Last updated: 2021-04-15

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Knit directory: Amphibolis_Posidonia_Comparison/

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Rmd 49dcbb8 Philipp Bayer 2021-04-15 wflow_publish(files = list.files(“analysis/”, pattern = "*Rmd", 
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(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())
********************************************************
theme_set(theme_cowplot())
library(RColorBrewer)
library(patchwork)

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

    align_plots
library(UpSetR)
groups <- read_tsv('data/Orthogroups.tsv') 
Parsed with column specification:
cols(
  Orthogroup = col_character(),
  Amphibolis_final.genome.scf.bigger1kbp.all.maker.proteins = col_character(),
  Atrichopoda_291_v1.0.protein_primaryTranscriptOnly = col_character(),
  Bdistachyon_556_v3.2.protein_primaryTranscriptOnly = col_character(),
  Creinhardtii_281_v5.6.protein_primaryTranscriptOnly = col_character(),
  Lemna_gibba.prots = col_character(),
  Osativa_323_v7.0.protein_primaryTranscriptOnly = col_character(),
  P_australis_genome_bigger1kbp.all.maker.proteins = col_character(),
  Ppatens_318_v3.3.protein_primaryTranscriptOnly = col_character(),
  Ptrichocarpa_533_v4.1.protein_primaryTranscriptOnly = col_character(),
  Smoellendorffii_91_v1.0.protein_primaryTranscriptOnly = col_character(),
  Sp9509_oxford_v3.proteins = col_character(),
  TAIR10_pep_20101214 = col_character(),
  TpV84ORFs.protein = col_character(),
  Vvinifera_457_v2.1.protein_primaryTranscriptOnly = col_character(),
  Zmays_493_RefGen_V4.protein_primaryTranscriptOnly = col_character(),
  Zmu_v1_protein = col_character(),
  ZosmaV2_prot_LATEST = col_character(),
  mRNA_ostlu_active_pep_20170531 = col_character()
)
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')
# 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)
`summarise()` ungrouping output (override with `.groups` argument)
per_spec %>% 
  filter(Orthogroup %in% species_specific_orthos$Orthogroup) %>% 
  group_by(name) %>% 
  count() %>% 
  arrange(n) %>% 
  knitr::kable()
name n
A. antarctica 74
S. polyrhiza 168
O. lucimarinus 201
P. australis 268
T. parvula 270
L. gibba 293
Z. marina 325
P. trichocarpa 491
A. thaliana 496
B. distachyon 507
Z. muelleri 632
V. vinifera 690
A. trichopada 847
C. reinhardtii 1109
Z. mays 1113
S. moellendorffii 1344
O. sativa 1422
P. patens 1572 
species_names <- per_spec %>% 
  group_by(Orthogroup) %>% 
  group_by(Orthogroup) %>% 
  summarise(catty = paste0(sort(name), collapse = ', '))
`summarise()` ungrouping output (override with `.groups` argument)

How many orthogroups are shared between the four seagrasses?

species_names %>% dplyr::filter(catty == 'A. antarctica, P. australis, Z. marina, Z. muelleri') %>% nrow()
[1] 31
species_names %>% dplyr::filter(catty == 'A. antarctica, P. australis') %>% nrow()
[1] 140
species_names %>% dplyr::filter(catty == 'A. antarctica, P. australis, Z. marina') %>% nrow()
[1] 5
species_names %>% dplyr::filter(catty == 'A. antarctica, P. australis, Z. muelleri') %>% nrow()
[1] 53
species_names %>% dplyr::filter(catty == 'P. australis, Z. marina, Z. muelleri') %>% nrow()
[1] 32

I should automate this…. and I can!

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

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

This can’t be right, how are 6532 clusters suddenly shared…. that’s less than the overall. Need to subset the original clusters, NOT the species.


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] UpSetR_1.4.0         patchwork_1.0.0      RColorBrewer_1.1-2  
 [4] cowplot_1.0.0        forcats_0.5.0        stringr_1.4.0       
 [7] dplyr_1.0.0          purrr_0.3.4          readr_1.3.1         
[10] tidyr_1.1.0          tibble_3.0.2         ggplot2_3.3.2       
[13] tidyverse_1.3.0      workflowr_1.6.2.9000

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5       lubridate_1.7.9  lattice_0.20-41  getPass_0.2-2   
 [5] ps_1.3.4         assertthat_0.2.1 rprojroot_1.3-2  digest_0.6.25   
 [9] plyr_1.8.6       R6_2.4.1         cellranger_1.1.0 backports_1.1.10
[13] reprex_0.3.0     evaluate_0.14    highr_0.8        httr_1.4.2      
[17] pillar_1.4.4     rlang_0.4.7      readxl_1.3.1     rstudioapi_0.11 
[21] whisker_0.4      callr_3.4.4      blob_1.2.1       rmarkdown_2.3   
[25] labeling_0.3     munsell_0.5.0    broom_0.5.6      compiler_3.6.3  
[29] httpuv_1.5.4     modelr_0.1.8     xfun_0.17        pkgconfig_2.0.3 
[33] htmltools_0.5.0  tidyselect_1.1.0 gridExtra_2.3    fansi_0.4.1     
[37] crayon_1.3.4     dbplyr_1.4.4     withr_2.2.0      later_1.1.0.1   
[41] grid_3.6.3       nlme_3.1-148     jsonlite_1.7.1   gtable_0.3.0    
[45] lifecycle_0.2.0  DBI_1.1.0        git2r_0.27.1     magrittr_1.5    
[49] scales_1.1.1     cli_2.0.2        stringi_1.5.3    farver_2.0.3    
[53] fs_1.5.0.9000    promises_1.1.1   xml2_1.3.2       ellipsis_0.3.1  
[57] generics_0.0.2   vctrs_0.3.1      tools_3.6.3      glue_1.4.2      
[61] hms_0.5.3        processx_3.4.4   yaml_2.2.1       colorspace_1.4-1
[65] rvest_0.3.5      knitr_1.29       haven_2.3.1