first-analysis

Last updated: 2020-09-21

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Knit directory: R_gene_analysis/

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knitr::opts_chunk$set(warning = FALSE, message = FALSE) 
library(tidyverse)
library(patchwork)
library(ggsci)
library(dabestr)
library(dabestr)
library(cowplot)
library(ggsignif)

theme_set(theme_cowplot())

Introduction

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 part

Let’s pull the NBS genes from the table

nbs <- read_tsv('./data/Lee.NBS.candidates.lst', col_names = c('Name', 'Class'))
# have to remove the .t1s 
nbs$Name <- gsub('.t1','', nbs$Name)

nbs_pav_table <- pav_table %>% filter(Individual %in% nbs$Name)

names <- c()
percs <- c()

for (i in seq_along(nbs_pav_table)){
  if ( i == 1) next
  thisind <- colnames(nbs_pav_table)[i]
  pavs <- nbs_pav_table[[i]]
  perc <- sum(pavs) / length(pavs) * 100
  names <- c(names, thisind)
  percs <- c(percs, perc)
}
nbs_res_tibb <- new_tibble(list(names = names, percs = percs))

OK what do these presence percentages look like?

ggplot(data=nbs_res_tibb, aes(x=percs)) + geom_histogram(bins=25)

On average, 91.7701034% of NBS genes are present in each individual.

Now let’s join the table of presences to the four different types so we can group these numbers.

nbs_groups <- read_csv('./data/Table_of_cultivar_groups.csv')
nbs_joined_groups <- left_join(nbs_res_tibb, nbs_groups, by = c('names'='Data-storage-ID'))

nbs_joined_groups$`Group in violin table` <- gsub('landrace', 'Landrace', nbs_joined_groups$`Group in violin table`)
nbs_joined_groups$`Group in violin table` <- gsub('Modern_cultivar', 'Modern cultivar', nbs_joined_groups$`Group in violin table`)
nbs_joined_groups$`Group in violin table` <- gsub('Old_cultivar', 'Old cultivar', nbs_joined_groups$`Group in violin table`)

nbs_joined_groups$`Group in violin table` <- factor(nbs_joined_groups$`Group in violin table`, levels=c(NA, 'Wild-type', 'Landrace', 'Old cultivar', 'Modern cultivar'))

library(ggforce)
nbs_vio <- nbs_joined_groups %>% filter(`Group in violin table` != 'NA') %>% 
  ggplot(aes(y=percs, x=`Group in violin table`, fill=`Group in violin table`)) + 
  geom_violin(draw_quantiles = c(0.5)) +
  geom_sina(alpha=0.5) +
  geom_smooth(aes(group=1), method='glm') +
  scale_fill_manual(values=col_list)+
  guides(fill = FALSE) +
  ylim(c(87, 100))

nbs_vio

nbs_joined_groups %>% filter(`Group in violin table` != 'NA') %>% 
  ggplot(aes(y=percs, x=`Group in violin table`, fill=`Group in violin table`)) + 
  geom_smooth(aes(group=1), method='lm', se = FALSE) +
  geom_jitter() +
  scale_fill_manual(values=col_list)+
  guides(fill = FALSE)# +

  #ylim(c(0, 100))

nbs_joined_groups %>% filter(!is.na(`PI-ID`)) %>% 
  group_by(`Group in violin table`) %>% 
  summarise(min_perc = min(percs),
            max_perc = max(percs),
            mean_perc = mean(percs),
            median_perc = median(percs),
            std_perc = sd(percs)) %>% 
  knitr::kable()

Group in violin table	min_perc	max_perc	mean_perc	median_perc	std_perc
Wild-type	89.50617	97.32510	93.19939	93.20988	1.4754746
Landrace	88.27160	95.67901	91.54130	91.56379	1.0312082
Old cultivar	89.09465	93.82716	91.53695	91.56379	1.0701423
Modern cultivar	88.68313	93.62140	91.01125	90.94650	0.8329189

RLK part

Let’s do the same plot with RLKs

rlk <- read_tsv('./data/Lee.RLK.candidates.lst', col_names = c('Name', 'Class', 'Subtype'))

# have to remove the .t1s 
rlk$Name <- gsub('.t1','', rlk$Name)

rlk_pav_table <- pav_table %>% filter(Individual %in% rlk$Name)

names <- c()
percs <- c()

for (i in seq_along(rlk_pav_table)){
  if ( i == 1) next
  thisind <- colnames(rlk_pav_table)[i]
  pavs <- rlk_pav_table[[i]]
  perc <- sum(pavs) / length(pavs) * 100
  names <- c(names, thisind)
  percs <- c(percs, perc)
}
rlk_res_tibb <- new_tibble(list(names = names, percs = percs))

OK what do these presence percentages look like?

ggplot(data=rlk_res_tibb, aes(x=percs)) + geom_histogram(bins=25)

On average, 99.190418% of NBS genes are present in each individual.

Now let’s join the table of presences to the four different types so we can group these numbers.

groups <- read_csv('./data/Table_of_cultivar_groups.csv')

rlk_joined_groups <- left_join(rlk_res_tibb, groups, by = c('names'='Data-storage-ID'))

rlk_joined_groups$`Group in violin table` <- gsub('landrace', 'Landrace', rlk_joined_groups$`Group in violin table`)
rlk_joined_groups$`Group in violin table` <- gsub('Modern_cultivar', 'Modern cultivar', rlk_joined_groups$`Group in violin table`)
rlk_joined_groups$`Group in violin table` <- gsub('Old_cultivar', 'Old cultivar', rlk_joined_groups$`Group in violin table`)

rlk_joined_groups$`Group in violin table` <- factor(rlk_joined_groups$`Group in violin table`, levels=c(NA, 'Wild-type', 'Landrace', 'Old cultivar', 'Modern cultivar'))

rlk_vio <- rlk_joined_groups %>% filter(`Group in violin table` != 'NA') %>% 
  ggplot(aes(y=percs, x=`Group in violin table`, fill=`Group in violin table`)) + 
  geom_violin(draw_quantiles = c(0.5)) +
  geom_sina(alpha=0.5) +
  geom_smooth(aes(group=1), method='lm', se = FALSE) +
  scale_fill_manual(values=col_list)+
  guides(fill = FALSE)# +
  #ylim(c(87, 100))
rlk_vio

rlk_joined_groups %>% filter(!is.na(`PI-ID`)) %>% 
  group_by(`Group in violin table`) %>% 
  summarise(min_perc = min(percs),
            max_perc = max(percs),
            mean_perc = mean(percs),
            median_perc = median(percs),
            std_perc = sd(percs)) %>% 
  knitr::kable()

Group in violin table	min_perc	max_perc	mean_perc	median_perc	std_perc
Wild-type	98.38022	99.74425	99.26314	99.31799	0.2177805
Landrace	98.63598	99.57374	99.16600	99.14749	0.1278145
Old cultivar	98.97698	99.40324	99.19752	99.23274	0.1199946
Modern cultivar	98.80648	99.57374	99.18922	99.14749	0.1255006

RLP part

And now with RLPs

rlp <- read_tsv('./data/Lee.RLP.candidates.lst', col_names = c('Name', 'Class', 'Subtype'))

# have to remove the .t1s 
rlp$Name <- gsub('.t1','', rlp$Name)

rlp_pav_table <- pav_table %>% filter(Individual %in% rlp$Name)

names <- c()
percs <- c()

for (i in seq_along(rlp_pav_table)){
  if ( i == 1) next
  thisind <- colnames(rlp_pav_table)[i]
  pavs <- rlp_pav_table[[i]]
  perc <- sum(pavs) / length(pavs) * 100
  names <- c(names, thisind)
  percs <- c(percs, perc)
}
rlp_res_tibb <- new_tibble(list(names = names, percs = percs))

OK what do these presence percentages look like?

ggplot(data=rlp_res_tibb, aes(x=percs)) + geom_histogram(bins=25)

On average, 95.6496496% of NBS genes are present in each individual.

Now let’s join the table of presences to the four different types so we can group these numbers.

groups <- read_csv('./data/Table_of_cultivar_groups.csv')
rlp_joined_groups <- left_join(rlp_res_tibb, groups, by = c('names'='Data-storage-ID'))

rlp_joined_groups$`Group in violin table` <- gsub('landrace', 'Landrace', rlp_joined_groups$`Group in violin table`)
rlp_joined_groups$`Group in violin table` <- gsub('Modern_cultivar', 'Modern cultivar', rlp_joined_groups$`Group in violin table`)
rlp_joined_groups$`Group in violin table` <- gsub('Old_cultivar', 'Old cultivar', rlp_joined_groups$`Group in violin table`)

rlp_joined_groups$`Group in violin table` <- factor(rlp_joined_groups$`Group in violin table`, levels=c(NA, 'Wild-type', 'Landrace', 'Old cultivar', 'Modern cultivar'))

rlp_vio <- rlp_joined_groups %>% filter(`Group in violin table` != 'NA') %>% 
  ggplot(aes(y=percs, x=`Group in violin table`, fill=`Group in violin table`)) + 
  geom_violin(draw_quantiles = c(0.5)) +
  geom_sina(alpha=0.5) +
    geom_smooth(aes(group=1), method='lm', se = FALSE) +
  scale_fill_manual(values=col_list)+
  guides(fill = FALSE) +
  ylim(c(87, 100))
rlp_vio

rlp_joined_groups %>% filter(`Group in violin table` != 'NA') %>% 
  ggplot(aes(y=percs, x=`Group in violin table`, fill=`Group in violin table`)) + 
  geom_jitter() +
  #geom_sina(alpha=0.5) +
  scale_fill_manual(values=col_list)+
  guides(fill = FALSE) +
  ylim(c(87, 100))

rlp_joined_groups %>% filter(!is.na(`PI-ID`)) %>% 
  group_by(`Group in violin table`) %>% 
  summarise(min_perc = min(percs),
            max_perc = max(percs),
            mean_perc = mean(percs),
            median_perc = median(percs),
            std_perc = sd(percs)) %>% 
  knitr::kable()

Group in violin table	min_perc	max_perc	mean_perc	median_perc	std_perc
Wild-type	93.33333	98.33333	96.34112	96.11111	0.8985510
Landrace	90.00000	98.33333	95.53711	95.55556	0.9230701
Old cultivar	93.88889	97.77778	95.45894	95.55556	0.9019439
Modern cultivar	93.88889	97.22222	95.44678	95.55556	0.7169931

Plotting together

nbs_vio + rlk_vio + rlp_vio

Stats - Dabayes

I want to know whether the groups are statistically significantly different. First let’s use dabestr

NBS

Let’s run dabestr first:

nbs_multi.two.group.unpaired <- 
  nbs_joined_groups %>% filter(!is.na(`PI-ID`)) %>% 
  dabest(`Group in violin table`, percs, 
         idx = list(c("Wild-type", "Landrace"),
                    c('Old cultivar', 'Modern cultivar')),
         paired = FALSE)
nbs_multi.two.group.unpaired

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good evening!
The current time is 12:55 PM on Monday September 21, 2020.

Dataset    :  .
The first five rows are:
# A tibble: 5 x 4
  names  percs `PI-ID`  `Group in violin table`
  <chr>  <dbl> <chr>    <fct>                  
1 AB-01   91.6 PI458020 Landrace               
2 AB-02   93.4 PI603713 Landrace               
3 DT2000  92.0 PI635999 Modern cultivar        
4 For     92.2 PI548645 Modern cultivar        
5 HN001   92.2 PI518664 Modern cultivar        

X Variable :  Group in violin table
Y Variable :  percs

Effect sizes(s) will be computed for:
  1. Landrace minus Wild-type
  2. Modern cultivar minus Old cultivar

nbs_multi.two.group.unpaired.meandiff <- mean_diff(nbs_multi.two.group.unpaired)
nbs_multi.two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good evening!
The current time is 12:55 PM on Monday September 21, 2020.

Dataset    :  .
X Variable :  Group in violin table
Y Variable :  percs

Unpaired mean difference of Landrace (n = 723) minus Wild-type (n = 157)
 -1.66 [95CI  -1.9; -1.41]

Unpaired mean difference of Modern cultivar (n = 143) minus Old cultivar (n = 46)
 -0.526 [95CI  -0.875; -0.2]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

plot(nbs_multi.two.group.unpaired.meandiff, color.column=`Group in violin table`,
     rawplot.ylabel = 'Presence (%)', show.legend=FALSE)

RLK

rlk_multi.two.group.unpaired <- 
  rlk_joined_groups %>% filter(!is.na(`PI-ID`)) %>% 
  dabest(`Group in violin table`, percs, 
         idx = list(c("Wild-type", "Landrace"),
                    c('Old cultivar', 'Modern cultivar')),
         paired = FALSE)
rlk_multi.two.group.unpaired

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good evening!
The current time is 12:55 PM on Monday September 21, 2020.

Dataset    :  .
The first five rows are:
# A tibble: 5 x 4
  names  percs `PI-ID`  `Group in violin table`
  <chr>  <dbl> <chr>    <fct>                  
1 AB-01   99.5 PI458020 Landrace               
2 AB-02   99.1 PI603713 Landrace               
3 DT2000  99.3 PI635999 Modern cultivar        
4 For     99.1 PI548645 Modern cultivar        
5 HN001   99.1 PI518664 Modern cultivar        

X Variable :  Group in violin table
Y Variable :  percs

Effect sizes(s) will be computed for:
  1. Landrace minus Wild-type
  2. Modern cultivar minus Old cultivar

rlk_multi.two.group.unpaired.meandiff <- mean_diff(rlk_multi.two.group.unpaired)
rlk_multi.two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good evening!
The current time is 12:55 PM on Monday September 21, 2020.

Dataset    :  .
X Variable :  Group in violin table
Y Variable :  percs

Unpaired mean difference of Landrace (n = 723) minus Wild-type (n = 157)
 -0.0971 [95CI  -0.132; -0.0611]

Unpaired mean difference of Modern cultivar (n = 143) minus Old cultivar (n = 46)
 -0.00831 [95CI  -0.0479; 0.0308]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

plot(rlk_multi.two.group.unpaired.meandiff, color.column=`Group in violin table`,
     rawplot.ylabel = 'Presence (%)', show.legend=FALSE)

No difference between old and modern cultivars!

RLP

rlp_multi.two.group.unpaired <- 
  rlp_joined_groups %>% filter(!is.na(`PI-ID`)) %>% 
  dabest(`Group in violin table`, percs, 
         idx = list(c("Wild-type", "Landrace"),
                    c('Old cultivar', 'Modern cultivar')),
         paired = FALSE)
rlp_multi.two.group.unpaired

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good evening!
The current time is 12:55 PM on Monday September 21, 2020.

Dataset    :  .
The first five rows are:
# A tibble: 5 x 4
  names  percs `PI-ID`  `Group in violin table`
  <chr>  <dbl> <chr>    <fct>                  
1 AB-01   95   PI458020 Landrace               
2 AB-02   95.6 PI603713 Landrace               
3 DT2000  95   PI635999 Modern cultivar        
4 For     95   PI548645 Modern cultivar        
5 HN001   95.6 PI518664 Modern cultivar        

X Variable :  Group in violin table
Y Variable :  percs

Effect sizes(s) will be computed for:
  1. Landrace minus Wild-type
  2. Modern cultivar minus Old cultivar

rlp_multi.two.group.unpaired.meandiff <- mean_diff(rlp_multi.two.group.unpaired)
rlp_multi.two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good evening!
The current time is 12:55 PM on Monday September 21, 2020.

Dataset    :  .
X Variable :  Group in violin table
Y Variable :  percs

Unpaired mean difference of Landrace (n = 723) minus Wild-type (n = 157)
 -0.804 [95CI  -0.965; -0.651]

Unpaired mean difference of Modern cultivar (n = 143) minus Old cultivar (n = 46)
 -0.0122 [95CI  -0.294; 0.265]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

plot(rlp_multi.two.group.unpaired.meandiff, color.column=`Group in violin table`,
     rawplot.ylabel = 'Presence (%)', show.legend=FALSE)

Again, no difference between old and modern cultivars!

Stats - classic t-test

NBS

nbs_joined_groups %>% 
  filter( !is.na(`PI-ID`) ) %>%
    ggplot(aes(x=`Group in violin table`, y = percs,
               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)

RLP

rlp_joined_groups %>% 
  filter( !is.na(`PI-ID`) ) %>%
    ggplot(aes(x=`Group in violin table`, y = percs,
               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)

RLK

rlk_joined_groups %>% 
  filter( !is.na(`PI-ID`) ) %>%
    ggplot(aes(x=`Group in violin table`, y = percs,
               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)

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] nlme_3.1-148       fs_1.5.0.9000      lubridate_1.7.9    RColorBrewer_1.1-2
 [5] httr_1.4.2         rprojroot_1.3-2    tools_3.6.3        backports_1.1.10  
 [9] utf8_1.1.4         R6_2.4.1           vipor_0.4.5        DBI_1.1.0         
[13] mgcv_1.8-31        colorspace_1.4-1   withr_2.2.0        tidyselect_1.1.0  
[17] processx_3.4.4     compiler_3.6.3     git2r_0.27.1       cli_2.0.2         
[21] rvest_0.3.5        xml2_1.3.2         labeling_0.3       scales_1.1.1      
[25] callr_3.4.4        digest_0.6.25      rmarkdown_2.3      pkgconfig_2.0.3   
[29] htmltools_0.5.0    dbplyr_1.4.4       highr_0.8          rlang_0.4.7       
[33] readxl_1.3.1       rstudioapi_0.11    farver_2.0.3       generics_0.0.2    
[37] jsonlite_1.7.1     Matrix_1.2-18      Rcpp_1.0.5         ggbeeswarm_0.6.0  
[41] munsell_0.5.0      fansi_0.4.1        lifecycle_0.2.0    stringi_1.5.3     
[45] whisker_0.4        yaml_2.2.1         MASS_7.3-51.6      plyr_1.8.6        
[49] grid_3.6.3         blob_1.2.1         promises_1.1.1     crayon_1.3.4      
[53] lattice_0.20-41    haven_2.3.1        splines_3.6.3      hms_0.5.3         
[57] knitr_1.29         ps_1.3.4           pillar_1.4.4       boot_1.3-25       
[61] reprex_0.3.0       glue_1.4.2         evaluate_0.14      getPass_0.2-2     
[65] modelr_0.1.8       vctrs_0.3.1        tweenr_1.0.1       httpuv_1.5.4      
[69] cellranger_1.1.0   gtable_0.3.0       polyclip_1.10-0    assertthat_0.2.1  
[73] xfun_0.17          broom_0.5.6        later_1.1.0.1      beeswarm_0.2.3    
[77] ellipsis_0.3.1

first-analysis

Philipp Bayer

2020-09-17

Introduction

NBS part

RLK part

RLP part

Plotting together

Stats - Dabayes

NBS

RLK

RLP

Stats - classic t-test

NBS

RLP

RLK