- Anova(FW_shoot_mod, type=2); FW_shoot_mod_sum
# post-hoc testing
set.seed(123); FW_shoot_mod1.HSD <- HSD.test(FW_shoot_mod, "donor_lab"); FW_shoot_mod1.HSD
set.seed(123); FW_shoot_mod2.HSD <- HSD.test(FW_shoot_mod, "chemotype"); FW_shoot_mod2.HSD
## FW LEAF ====================================================================
# check distribution of response variable using Cullen-Frey graph
descdist(pheno_data$FW_leaf_g, discrete=F)
# normalize response
set.seed(1); FW_leaf_norm <- bestNormalize(pheno_data$FW_leaf_g); FW_leaf_norm
pheno_data$FW_leaf_BC <- FW_leaf_norm$x.t
# run linear model
FW_leaf_mod <- lm(FW_leaf_BC ~ donor_lab*chemotype,
data=pheno_data,
contrasts=list(donor_lab=contr.sum,
chemotype=contr.sum))
# check assumptions visually
check_model(FW_leaf_mod)
# calculate type-III analysis-of-variance table
FW_leaf_mod_sum <- Anova(FW_leaf_mod, type=3); FW_leaf_mod_sum
# post-hoc testing
set.seed(123); FW_leaf_mod.HSD <- HSD.test(update(FW_leaf_mod, .~.
-donor_lab:chemotype +
dlab_chemotype),
"dlab_chemotype"); FW_leaf_mod.HSD
# FIGURE 2 ====================================================================
## Subfigure 2A ===============================================================
# PLANT HEIGHT
# prepare asterisk notation for interaction term
asterisk_2A <- if_else(height_mod_sum$`Pr(>F)`[4] < 0.001, "***",
if_else(height_mod_sum$`Pr(>F)`[4] < 0.01, "**",
if_else(height_mod_sum$`Pr(>F)`[4] < 0.05, "*",
"n.s."))); asterisk_2A
# plot
set.seed(123); subfigure_2A <- pheno_data %>%
ggplot(aes(x=donor_lab,
y=plant_height_cm,
color=chemotype,
fill=chemotype)) +
geom_boxplot(fill=rep(c("#4F94CD","#CD69C9"),5),
notch=F,
outlier.shape = NA) +
geom_point(fill="transparent",
position=position_jitterdodge(),
size=2, shape=21) +
scale_y_continuous(limits=c(0,28),
breaks=c(0,5,10,15,20,25),
expand=c(0,0)) +
labs(y="Plant height [cm]",
x=NULL,
title="",
subtitle=bquote("Donor laboratory"%*%"Chemotype"~"["*italic(F)[.(height_mod_sum$Df[4])*","*
.(height_mod_sum$Df[5])]~
"="~.(round(height_mod_sum$`F value`[4],2))*"]"~.(asterisk_2A))) +
scale_color_manual(values=rep(c("black","black"),4)) +
theme_cowplot() +
theme(legend.position = "none",
plot.title = element_text(size=12, face="plain"),
panel.border=element_rect(fill="transparent",
color="black",
linewidth=0.8)) +
stat_summary(geom='text',
label=height_mod.HSD$groups$groups[c(7,10,9,8,5,6,4:1)],
fun = max,
vjust = -1, hjust = 0.5,
position = position_dodge(width = 0.9)) +
annotate(geom="text",
x=.5, y= 1,
hjust=0, vjust=0,
label=bquote(italic(n)~"= 5")); subfigure_2A
## Subfigure 2B ===============================================================
# EXPANDED LEAVES
# prepare asterisk notation for factor donor lab
asterisk_2B_dlab <- if_else(leaves_mod_sum$`Pr(>F)`[1] < 0.001, "***",
if_else(leaves_mod_sum$`Pr(>F)`[1] < 0.01, "**",
if_else(leaves_mod_sum$`Pr(>F)`[1] < 0.05, "*",
"n.s."))); asterisk_2B_dlab
# prepare asterisk notation for factor chemotype
asterisk_2B_chem <- if_else(leaves_mod_sum$`Pr(>F)`[2] < 0.001, "***",
if_else(leaves_mod_sum$`Pr(>F)`[2] < 0.01, "**",
if_else(leaves_mod_sum$`Pr(>F)`[2] < 0.05, "*",
"n.s."))); asterisk_2B_chem
# plot
set.seed(123); subfigure_2B <- pheno_data %>%
ggplot(aes(x=donor_lab,
y=expanded_leaves_n,
color=chemotype,
fill=chemotype)) +
geom_boxplot(fill=rep(c("#4F94CD","#CD69C9"),5),
notch=F,
outlier.shape = NA) +
geom_point(show.legend=F,
fill="transparent",
position=position_jitterdodge(),
size=2, shape=21) +
scale_y_continuous(limits=c(0,28),
breaks=c(0,5,10,15,20,25),
expand=c(0,0),
sec.axis = sec_axis(~ predict(leaves_norm, .),
name = "Ordered-quantile normalised data")) +
labs(y="Number of leaves",
x=NULL,
title=bquote("Donor laboratory"~"["*italic(F)[.(leaves_mod_sum$Df[1])*","*
.(leaves_mod_sum$Df[3])]~
"="~.(round(leaves_mod_sum$`F value`[1],2))*"]"~.(asterisk_2B_dlab)),
subtitle=bquote("Chemotype"~"["*italic(F)[.(leaves_mod_sum$Df[2])*","*
.(leaves_mod_sum$Df[3])]~
"="~.(round(leaves_mod_sum$`F value`[2],2))*"]"~.(asterisk_2B_chem))) +
scale_color_manual(values=c("black","black"),
labels=c("Mono", "Mixed")) +
theme_cowplot() +
theme(legend.position = c(0.75, 0.9),
legend.title = element_blank(),
plot.title = element_text(size=12, face="plain"),
panel.border=element_rect(fill="transparent",
color="black",
linewidth=0.8)) +
guides(color = guide_legend(override.aes = list(fill = c("#4F94CD","#CD69C9")))) +
annotate(geom="text",
x=1:5,
y=c((pheno_data %>%
group_by(donor_lab) %>%
summarise(Value = max(expanded_leaves_n))))[[2]] + 0.5,
hjust=0, vjust=0,
label=toupper(leaves_mod.HSD$groups$groups[c(1,2,4,5,3)])) +
annotate(geom="text",
x=.5, y= 1,
hjust=0, vjust=0,
label=bquote(italic(n)~"= 5")); subfigure_2B
## Subfigure 2C ===============================================================
# FW BIOMASS
# prepare asterisk notation for factor donor lab
asterisk_2C_dlab <- if_else(FW_shoot_mod_sum$`Pr(>F)`[1] < 0.001, "***",
if_else(FW_shoot_mod_sum$`Pr(>F)`[1] < 0.01, "**",
if_else(FW_shoot_mod_sum$`Pr(>F)`[1] < 0.05, "*",
"n.s."))); asterisk_2C_dlab
# prepare asterisk notation for factor chemotype
asterisk_2C_chem <- if_else(FW_shoot_mod_sum$`Pr(>F)`[2] < 0.001, "***",
if_else(FW_shoot_mod_sum$`Pr(>F)`[2] < 0.01, "**",
if_else(FW_shoot_mod_sum$`Pr(>F)`[2] < 0.05, "*",
"n.s."))); asterisk_2C_chem
# plot
set.seed(123); subfigure_2C <- pheno_data %>%
ggplot(aes(x=donor_lab,
y=FW_biomass_g,
color=chemotype,
fill=chemotype)) +
geom_boxplot(fill=rep(c("#4F94CD","#CD69C9"),5),
notch=F,
outlier.shape = NA) +
geom_point(fill="transparent",
position=position_jitterdodge(),
size=2, shape=21) +
scale_y_continuous(limits=c(0,11),
expand=c(0,0),
breaks=c(0,2,4,6,8,10)) +
labs(y="Fresh weight of aboveground biomass [g]",
x="Donor laboratory",
title=bquote("Donor laboratory"~"["*italic(F)[.(FW_shoot_mod_sum$Df[1])*","*
.(FW_shoot_mod_sum$Df[3])]~
"="~.(round(FW_shoot_mod_sum$`F value`[1],2))*"]"~.(asterisk_2C_dlab)),
subtitle=bquote("Chemotype"~"["*italic(F)[.(FW_shoot_mod_sum$Df[2])*","*
.(FW_shoot_mod_sum$Df[3])]~
"="~.(round(FW_shoot_mod_sum$`F value`[2],2))*"]"~.(asterisk_2C_chem))) +
scale_color_manual(values=rep(c("black","black"),5),
labels=c("Mono", "Mixed")) +
theme_cowplot() +
theme(legend.position = "none",
plot.title = element_text(size=12, face="plain"),
panel.border=element_rect(fill="transparent",
color="black",
linewidth=0.8)) +
annotate(geom="text",
x=1:5,
y=c((pheno_data %>%
group_by(donor_lab) %>%
summarise(Value = max(FW_shoot_g))))[[2]] + 1.5,
hjust=0, vjust=0,
label=toupper(FW_shoot_mod1.HSD$groups$groups[c(4,5,2,1,3)])) +
annotate(geom="text",
x=.5, y= .4,
hjust=0, vjust=0,
label=bquote(italic(n)~"= 5")); subfigure_2C
## Subfigure 2D ===============================================================
# FW LEAF
# prepare asterisk notation for interaction term
asterisk_2D <- if_else(FW_leaf_mod_sum$`Pr(>F)`[4] < 0.001, "***",
if_else(FW_leaf_mod_sum$`Pr(>F)`[4] < 0.01, "**",
if_else(FW_leaf_mod_sum$`Pr(>F)`[4] < 0.05, "*",
"n.s."))); asterisk_2D
# plot
set.seed(123); subfigure_2D <- pheno_data %>%
ggplot(aes(x=donor_lab,
y=FW_leaf_g,
color=chemotype,
fill=chemotype)) +
geom_boxplot(fill=rep(c("#4F94CD","#CD69C9"),5),
notch=F,
outlier.shape = NA) +
geom_point(fill="transparent",
position=position_jitterdodge(),
size=2, shape=21) +
scale_y_continuous(limits=c(0,1.7),
expand=c(0,0),
sec.axis = sec_axis(~ predict(FW_leaf_norm, .), name = "Box-Cox transformed data")) +
labs(y="Fresh weight of sampled leaf [g]",
x="Donor laboratory",
title="",
subtitle=bquote("Donor laboratory"%*%"Chemotype"~"["*italic(F)[.(FW_leaf_mod_sum$Df[4])*","*
.(FW_leaf_mod_sum$Df[5])]~
"="~.(round(FW_leaf_mod_sum$`F value`[4],2))*"]"~.(asterisk_2D))) +
scale_color_manual(values=rep(c("black","black"),5),
labels=c("Mono", "Mixed")) +
theme_cowplot() +
theme(legend.position = "none",
plot.title = element_text(size=12, face="plain"),
panel.border=element_rect(fill="transparent",
color="black",
linewidth=0.8)) +
stat_summary(geom='text',
label=FW_leaf_mod.HSD$groups$groups[c(4,3,10,9,2,7,1,8,5:6)],
fun = max,
vjust = -1, hjust = 0.5,
position = position_dodge(width = 0.9)) +
annotate(geom="text",
x=.5, y= .05,
hjust=0, vjust=0,
label=bquote(italic(n)~"= 5")); subfigure_2D
## COMBINE PLOTS ==============================================================
plot_grid(subfigure_2A,
subfigure_2B,
subfigure_2C,
subfigure_2D,
ncol=2,
rel_heights = c(0.95,1),
rel_widths = c(0.9,1),
labels=LETTERS[1:4]) +
theme(plot.margin = margin(1,1,1,1, "cm"))
# and save
ggsave(plot=last_plot(),
file=here::here(".", # replace with desired file path
"Eckert_et_al_2023_Fig2.tiff"),
width=10, height=9,
dpi=600, bg="transparent",
compression = "lzw")
# SESSION INFO ================================================================
sessionInfo()