Visualising gene differences based on read alignments
Philipp Bayer
21 February 2022
Last updated: 2022-02-23
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Knit directory: Amphibolis_Posidonia_Comparison/
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Here I compare read alignment differences with known Arabidopsis genes.
library(tidyverse)Warning: package 'tidyverse' was built under R version 4.1.1-- Attaching packages --------------------------------------- tidyverse 1.3.1 --v ggplot2 3.3.5 v purrr 0.3.4
v tibble 3.1.5 v dplyr 1.0.7
v tidyr 1.1.4 v stringr 1.4.0
v readr 2.0.2 v forcats 0.5.1Warning: package 'ggplot2' was built under R version 4.1.1Warning: package 'tibble' was built under R version 4.1.1Warning: package 'tidyr' was built under R version 4.1.1Warning: package 'readr' was built under R version 4.1.1Warning: package 'purrr' was built under R version 4.1.1Warning: package 'dplyr' was built under R version 4.1.1Warning: package 'stringr' was built under R version 4.1.1Warning: package 'forcats' was built under R version 4.1.1-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(cowplot)Warning: package 'cowplot' was built under R version 4.1.1theme_set(theme_cowplot())
library(RColorBrewer)Warning: package 'RColorBrewer' was built under R version 4.1.1library(wesanderson)Warning: package 'wesanderson' was built under R version 4.1.1library(gghighlight)Warning: package 'gghighlight' was built under R version 4.1.2library(patchwork)Warning: package 'patchwork' was built under R version 4.1.1
Attaching package: 'patchwork'The following object is masked from 'package:cowplot':
align_plotsknitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())The data is based on Supplementary Table S6 and S7.
Stomata genes
stomata <- tibble::tribble(
~Gene.symbol, ~A..antarctica, ~P..australis, ~H..ovalis, ~Z..muelleri, ~Z..marina,
"SBT1.2", 0, 0, 5.63, 8.2, 4.34,
"EPFL9", 0, 0, 0, 0, 0,
"EPF1", 0, 25.08, 0, 0, 0,
"TMM", 3.35, 0, 0, 0, 0,
"EPF2", 0, 0, 0, 0, 0,
"MYB88", 0, 0, 6.74, 2.34, 0,
"MUTE", 0, 0, 0, 0, 0,
"SPCH", 7.95, 3.29, 5.84, 4.47, 0,
"FAMA", 6.99, 20.64, 6.83, 4.98, 4.98,
"SCRM2", 21.8, 25.57, 19.66, 26.16, 22.25,
"FLP", 3.2, 8.77, 4.42, 0, 0
)stomata_long <- stomata %>% pivot_longer(-Gene.symbol, values_to = 'Coverage', names_to='Species') %>%
mutate(Species = gsub('\\.\\.', '. ', Species))stom_p <- stomata_long %>% ggplot(aes(x=forcats::fct_rev(Species), y=Coverage, fill=Species, color=Species)) +
geom_boxplot(
aes(fill = after_scale(colorspace::lighten(fill, .9))),
size = 1.5, outlier.shape = NA
) +
geom_jitter(width = .1, size = 4, alpha = .5) +
ylim(c(-1,100)) +
theme(legend.position = "None",
axis.text.y = element_text(face = "italic")) +
scale_color_brewer(palette='Dark2') +
ylab('Coverage (%)') +
coord_flip() + xlab('Species')
stom_p
Ethylene
ethylene <- tibble::tribble(
~Gene.symbol, ~A..antarctica, ~P..australis, ~H..ovalis, ~Z..muelleri, ~Z..marina,
"ACO1", 0, 53.16, 0, 0, 0,
"ACO2", 0, 11.53, 0, 0, 0,
"ACO4", 0, 15.74, 0, 0, 0,
"ACO5", 0, 0, 0, 6.39, 5.3,
"ACS1", 9.34, 5.11, 0, 0, 0,
"ACS2", 7.24, 5.97, 0, 4.43, 0,
"ACS4", 18.74, 25.19, 0, 8.49, 8.21,
"ACS5", 19.25, 50.39, 4.46, 0, 0,
"ACS6", 0, 0, 0, 0, 0,
"ACS7", 10.27, 66.29, 0, 6.47, 0,
"ACS8", 38.58, 42.62, 6.24, 0, 0,
"ACS9", 20.52, 36.94, 0, 0, 0,
"ACS11", 0, 22.05, 7.45, 0, 0,
"ERS1", 35.23, 40.88, 0, 2.5, 0,
"ETR1/EIN1", 49.57, 51.87, 0, 3.83, 0,
"ETR2", 0, 21.96, 0, 0, 0,
"EIN4", 1.74, 31.33, 0, 0, 1.78,
"CTR1", 26.68, 31.55, 5.96, 0, 0,
"EIN2", 0, 14.54, 11.12, 0, 0,
"EBF1", 0, 0, 4.5, 0, 0,
"EBF2", 0, 12.45, 4.22, 0, 0,
"EIN3", 27.03, 43.08, 36.83, 31.27, 31.11,
"MYC2", 4.43, 27.83, 10.31, 23.61, 23.5,
"MYC3", 19, 17.14, 10.06, 12.14, 13.77,
"MYC4", 0, 3.84, 8.19, 20.96, 11.75
)ethylene_long <- ethylene %>% pivot_longer(-Gene.symbol, values_to = 'Coverage', names_to='Species') %>%
mutate(Species = gsub('\\.\\.', '. ', Species))eth_p <- ethylene_long %>% ggplot(aes(x=forcats::fct_rev(Species), y=Coverage, fill=Species, color=Species)) +
geom_boxplot(
aes(fill = after_scale(colorspace::lighten(fill, .9))),
size = 1.5, outlier.shape = NA
) +
geom_jitter(width = .1, size = 4, alpha = .5) +
ylim(c(-1,100)) +
theme(legend.position = "None",
axis.text.y = element_text(face = "italic")) +
scale_color_brewer(palette='Dark2') +
ylab('Coverage (%)') +
coord_flip() +
xlab('Species')
eth_p
# NDH genes
ndh <- tibble::tribble(
~Gene.symbol, ~A..antarctica, ~P..australis, ~H..ovalis, ~Z..muelleri, ~Z..marina,
"NDHL", 0, 0, 0, 16.67, 16.67,
"NDHM", 0, 0, 0, 36.24, 34.4,
"NDHN", 48.73, 0, 0, 66.35, 59.68,
"NDHO", 0, 0, 0, 33.54, 22.22,
"NDHS", 12.35, 28.29, 0, 34.4, 30.15,
"NDHT", 0, 8.67, 0, 19.47, 16.67,
"NDHU", 0, 0, 0, 9.89, 0,
"PNSB1", 4.26, 42.42, 0, 32.4, 31.24,
"PNSB2", 0, 0, 0, 0, 0,
"PNSB3", 0, 8.46, 0, 16.26, 9.76,
"PNSB4", 0, 19.13, 0, 18.75, 18.75,
"PNSL1", 0, 0, 0, 0, 0,
"PNSL2", 16.58, 0, 0, 45.03, 15.01,
"PNSL3", 0, 0, 0, 0, 0,
"PNSL4", 7.19, 23.55, 0, 31.04, 45.11,
"PNSL5", 52.82, 62.82, 57.31, 48.72, 46.28,
"CRR6", 0, 0, 0, 43.86, 33.06,
"CRR7", 0, 0, 0, 0, 0,
"Lhca5", 0, 35.15, 0, 18.29, 27.76,
"Lhca6", 0, 47.85, 0, 58.43, 60.27,
"CRR27", 21.9, 9.2, 9.26, 36.38, 41.78,
"CRR41", 0, 0, 0, 44.03, 7.86,
"PGR5", 55.97, 56.72, 53.98, 55.22, 55.22,
"PGRL1A", 17.54, 23.79, 29.54, 33.54, 31.08,
"CRR2", 41.49, 61.4, 2.94, 23.51, 13.93,
"CRR3", 0, 0, 0, 0, 0,
"CRR42", 0, 0, 0, 30.89, 28.44,
"PQL3", 0, 0, 0, 0, 0,
"NDF5", 0, 0, 0, 0, 0
)ndh_long <- ndh %>% pivot_longer(-Gene.symbol, values_to = 'Coverage', names_to='Species') %>%
mutate(Species = gsub('\\.\\.', '. ', Species))ndh_p <- ndh_long %>% ggplot(aes(x=forcats::fct_rev(Species), y=Coverage, fill=Species, color=Species)) +
geom_boxplot(
aes( fill = after_scale(colorspace::lighten(fill, .9))),
size = 1.5, outlier.shape = NA
) +
geom_jitter(width = .1, size = 4, alpha = .5) +
ylim(c(-1,100)) +
theme(legend.position = "None",
axis.text.y = element_text(face = "italic")) +
scale_color_brewer(palette='Dark2') +
ylab('Coverage %') +
coord_flip() +
xlab('Species')
ndh_p
The following data is slightly different because it compares lengths of annotated genes in the chloroplast assembly.
ndh_chloro <- tibble::tribble(
~Gene.symbol, ~A..antarctica, ~H..ovalis, ~P..australis, ~Z..marina, ~Z..muelleri,
"ndhA", 0, 0, 0, 101.8, 101.4,
"ndhB", 0, 0, 0, 100.4, 100.9,
"ndhB-2", 0, 0, 0, 100.4, 100.9,
"ndhC", 92.5, 0, 100, 100, 100,
"ndhD", 29.4, 0, 0, 100, 100,
"ndhE", 0, 0, 45.4, 100, 100,
"ndhF", 0, 0, 0, 98.4, 98.3,
"ndhG", 45.9, 0, 0, 100, 100,
"ndhH", 34, 63.1, 63.8, 100, 100,
"ndhI", 0, 0, 0, 110.5, 110.5,
"ndhJ", 70.8, 0, 0, 100, 100,
"ndhK", 55, 0, 109.3, 109.8, 109.8
)ndh_chl_long <- ndh_chloro %>% pivot_longer(-Gene.symbol, values_to = 'Coverage', names_to='Species') %>%
mutate(Species = gsub('\\.\\.', '. ', Species))Stylistic choice - if the new gene is longer than the Arabidopsis one, set to 100%.
ndh_chl_p <- ndh_chl_long %>%
mutate(Coverage = case_when(Coverage > 100 ~ 100,
TRUE ~ Coverage)) %>%
ggplot(aes(x=forcats::fct_rev(Species), y=Coverage, fill=Species, color=Species)) +
geom_boxplot(
aes( fill = after_scale(colorspace::lighten(fill, .9))),
size = 1.5, outlier.shape = NA
) +
geom_jitter(width = .1, size = 4, alpha = .5) +
ylim(c(-1,100.1)) +
theme(legend.position = "None",
axis.text.y = element_text(face = "italic")) +
scale_color_brewer(palette='Dark2') +
ylab('Gene size similarity (%)') +
xlab('Species') +
coord_flip()
ndh_chl_p
all together
patchwork <- (eth_p + stom_p) / ( ndh_chl_p + ndh_p )
# patchwork[[2]] = patchwork[[2]] + theme(axis.text.y = element_blank(),
# axis.ticks.y = element_blank(),
# axis.title.y = element_blank() )
# patchwork[[1]] = patchwork[[1]] + theme(axis.text.y = element_blank(),
# axis.ticks.y = element_blank(),
# axis.title.y = element_blank() )
patchwork+ plot_annotation(tag_levels = "A")
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
[3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
[5] LC_TIME=English_Australia.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.1.1 gghighlight_0.3.2 wesanderson_0.3.6 RColorBrewer_1.1-2
[5] cowplot_1.1.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[9] purrr_0.3.4 readr_2.0.2 tidyr_1.1.4 tibble_3.1.5
[13] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lubridate_1.8.0 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.28 utf8_1.2.2 R6_2.5.1 cellranger_1.1.0
[9] backports_1.2.1 reprex_2.0.1 evaluate_0.14 highr_0.9
[13] httr_1.4.2 pillar_1.6.4 rlang_0.4.12 readxl_1.3.1
[17] rstudioapi_0.13 whisker_0.4 jquerylib_0.1.4 rmarkdown_2.11
[21] labeling_0.4.2 munsell_0.5.0 broom_0.7.9 compiler_4.1.0
[25] httpuv_1.6.3 modelr_0.1.8 xfun_0.27 pkgconfig_2.0.3
[29] htmltools_0.5.2 tidyselect_1.1.1 fansi_0.5.0 crayon_1.4.1
[33] tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.2 later_1.3.0
[37] grid_4.1.0 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.1
[41] DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1 scales_1.1.1
[45] cli_3.0.1 stringi_1.7.5 farver_2.1.0 fs_1.5.0
[49] promises_1.2.0.1 xml2_1.3.2 bslib_0.3.1 ellipsis_0.3.2
[53] generics_0.1.1 vctrs_0.3.8 tools_4.1.0 glue_1.4.2
[57] hms_1.1.1 fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-2
[61] rvest_1.0.2 knitr_1.36 haven_2.4.3 sass_0.4.0