Linking yield with NLR PAV
Philipp Bayer
2020-09-22
Last updated: 2020-09-24
Checks: 6 1
Knit directory: R_gene_analysis/
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knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(tidyverse)-- Attaching packages ------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --v ggplot2 3.3.2 v purrr 0.3.4
v tibble 3.0.2 v dplyr 1.0.0
v tidyr 1.1.0 v stringr 1.4.0
v readr 1.3.1 v forcats 0.5.0-- Conflicts ---------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(patchwork)
library(ggsci)
library(dabestr)Loading required package: magrittr
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extractlibrary(dabestr)
library(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************
Attaching package: 'cowplot'The following object is masked from 'package:patchwork':
align_plotslibrary(ggsignif)
library(ggforce)
theme_set(theme_cowplot())Data loading
npg_col = pal_npg("nrc")(9)
col_list <- c(`Wild-type`=npg_col[8],
Landrace = npg_col[3],
`Old cultivar`=npg_col[2],
`Modern cultivar`=npg_col[4])
pav_table <- read_tsv('./data/soybean_pan_pav.matrix_gene.txt.gz')nbs <- read_tsv('./data/Lee.NBS.candidates.lst', col_names = c('Name', 'Class'))
nbs# A tibble: 486 x 2
Name Class
<chr> <chr>
1 UWASoyPan00953.t1 CN
2 GlymaLee.13G222900.1.p CN
3 GlymaLee.18G227000.1.p CN
4 GlymaLee.18G080600.1.p CN
5 GlymaLee.20G036200.1.p CN
6 UWASoyPan01876.t1 CN
7 UWASoyPan04211.t1 CN
8 GlymaLee.19G105400.1.p CN
9 GlymaLee.18G085100.1.p CN
10 GlymaLee.11G142600.1.p CN
# ... with 476 more rows# have to remove the .t1s
nbs$Name <- gsub('.t1','', nbs$Name)
nbs_pav_table <- pav_table %>% filter(Individual %in% nbs$Name)names <- c()
presences <- c()
for (i in seq_along(nbs_pav_table)){
if ( i == 1) next
thisind <- colnames(nbs_pav_table)[i]
pavs <- nbs_pav_table[[i]]
presents <- sum(pavs)
names <- c(names, thisind)
presences <- c(presences, presents)
}
nbs_res_tibb <- new_tibble(list(names = names, presences = presences))groups <- read_csv('./data/Table_of_cultivar_groups.csv')
groups <- groups %>%
mutate(`Group in violin table` = str_replace_all(`Group in violin table`, 'landrace', 'Landrace')) %>%
mutate(`Group in violin table` = str_replace_all(`Group in violin table`, 'Old_cultivar', 'Old cultivar')) %>%
mutate(`Group in violin table` = str_replace_all(`Group in violin table`, 'Modern_cultivar', 'Modern cultivar'))
groups$`Group in violin table` <-
factor(
groups$`Group in violin table`,
levels = c('Wild-type',
'Landrace',
'Old cultivar',
'Modern cultivar')
)
nbs_joined_groups <-
inner_join(nbs_res_tibb, groups, by = c('names' = 'Data-storage-ID'))Linking with yield
Can we link the trajectory of NLR genes with the trajectory of yield across the history of soybean breeding? let’s make a simple regression for now
Yield
yield <- read_tsv('./data/yield.txt')
yield_join <- inner_join(nbs_res_tibb, yield, by=c('names'='Line'))yield_join %>% ggplot(aes(x=presences, y=Yield)) + geom_hex() + geom_smooth() +
xlab('NLR gene count')
Protein
protein <- read_tsv('./data/protein_phenotype.txt')
protein_join <- left_join(nbs_res_tibb, protein, by=c('names'='Line')) %>% filter(!is.na(Protein))protein_join %>% ggplot(aes(x=presences, y=Protein)) + geom_hex() + geom_smooth() +
xlab('NLR gene count')
summary(lm(Protein ~ presences, data = protein_join))
Call:
lm(formula = Protein ~ presences, data = protein_join)
Residuals:
Min 1Q Median 3Q Max
-11.8479 -2.1274 -0.3336 1.9959 10.0949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.98158 7.24125 -1.102 0.271
presences 0.11786 0.01624 7.258 8.07e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.106 on 960 degrees of freedom
Multiple R-squared: 0.05203, Adjusted R-squared: 0.05104
F-statistic: 52.69 on 1 and 960 DF, p-value: 8.075e-13Seed weight
Let’s look at seed weight:
seed_weight <- read_tsv('./data/Seed_weight_Phenotype.txt', col_names = c('names', 'wt'))
seed_join <- left_join(nbs_res_tibb, seed_weight) %>% filter(!is.na(wt))seed_join %>% filter(wt > 5) %>% ggplot(aes(x=presences, y=wt)) + geom_hex() + geom_smooth() +
ylab('Seed weight') +
xlab('NLR gene count')
summary(lm(wt ~ presences, data = seed_join))
Call:
lm(formula = wt ~ presences, data = seed_join)
Residuals:
Min 1Q Median 3Q Max
-12.2910 -2.8692 0.1462 2.7771 19.6962
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.40656 14.67990 6.227 8.28e-10 ***
presences -0.17636 0.03298 -5.348 1.21e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.714 on 690 degrees of freedom
Multiple R-squared: 0.0398, Adjusted R-squared: 0.0384
F-statistic: 28.6 on 1 and 690 DF, p-value: 1.213e-07Oil content
And now let’s look at the oil phenotype:
oil <- read_tsv('./data/oil_phenotype.txt')
oil_join <- left_join(nbs_res_tibb, oil, by=c('names'='Line')) %>% filter(!is.na(Oil))oil_join %>% ggplot(aes(x=presences, y=Oil)) + geom_hex() + geom_smooth() +
xlab('NLR gene count')
summary(lm(Oil ~ presences, data = oil_join))
Call:
lm(formula = Oil ~ presences, data = oil_join)
Residuals:
Min 1Q Median 3Q Max
-10.4376 -1.9081 0.4846 2.2401 9.0361
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 118.03941 7.31646 16.13 <2e-16 ***
presences -0.22591 0.01641 -13.77 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.139 on 960 degrees of freedom
Multiple R-squared: 0.1649, Adjusted R-squared: 0.1641
F-statistic: 189.6 on 1 and 960 DF, p-value: < 2.2e-16OK there are many, many outliers here. Clearly I’ll have to do something fancier - for example, using the first two PCs as covariates might get rid of some of those outliers.
Boxplots per group
Yield
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(yield, by=c('names'='Line')) %>%
ggplot(aes(x=`Group in violin table`, y=Yield, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Protein') +
xlab('Accession group')
And let’s check the dots:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(yield_join, by = 'names') %>%
ggplot(aes(y=presences.x, x=Yield, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
ylab('NLR gene count')
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(yield_join, by = 'names') %>%
filter(`Group in violin table` != 'Landrace') %>%
ggplot(aes(x=presences.x, y=Yield, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
geom_smooth() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count')
## Protein
protein vs. the four groups:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(protein, by=c('names'='Line')) %>%
ggplot(aes(x=`Group in violin table`, y=Protein, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Protein') +
xlab('Accession group')
Seed weight
And seed weight:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(seed_join) %>%
ggplot(aes(x=`Group in violin table`, y=wt, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Seed weight') +
xlab('Accession group')
Wow, that’s breeding!
Oil content
And finally, Oil content:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
ggplot(aes(x=`Group in violin table`, y=Oil, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Oil content') +
xlab('Accession group')
Oha, a single star. That’s p < 0.05!
Let’s redo the above hexplot, but also color the dots by group.
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
ggplot(aes(x=presences.x, y=Oil, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count')
Oha, so it’s the wild-types that drag this out a lot.
Let’s remove them and see what it looks like:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
filter(`Group in violin table` %in% c('Old cultivar', 'Modern cultivar')) %>%
ggplot(aes(x=presences.x, y=Oil, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count') +
geom_smooth()
Let’s remove that one outlier:
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
filter(`Group in violin table` %in% c('Old cultivar', 'Modern cultivar')) %>%
filter(Oil > 13) %>%
ggplot(aes(x=presences.x, y=Oil, color=`Group in violin table`)) +
geom_point() +
scale_color_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
xlab('NLR gene count') +
geom_smooth()
Does the above oil content boxplot become different if we exclude the one outlier? I’d bet so
nbs_joined_groups %>%
filter(!is.na(`Group in violin table`)) %>%
inner_join(oil_join, by = 'names') %>%
filter(names != 'USB-393') %>%
ggplot(aes(x=`Group in violin table`, y=Oil, fill = `Group in violin table`)) +
geom_boxplot() +
scale_fill_manual(values = col_list) +
theme_minimal_hgrid() +
theme(axis.text.x = element_text(size=12),
axis.text.y = element_text(size=12)) +
geom_signif(comparisons = list(c('Wild-type', 'Landrace'),
c('Old cultivar', 'Modern cultivar')),
map_signif_level = T) +
guides(fill=FALSE) +
ylab('Oil content') +
xlab('Accession group')
Nope, still significantly higher in modern cultivars!
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)
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] ggforce_0.3.1 ggsignif_0.6.0 cowplot_1.0.0
[4] dabestr_0.3.0 magrittr_1.5 ggsci_2.9
[7] patchwork_1.0.0 forcats_0.5.0 stringr_1.4.0
[10] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[13] tidyr_1.1.0 tibble_3.0.2 ggplot2_3.3.2
[16] tidyverse_1.3.0 workflowr_1.6.2.9000
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.1 splines_3.6.3 modelr_0.1.8
[5] assertthat_0.2.1 getPass_0.2-2 blob_1.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.4 backports_1.1.10 lattice_0.20-41
[13] glue_1.4.2 digest_0.6.25 promises_1.1.1 polyclip_1.10-0
[17] rvest_0.3.5 colorspace_1.4-1 Matrix_1.2-18 htmltools_0.5.0
[21] httpuv_1.5.4 pkgconfig_2.0.3 broom_0.5.6 haven_2.3.1
[25] scales_1.1.1 processx_3.4.4 tweenr_1.0.1 whisker_0.4
[29] later_1.1.0.1 git2r_0.27.1 mgcv_1.8-31 generics_0.0.2
[33] farver_2.0.3 ellipsis_0.3.1 withr_2.2.0 hexbin_1.28.1
[37] cli_2.0.2 crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[41] ps_1.3.4 fs_1.5.0.9000 fansi_0.4.1 nlme_3.1-148
[45] MASS_7.3-51.6 xml2_1.3.2 tools_3.6.3 hms_0.5.3
[49] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 callr_3.4.4
[53] compiler_3.6.3 rlang_0.4.7 grid_3.6.3 rstudioapi_0.11
[57] labeling_0.3 rmarkdown_2.3 boot_1.3-25 gtable_0.3.0
[61] DBI_1.1.0 R6_2.4.1 lubridate_1.7.9 knitr_1.29
[65] utf8_1.1.4 rprojroot_1.3-2 stringi_1.5.3 Rcpp_1.0.5
[69] vctrs_0.3.1 dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.17