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Knit directory: R_gene_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6b0f0ac | Philipp Bayer | 2022-03-30 | Fix tidyr update |
| html | 6101181 | Philipp Bayer | 2021-09-23 | Build site. |
| Rmd | 2a87744 | Philipp Bayer | 2021-09-23 | add more EDA |
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| Rmd | 01cf4a4 | Philipp Bayer | 2021-09-23 | add more EDA |
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| html | 2844fd3 | Philipp Bayer | 2021-02-09 | Add changes, add missing yield |
| html | e519122 | Philipp Bayer | 2020-12-11 | Build site. |
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| html | dae157b | Philipp Bayer | 2020-09-24 | Update of analysis |
| Rmd | 1b346e9 | Philipp Bayer | 2020-09-22 | More data! |
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| Rmd | c71005a | Philipp Bayer | 2020-09-18 | lots changes |
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library(tidyverse)Warning: package 'tidyverse' was built under R version 4.1.2-- 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 2.1.2 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 4.1.2Warning: 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(knitr)Warning: package 'knitr' was built under R version 4.1.1Here i make some of the tables for the manuscript.
files_to_make_tables_for <- list.files(path = './data', pattern = '*lst')Let’s print the counts of R-gene classes for each type, that will be table 1:
all_results <- c()
class_dict <- list()
for(i in seq_along(files_to_make_tables_for)) {
f <- files_to_make_tables_for[i]
if (f == 'Lee.preRGA.candidates.by.Blast.lst' ) {next}
if (f == 'Lee.RGA.candidates.lst') {next}
print(f)
fh <- read_tsv(paste('./data/', f, sep=''), col_names = c('Name','Class','Type'))
if (ncol(fh) == 2) {
# weird change in behaviour of tidyverse- used to be all NA by default
# when there were only 2 columns
fh$Type <- NA
}
fh <- fh %>% unite(United, Class, Type)
# remove NAs in files with 2 columns
fh$United <- gsub(pattern = '_NA', replacement = '', fh$United)
fh <- fh %>% mutate(Name = gsub('.t1', '', Name))
class_dict[[gsub('Lee.|.candidates.lst','',f)]] <- fh
all_results <- c(all_results, table(fh$United))
}[1] “Lee.NBS.candidates.lst” [1] “Lee.RLK.candidates.lst” [1] “Lee.RLP.candidates.lst” [1] “Lee.TMCC.candidates.lst”
kable(enframe(all_results, name='Class', value = 'Count'))| Class | Count |
|---|---|
| CN | 13 |
| CNL | 123 |
| NBS | 52 |
| NL | 95 |
| OTHER | 20 |
| TN | 22 |
| TNL | 99 |
| TX | 62 |
| RLK_lrr | 470 |
| RLK_lysm | 19 |
| RLK_other_receptor | 684 |
| RLP_lrr | 177 |
| RLP_lysm | 3 |
| TM-CC | 280 |
Let’s calculate the per-class gene variability.
pav_table <- read_tsv('./data/soybean_pan_pav.matrix_gene.txt.gz')Rows: 51414 Columns: 1111-- Column specification --------------------------------------------------------
Delimiter: "\t"
chr (1): Individual
dbl (1110): AB-01, AB-02, BR-01, BR-02, BR-03, BR-04, BR-05, BR-06, BR-07, B...
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.#t_pav_table <- as_tibble(cbind(nms = names(pav_table), t(pav_table)))
t_pav_table <- pav_table %>% pivot_longer(cols= -1) %>% pivot_wider(names_from = "Individual",values_from = "value") %>% rename('Individual'='name')names <- c()
presences <- c()
for (i in seq_along(t_pav_table)){
if ( i == 1) next
thisind <- colnames(t_pav_table)[i]
pavs <- t_pav_table[[i]]
presents <- sum(strtoi(pavs))
names <- c(names, thisind)
presences <- c(presences, presents)
}
res_tibb <- new_tibble(list(names = names, presences = presences))res_tibb now stores for each gene, in how many individuals it is present. We have 1110 individuals, so all genes with presences < 1110 are variable.
res_tibb <- res_tibb %>% mutate(type = case_when(presences == 1110 ~ 'core',
TRUE ~ 'variable'))nbs_joined <- left_join(class_dict[['NBS']], res_tibb, by = c('Name'='names'))There are 486 NLR genes out of which 320 are core and 166 are variable.
rlk_joined <- left_join(class_dict[['RLK']], res_tibb, by = c('Name'='names'))There are 1173 RLK genes out of which 1075 are core and 98 are variable.
rlp_joined <- left_join(class_dict[['RLP']], res_tibb, by = c('Name'='names'))There are 180 RLK genes out of which 125 are core and 55 are variable.
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] knitr_1.36 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.1.2 tidyr_1.1.4 tibble_3.1.5
[9] 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] bit_4.0.4 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_2.5.0 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] vroom_1.5.7 cli_3.2.0 stringi_1.7.5 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 bit64_4.0.5
[57] glue_1.6.2 hms_1.1.1 parallel_4.1.0 fastmap_1.1.0
[61] yaml_2.2.1 colorspace_2.0-2 rvest_1.0.2 haven_2.4.3
[65] sass_0.4.0