Last updated: 2022-02-21

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Here I use Orthofinder results to pull out genes unique to seagrasses, unique to duckweeds, and make some nice summary tables

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 0.5.1
Warning: package 'ggplot2' was built under R version 4.1.1
Warning: package 'tibble' was built under R version 4.1.1
Warning: package 'tidyr' was built under R version 4.1.1
Warning: package 'readr' was built under R version 4.1.1
Warning: package 'purrr' was built under R version 4.1.1
Warning: package 'dplyr' was built under R version 4.1.1
Warning: package 'stringr' was built under R version 4.1.1
Warning: 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 4.1.1
theme_set(theme_cowplot())
library(RColorBrewer)
Warning: package 'RColorBrewer' was built under R version 4.1.1
library(wesanderson)
Warning: package 'wesanderson' was built under R version 4.1.1
library(patchwork)
Warning: package 'patchwork' was built under R version 4.1.1

Attaching package: 'patchwork'
The following object is masked from 'package:cowplot':

    align_plots
library(UpSetR)
Warning: package 'UpSetR' was built under R version 4.1.1
library(ggVennDiagram)
Warning: package 'ggVennDiagram' was built under R version 4.1.2
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
groups <- read_tsv('./data/Orthogroups.tsv') 
Rows: 31136 Columns: 20
-- Column specification --------------------------------------------------------
Delimiter: "\t"
chr (20): Orthogroup, Amphibolis_final.genome.scf.bigger1kbp.all.maker.prote...

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.
names(groups) <- c('Orthogroup', 'A. antarctica', 'A. trichopada', 'B. distachyon', 'C. reinhardtii', 'L. gibba', 'O. sativa', 'P. australis', 'P. patens', 'P. trichocarpa', 'S. moellendorffii', 'S. polyrhiza', 'A. thaliana', 'T. parvula', 'V. vinifera', 'Z. mays', 'Z. muelleri', 'Z. marina', 'O. lucimarinus', 'W. australis')
# for upsetr, we need to know only which OG-groups are shared between species, the actual genes don't matter
per_spec <- groups %>% pivot_longer(-Orthogroup) %>% 
  filter(!is.na(value)) %>% # species not in an orthogroup are still listed, they just have NA genes for this group
  select(-value) # don't need all gene names, speed things up
# now I want the data in this format:
# listInput <- list(one = c(1, 2, 3, 5, 7, 8, 11, 12, 13), two = c(1, 2, 4, 5, 
#   10), three = c(1, 5, 6, 7, 8, 9, 10, 12, 13))
x <- per_spec %>% 
  select(name, Orthogroup) %>% # turn the table around
  deframe() # convert to named vector
mylist <- lapply(split(x, names(x)), unname) # yuck - ugly code to convert the named vector to a list
x <- upset(fromList(mylist), order.by='freq', nsets = length(groups) - 1)
x

Let’s get the species-only cluster numbers

species_specific_orthos <- per_spec %>% 
  group_by(Orthogroup) %>% 
  summarise(counts = length(name)) %>%
  filter(counts == 1)
per_spec %>% 
  filter(Orthogroup %in% species_specific_orthos$Orthogroup) %>% 
  group_by(name) %>% 
  count() %>% 
  arrange(n) %>% 
  knitr::kable()
name n
A. antarctica 74
S. polyrhiza 155
O. lucimarinus 198
L. gibba 267
P. australis 267
T. parvula 273
Z. marina 322
P. trichocarpa 485
A. thaliana 489
B. distachyon 507
Z. muelleri 619
V. vinifera 682
A. trichopada 848
W. australis 1006
C. reinhardtii 1105
Z. mays 1108
S. moellendorffii 1334
O. sativa 1419
P. patens 1568

How many orthogroups are shared between the four seagrasses?

newlist <- mylist[c('A. antarctica', 'Z. marina', 'P. australis', 'Z. muelleri')]

x <- upset(fromList(newlist), order.by='freq', nsets = 4)
x

Connect Orthofinder results with functional table of Arabidopsis genes

File comes from https://www.arabidopsis.org/download_files/Genes/TAIR10_genome_release/TAIR10_functional_descriptions

download.file('https://www.arabidopsis.org/download_files/Genes/TAIR10_genome_release/TAIR10_functional_descriptions', 'data/TAIR10_functional_descriptions')
functions <- read_tsv('data/TAIR10_functional_descriptions')
Rows: 41671 Columns: 5
-- Column specification --------------------------------------------------------
Delimiter: "\t"
chr (5): Model_name, Type, Short_description, Curator_summary, Computational...

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

I also downloaded the gene symbols:

esearch -db gene -query "Arabidopsis thaliana [ORGN]" | esummary | xtract -pattern DocumentSummary -element Name,OtherAliases |  awk -F "\t|," '{OFS="\t"}{print $2,$1}' > arabidopsis_gene_symbols.txt
symbols <- read_tsv('./data/arabidopsis_gene_symbols.txt', col_names = c('Gene','Symbol'))
Rows: 44111 Columns: 2
-- Column specification --------------------------------------------------------
Delimiter: "\t"
chr (2): Gene, Symbol

i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.

OK now I need to flip the Orthofinder table to then join the functional table

ara_groups <- groups %>% select(Orthogroup, `A. thaliana`) %>% filter(!is.na(`A. thaliana`)) %>% separate_rows(Orthogroup, `A. thaliana`, convert=T)
ara_groups <- ara_groups %>% separate(`A. thaliana`, c('Gene','Number'), remove=FALSE)
ara_groups <- left_join(ara_groups, symbols)
Joining, by = "Gene"
ara_groups$Number <- NULL
head(ara_groups)
# A tibble: 6 x 4
  Orthogroup `A. thaliana` Gene      Symbol   
  <chr>      <chr>         <chr>     <chr>    
1 OG0000000  AT1G01250.1   AT1G01250 AT1G01250
2 OG0000000  AT1G04370.1   AT1G04370 ERF14    
3 OG0000000  AT1G06160.1   AT1G06160 ORA59    
4 OG0000000  AT1G12630.1   AT1G12630 AT1G12630
5 OG0000000  AT1G12890.1   AT1G12890 AT1G12890
6 OG0000000  AT1G19210.1   AT1G19210 AT1G19210
ara_joined <- left_join(ara_groups, functions, by=c(`A. thaliana`='Model_name'))

You know what? we shouldn’t directly compare a dicot (A. thaliana) with monocots (seagrasses), should also add another regular monocot (rice)

posi_groups <- groups %>% select(Orthogroup, `P. australis`) %>% filter(!is.na(`P. australis`)) %>% separate_rows(Orthogroup, `P. australis`, convert=T, sep = ', ')
amphi_groups <- groups %>% select(Orthogroup, `A. antarctica`) %>% filter(!is.na(`A. antarctica`)) %>% separate_rows(Orthogroup, `A. antarctica`, convert=T, sep = ', ')
zmar_groups <- groups %>%  select(Orthogroup, `Z. marina`) %>% filter(!is.na(`Z. marina`)) %>% separate_rows(Orthogroup, `Z. marina`, convert=T, sep = ', ')
zmuel_groups <- groups %>%  select(Orthogroup, `Z. muelleri`) %>% filter(!is.na(`Z. muelleri`)) %>% separate_rows(Orthogroup, `Z. muelleri`, convert=T, sep = ', ')
w_australis_groups <- groups %>%  select(Orthogroup, `W. australis`) %>% filter(!is.na(`W. australis`)) %>% separate_rows(Orthogroup, `W. australis`, convert=T, sep = ', ')
s_polyrhiza_groups <- groups %>%  select(Orthogroup, `S. polyrhiza`) %>% filter(!is.na(`S. polyrhiza`)) %>% separate_rows(Orthogroup, `S. polyrhiza`, convert=T, sep = ', ')
l_gibba_groups <- groups %>%  select(Orthogroup, `L. gibba`) %>% filter(!is.na(`L. gibba`)) %>% separate_rows(Orthogroup, `L. gibba`, convert=T, sep = ', ')
rice_groups<- groups %>%  select(Orthogroup, `O. sativa`) %>% filter(!is.na(`O. sativa`)) %>% separate_rows(Orthogroup, `O. sativa`, convert=T, sep = ', ')
big_joined <- ara_joined %>% mutate(
  present_in_zmuel = case_when(Orthogroup %in% unique(zmuel_groups$Orthogroup) ~ T,
                               TRUE ~ F),
  present_in_zmar = case_when(Orthogroup %in% unique(zmar_groups$Orthogroup) ~ T,
                              TRUE ~ F),
  present_in_amphi = case_when(Orthogroup %in% unique(amphi_groups$Orthogroup) ~ T,
                               TRUE ~ F),
  present_in_posi = case_when(Orthogroup %in% unique(posi_groups$Orthogroup) ~ T,
                              TRUE ~ F),
  present_in_waustralis = case_when(Orthogroup %in% unique(w_australis_groups$Orthogroup) ~ T,
                              TRUE ~ F),
  present_in_spolyrhiza = case_when(Orthogroup %in% unique(s_polyrhiza_groups$Orthogroup) ~ T,
                              TRUE ~ F),
  present_in_lgibba = case_when(Orthogroup %in% unique(l_gibba_groups$Orthogroup) ~ T,
                              TRUE ~ F),
  present_in_rice = case_when(Orthogroup %in% unique(rice_groups$Orthogroup) ~ T,
                              TRUE ~ F)
  )

Done :) Now we have a big table with all A. thaliana gene functions and whether these genes are present in clusters of the four seagrasses and three duckweeds.

Let’s also add counts - how often is this gene lost?

big_joined <- big_joined %>% mutate(Lost_in_Seagrasses = 4 - (present_in_posi + present_in_zmuel + present_in_zmar + present_in_amphi),
                      Lost_in_duckweeds=3 - (present_in_lgibba + present_in_spolyrhiza + present_in_waustralis),
                      Lost_in_both = (Lost_in_Seagrasses + Lost_in_duckweeds))
big_joined %>% writexl::write_xlsx('data/arabidopsis_gene_level_comparison.xlsx')
big_joined %>% filter_at(vars(starts_with('present')), any_vars(. == FALSE)) %>% writexl::write_xlsx('data/arabidopsis_gene_level_comparison_only_losts.xlsx')

That was about loss - can we also check for gene family extension using the A. thaliana genes?

Gene family extension

First, let’s count how many members each of these orthogroups has per species.

ara_count <- ara_joined %>% count(Orthogroup)
posi_count <- posi_groups %>% count(Orthogroup)
amphi_count <- amphi_groups %>% count(Orthogroup)
zmar_count <- zmar_groups %>% count(Orthogroup)
zmuel_count <- zmuel_groups %>% count(Orthogroup)
lgibba_count <- l_gibba_groups %>% count(Orthogroup)
s_polyrhi_count <- s_polyrhiza_groups %>% count(Orthogroup)
w_aus_count <- w_australis_groups %>% count(Orthogroup)
rice_count <- rice_groups %>% count(Orthogroup)
counts <- plyr::join_all(list(ara_count, posi_count, amphi_count, zmar_count, zmuel_count, lgibba_count, s_polyrhi_count, w_aus_count, rice_count), by='Orthogroup', type='left') 
names(counts) <- c('Orthogroup', 'A. thaliana', 'P. australis', 'A. antarctica', 'Z. marina', 'Z. muelleri', 'L. gibba', 'S. polyrhiza', 'W. australis', 'O. sativa')
head(counts)
  Orthogroup A. thaliana P. australis A. antarctica Z. marina Z. muelleri
1  OG0000000         112           19            23        76          76
2  OG0000001          81           49            41        85         116
3  OG0000002          97           60            61        16          23
4  OG0000003          88           12            13        51          58
5  OG0000005          64           31            33        31          50
6  OG0000006          53           21            17        28          30
  L. gibba S. polyrhiza W. australis O. sativa
1       69           48           84       113
2       96           59           63        86
3       56           20           35        84
4       55           35           35       131
5       36           35           53        59
6       60           21           28        53
joined_counts <- left_join(big_joined, counts, by='Orthogroup') %>% select(-starts_with('present')) %>% 
  select(-starts_with('Lost_i'))
joined_counts %>% writexl::write_xlsx('data/arabidopsis_gene_level_counts.xlsx')

FINAL version redoing - terrestrial vs aquatics vs seagrasses

Plotting all species separately is messy as I have so many species. I’m joining the species into three groups: aquatics, terrestrials, and seagrasses!

per_group_spec <-
  per_spec %>% mutate(group = case_when(
    name %in% c(
      'A. trichopada',
      'B. distachyon',
      'O. sativa',
      'P. patens',
      'P. trichocarpa',
      'S. moellendorffii',
      'A. thaliana',
      'T. parvula',
      'V. vinifera',
      'Z. mays'
    ) ~ 'Terrestrials',
    name %in% c(
      'C. reinhardtii',
      'L. gibba',
      'S. polyrhiza',
      'O. lucimarinus',
      'W. australis'
    ) ~ 'Aquatics',
    name %in% c('P. australis', 'Z. muelleri', 'Z. marina', 'A. antarctica') ~ 'Seagrasses'
  )) 
# now I want the data in this format:
# listInput <- list(one = c(1, 2, 3, 5, 7, 8, 11, 12, 13), two = c(1, 2, 4, 5, 
#   10), three = c(1, 5, 6, 7, 8, 9, 10, 12, 13))
groupx <- per_group_spec %>% 
  select(group, Orthogroup) %>% # turn the table around
  deframe() # convert to named vector
mylistgroup <- lapply(split(groupx, names(groupx)), unname) # yuck - ugly code to convert the named vector to a list
mylistgroup <- lapply(mylistgroup, unique)
xgroup <- upset(fromList(mylistgroup), order.by='freq', nsets = length(groups) - 1)
xgroup

MUCH better. The biggest chunk of orthogroups is not in aquatics or seagrasses with 13562 orthogroups. 3437 orthogroups are unique to aquatics, but not in seagrasses - 1183 orthogroups are unique in seagrasses, but not in aquatics. 404 + 3437 + 2217 orthogroups are not in terrestrials.

OK now we need to do the GOenrichment for those three groups.

Let’s also make an Venn plot, we have only three groups:

p <- ggVennDiagram(mylistgroup, label_alpha=0) + 
  scale_fill_gradientn(colours=wes_palette("Zissou1", 100, type = "continuous")) + 
  theme(legend.position = "none")
p

Nice!


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] ggVennDiagram_1.2.0 UpSetR_1.4.0        patchwork_1.1.1    
 [4] wesanderson_0.3.6   RColorBrewer_1.1-2  cowplot_1.1.1      
 [7] forcats_0.5.1       stringr_1.4.0       dplyr_1.0.7        
[10] purrr_0.3.4         readr_2.0.2         tidyr_1.1.4        
[13] tibble_3.1.5        ggplot2_3.3.5       tidyverse_1.3.1    
[16] workflowr_1.6.2    

loaded via a namespace (and not attached):
 [1] fs_1.5.0           sf_1.0-5           lubridate_1.8.0    bit64_4.0.5       
 [5] httr_1.4.2         rprojroot_2.0.2    tools_4.1.0        backports_1.2.1   
 [9] bslib_0.3.1        utf8_1.2.2         R6_2.5.1           KernSmooth_2.23-20
[13] DBI_1.1.1          colorspace_2.0-2   withr_2.4.2        tidyselect_1.1.1  
[17] gridExtra_2.3      bit_4.0.4          compiler_4.1.0     git2r_0.28.0      
[21] cli_3.0.1          rvest_1.0.2        xml2_1.3.2         labeling_0.4.2    
[25] sass_0.4.0         scales_1.1.1       classInt_0.4-3     proxy_0.4-26      
[29] digest_0.6.28      rmarkdown_2.11     pkgconfig_2.0.3    htmltools_0.5.2   
[33] dbplyr_2.1.1       fastmap_1.1.0      highr_0.9          rlang_0.4.12      
[37] readxl_1.3.1       rstudioapi_0.13    jquerylib_0.1.4    generics_0.1.1    
[41] farver_2.1.0       jsonlite_1.7.2     vroom_1.5.5        magrittr_2.0.1    
[45] Rcpp_1.0.7         munsell_0.5.0      fansi_0.5.0        lifecycle_1.0.1   
[49] stringi_1.7.5      whisker_0.4        yaml_2.2.1         plyr_1.8.6        
[53] grid_4.1.0         parallel_4.1.0     promises_1.2.0.1   crayon_1.4.1      
[57] haven_2.4.3        hms_1.1.1          knitr_1.36         pillar_1.6.4      
[61] reprex_2.0.1       glue_1.4.2         evaluate_0.14      modelr_0.1.8      
[65] vctrs_0.3.8        tzdb_0.1.2         httpuv_1.6.3       cellranger_1.1.0  
[69] gtable_0.3.0       assertthat_0.2.1   xfun_0.27          broom_0.7.9       
[73] e1071_1.7-9        later_1.3.0        class_7.3-19       RVenn_1.1.0       
[77] units_0.7-2        writexl_1.4.0      ellipsis_0.3.2