Body size analysis
Martin Garlovsky
2022-08-04
Last updated: 2025-04-02
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Knit directory: mito_age_fert/
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| Rmd | 5ebfaed | MartinGarlovsky | 2025-04-02 | wflow_publish(c("analysis/body_size.Rmd", "analysis/female_fertility.Rmd", |
| html | a243bcc | MartinGarlovsky | 2024-10-09 | Build site. |
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Load packages
library(tidyverse)
library(lme4)
library(DHARMa)
library(emmeans)
library(kableExtra)
library(knitrhooks) # install with devtools::install_github("nathaneastwood/knitrhooks")
library(showtext)
output_max_height() # a knitrhook option
options(stringsAsFactors = FALSE)
# colour palettes
met.pal <- MetBrewer::met.brewer('Johnson')
met3 <- met.pal[c(1, 3, 5)]
# set contrasts
options(contrasts = c("contr.sum", "contr.poly"))Load data
bd_dat <- read.csv('data/wrangled/body_data.csv') %>%
mutate(mito_snp = as.factor(mito_snp),
coevolved = if_else(mito == nuclear, "matched", "mismatched"))Geography based analysis
We collected 4 offspring of each sex from 4 females - a family
(fam) in our analysis - and measured landmarks on the wing
as a proxy for body size. We modelled body size with a linear mixed
model, with scaled wing area as the response.
Look at the raw data
bd_dat %>%
ggplot(aes(x = nuclear, y = scaled_area, fill = mito)) +
geom_boxplot() +
facet_wrap(~sex, scales = 'free_x') +
scale_colour_manual(values = met3) +
scale_fill_manual(values = met3) +
theme_bw() +
theme() +
NULL
| Version | Author | Date |
|---|---|---|
| a243bcc | MartinGarlovsky | 2024-10-09 |
hist(bd_dat$scaled_area, breaks = 50)
| Version | Author | Date |
|---|---|---|
| a243bcc | MartinGarlovsky | 2024-10-09 |
b1full <- lmerTest::lmer(scaled_area ~ mito * nuclear * sex + (sex|LINE) + (1|fam),
data = bd_dat, REML = TRUE)Check model diagnostics
performance::check_model(b1full)
| Version | Author | Date |
|---|---|---|
| a243bcc | MartinGarlovsky | 2024-10-09 |
Results
anova(b1full, type = "III", ddf = "Kenward-Roger") %>% broom::tidy() %>%
as_tibble() %>% #write_csv("output/anova_tables/bodysize.csv") # save anova table for supp. tables
kable(digits = 3,
caption = 'Type III Analysis of Variance Table with Kenward-Roger`s method') %>%
kable_styling(full_width = FALSE)| term | sumsq | meansq | NumDF | DenDF | statistic | p.value |
|---|---|---|---|---|---|---|
| mito | 0.107 | 0.054 | 2 | 18 | 0.601 | 0.559 |
| nuclear | 1.886 | 0.943 | 2 | 18 | 10.569 | 0.001 |
| sex | 35.956 | 35.956 | 1 | 18 | 403.047 | 0.000 |
| mito:nuclear | 0.061 | 0.015 | 4 | 18 | 0.172 | 0.950 |
| mito:sex | 0.081 | 0.040 | 2 | 18 | 0.454 | 0.642 |
| nuclear:sex | 0.476 | 0.238 | 2 | 18 | 2.667 | 0.097 |
| mito:nuclear:sex | 0.074 | 0.018 | 4 | 18 | 0.206 | 0.932 |
#summary(b1full)
bind_rows(emmeans(b1full, pairwise ~ sex, adjust = "tukey")$contrasts %>% as_tibble(),
emmeans(b1full, pairwise ~ nuclear, adjust = "tukey")$contrasts %>% as_tibble()) %>%
kable(digits = 3,
caption = 'Posthoc Tukey tests to compare which groups differ') %>%
kable_styling(full_width = FALSE) %>%
kableExtra::group_rows("Sex", 1, 1) %>%
kableExtra::group_rows("Nuclear", 2, 4)| contrast | estimate | SE | df | t.ratio | p.value |
|---|---|---|---|---|---|
| Sex | |||||
| f - m | 1.679 | 0.084 | 18 | 20.076 | 0.000 |
| Nuclear | |||||
| A - B | -0.563 | 0.144 | 18 | -3.905 | 0.003 |
| A - C | -0.584 | 0.144 | 18 | -4.054 | 0.002 |
| B - C | -0.021 | 0.144 | 18 | -0.149 | 0.988 |
Reaction norms
body2_emm <- emmeans(b1full, ~ mito * nuclear * sex)
# reaction norms
body_norms <- emmeans(body2_emm, ~ mito * nuclear * sex, type = 'response') %>% as_tibble() %>%
ggplot(aes(x = nuclear, y = emmean, fill = mito)) +
geom_line(aes(group = mito, colour = mito), position = position_dodge(width = .5)) +
# geom_jitter(data = bd_dat %>%
# group_by(LINE, sex) %>%
# summarise(mn = mean(scaled_area)) %>%
# separate(LINE, into = c("mito", "nuclear", NA), sep = "(?<=.)", remove = FALSE),
# aes(y = mn, colour = mito),
# position = position_jitterdodge(dodge.width = .5, jitter.width = .1),
# alpha = .5, size = 2) +
geom_point(size = 5, pch = 21, position = position_dodge(width = .5)) +
labs(y = 'Scaled wing area (EMM)') +
scale_colour_manual(values = met3) +
scale_fill_manual(values = met3) +
facet_wrap(~ sex, labeller = as_labeller(c(f = "Female", m = "Male"))) +
theme_bw() +
theme()
body_norms
| Version | Author | Date |
|---|---|---|
| a243bcc | MartinGarlovsky | 2024-10-09 |
Raw data with means
body_size <- emmeans(body2_emm, ~ mito * nuclear * sex, type = 'response') %>% as_tibble() %>%
ggplot(aes(x = nuclear, y = emmean, fill = mito)) +
geom_jitter(data = bd_dat,
aes(y = scaled_area, colour = mito),
position = position_jitterdodge(dodge.width = .5, jitter.width = .1),
size = 0.75, alpha = .1) +
geom_jitter(data = bd_dat %>%
group_by(LINE, sex) %>%
summarise(mn = mean(scaled_area)) %>%
separate(LINE, into = c("mito", "nuclear", NA), sep = "(?<=.)", remove = FALSE),
aes(y = mn, colour = mito),
position = position_jitterdodge(dodge.width = .5, jitter.width = .1),
alpha = .85, size = 2) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL),
width = .25, position = position_dodge(width = .5)) +
geom_point(size = 3, pch = 21, position = position_dodge(width = .5)) +
labs(y = 'Scaled wing area (EMM ± 95% CIs)') +
scale_colour_manual(values = met3) +
scale_fill_manual(values = met3) +
facet_wrap(~ sex, labeller = as_labeller(c(f = "Female", m = "Male"))) +
theme_bw() +
theme() +
geom_text(data = bd_dat %>% group_by(mito, nuclear, sex) %>% count(), aes(y = -2.2, label = n),
size = 2, position = position_dodge(width = .5)) +
NULL
body_size
| Version | Author | Date |
|---|---|---|
| a243bcc | MartinGarlovsky | 2024-10-09 |
Matched vs. mismatched
body_matched_lmerTest <- lmerTest::lmer(scaled_area ~ sex * coevolved + (sex|LINE) + (1|fam),
data = bd_dat, REML = TRUE)
anova(body_matched_lmerTest, type = "III", ddf = "Kenward-Roger")Type III Analysis of Variance Table with Kenward-Roger's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
sex 31.773 31.773 1 25 356.1571 2.647e-16 ***
coevolved 0.019 0.019 1 25 0.2154 0.6466
sex:coevolved 0.001 0.001 1 25 0.0111 0.9169
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#summary(body_matched_lmerTest)Mito-type analysis
snp_full <- lmerTest::lmer(scaled_area ~ mito_snp * nuclear * sex + (sex|LINE) + (1|fam),
data = bd_dat,
na.action = 'na.fail', REML = TRUE)
anova(snp_full, type = "III", ddf = "Kenward-Roger")Type III Analysis of Variance Table with Kenward-Roger's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
mito_snp 0.609 0.076 8 9 0.8532 0.583216
nuclear 1.364 0.682 2 9 7.6439 0.011477 *
sex 95.474 95.474 1 9 1070.1992 1.147e-10 ***
mito_snp:nuclear 1.168 0.167 7 9 1.8699 0.187948
mito_snp:sex 2.187 0.273 8 9 3.0639 0.057706 .
nuclear:sex 1.797 0.898 2 9 10.0688 0.005059 **
mito_snp:nuclear:sex 4.200 0.600 7 9 6.7254 0.005424 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# snp_full_emm <- emmeans(snp_full, ~ mito_snp * nuclear * sex)
# pairs(snp_full_emm, simple = "each")
emmeans(snp_full, ~ mito_snp * nuclear * sex) %>% as_tibble() %>% drop_na() %>%
ggplot(aes(x = nuclear, y = emmean, fill = mito_snp)) +
geom_jitter(data = bd_dat,
aes(y = scaled_area, colour = mito_snp),
position = position_jitterdodge(dodge.width = .5, jitter.width = .1),
alpha = .25) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL),
width = .25, position = position_dodge(width = .5)) +
geom_point(size = 3, pch = 21, position = position_dodge(width = .5)) +
labs(y = 'Scaled wing area (EMM ± 95% CIs)') +
scale_colour_viridis_d(option = "H") +
scale_fill_viridis_d(option = "H") +
facet_wrap(~sex, labeller = as_labeller(c(f = "Female", m = "Male"))) +
theme_bw() +
theme() +
#ggsave('figures/body_size_mitocluster.pdf', height = 3.5, width = 7.5, dpi = 600, useDingbats = FALSE) +
NULL
| Version | Author | Date |
|---|---|---|
| a243bcc | MartinGarlovsky | 2024-10-09 |
sessionInfo()R version 4.4.0 (2024-04-24) Platform: aarch64-apple-darwin20 Running under: macOS Sonoma 14.6.1 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0 locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 time zone: Europe/London tzcode source: internal attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] showtext_0.9-7 showtextdb_3.0 sysfonts_0.8.9 knitrhooks_0.0.4 [5] knitr_1.48 kableExtra_1.4.0 emmeans_1.10.5 DHARMa_0.4.7 [9] lme4_1.1-35.5 Matrix_1.7-1 lubridate_1.9.3 forcats_1.0.0 [13] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 [17] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 [21] workflowr_1.7.1 loaded via a namespace (and not attached): [1] tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2 [4] fastmap_1.2.0 bayestestR_0.15.0 promises_1.3.0 [7] digest_0.6.37 timechange_0.3.0 estimability_1.5.1 [10] lifecycle_1.0.4 processx_3.8.4 magrittr_2.0.3 [13] compiler_4.4.0 rlang_1.1.4 sass_0.4.9 [16] tools_4.4.0 utf8_1.2.4 yaml_2.3.10 [19] labeling_0.4.3 xml2_1.3.6 numDeriv_2016.8-1.1 [22] withr_3.0.2 datawizard_1.0.0 grid_4.4.0 [25] fansi_1.0.6 git2r_0.35.0 xtable_1.8-4 [28] colorspace_2.1-1 scales_1.3.0 MASS_7.3-61 [31] insight_1.0.1 cli_3.6.3 mvtnorm_1.3-1 [34] rmarkdown_2.28 generics_0.1.3 performance_0.13.0 [37] rstudioapi_0.17.1 httr_1.4.7 tzdb_0.4.0 [40] minqa_1.2.8 cachem_1.1.0 splines_4.4.0 [43] parallel_4.4.0 vctrs_0.6.5 boot_1.3-31 [46] jsonlite_1.8.9 callr_3.7.6 patchwork_1.3.0 [49] hms_1.1.3 pbkrtest_0.5.3 ggrepel_0.9.6 [52] systemfonts_1.1.0 see_0.9.0 jquerylib_0.1.4 [55] glue_1.8.0 nloptr_2.1.1 ps_1.8.1 [58] stringi_1.8.4 gtable_0.3.6 later_1.3.2 [61] lmerTest_3.1-3 munsell_0.5.1 pillar_1.9.0 [64] htmltools_0.5.8.1 R6_2.5.1 rprojroot_2.0.4 [67] evaluate_1.0.1 lattice_0.22-6 MetBrewer_0.2.0 [70] highr_0.11 backports_1.5.0 broom_1.0.7 [73] httpuv_1.6.15 bslib_0.8.0 Rcpp_1.0.13 [76] svglite_2.1.3 coda_0.19-4.1 nlme_3.1-166 [79] mgcv_1.9-1 whisker_0.4.1 xfun_0.48 [82] fs_1.6.5 getPass_0.2-4 pkgconfig_2.0.3