Last updated: 2021-09-23
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Knit directory: R_gene_analysis/
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|---|---|---|---|---|
| Rmd | 01cf4a4 | Philipp Bayer | 2021-09-23 | add more EDA |
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| Rmd | c71005a | Philipp Bayer | 2020-09-18 | lots changes |
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| html | ca65f66 | Philipp Bayer | 2020-09-18 | Build site. |
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library(tidyverse)-- Attaching packages --------------------------------------- tidyverse 1.3.1 --v ggplot2 3.3.5 v purrr 0.3.4
v tibble 3.1.2 v dplyr 1.0.7
v tidyr 1.1.3 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.1Warning: package 'ggplot2' was built under R version 4.0.5Warning: package 'tibble' was built under R version 4.0.5Warning: package 'dplyr' was built under R version 4.0.5-- 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 4.0.5Here 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 == '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'))
fh <- fh %>% unite(United, Class, Type)
# remove NAs in files with 2 columns
fh$United <- gsub(pattern = '_NA', replacement = '', fh$United)
class_dict[[gsub('Lee.|.candidates.lst','',f)]] <- fh
all_results <- c(all_results, table(fh$United))
}[1] “Lee.NBS.candidates.lst” [1] “Lee.preRGA.candidates.by.Blast.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 |
| NA | 14358 |
| 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')
-- Column specification --------------------------------------------------------
cols(
.default = col_double(),
Individual = col_character()
)
i Use `spec()` for the full column specifications.t_pav_table <- as_tibble(cbind(nms = names(pav_table), t(pav_table)))Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
Using compatibility `.name_repair`.names(t_pav_table) <- t_pav_table[1,]Warning: The `value` argument of `names<-` must be a character vector as of
tibble 3.0.0.t_pav_table <- t_pav_table %>% filter(Individual != 'Individual')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))Warning: The `nrow` argument of `new_tibble()` can't be missing as of tibble 2.0.0.
`x` must be a scalar integer.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 122 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 89 are variable.
rlp_joined <- left_join(class_dict[['RLP']], res_tibb, by = c('Name'='names'))There are rnrow(rlp_joined)` RLK genes out of which 125 are core and 45 are variable.
sessionInfo()R version 4.0.3 (2020-10-10)
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] knitr_1.33 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2
[9] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.22 bslib_0.2.4 haven_2.4.0
[5] colorspace_2.0-2 vctrs_0.3.8 generics_0.1.0 htmltools_0.5.1.1
[9] yaml_2.2.1 utf8_1.2.1 rlang_0.4.11 jquerylib_0.1.4
[13] later_1.2.0 pillar_1.6.1 withr_2.4.2 glue_1.4.2
[17] DBI_1.1.1 dbplyr_2.1.1 readxl_1.3.1 modelr_0.1.8
[21] lifecycle_1.0.0 cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0
[25] rvest_1.0.0 evaluate_0.14 httpuv_1.6.0 fansi_0.5.0
[29] highr_0.9 broom_0.7.6 Rcpp_1.0.6 promises_1.2.0.1
[33] backports_1.2.1 scales_1.1.1 jsonlite_1.7.2 fs_1.5.0
[37] hms_1.0.0 digest_0.6.27 stringi_1.6.2 rprojroot_2.0.2
[41] grid_4.0.3 cli_3.0.0 tools_4.0.3 magrittr_2.0.1
[45] sass_0.3.1 crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[49] ellipsis_0.3.2 xml2_1.3.2 reprex_2.0.0 lubridate_1.7.10
[53] rstudioapi_0.13 assertthat_0.2.1 rmarkdown_2.7 httr_1.4.2
[57] R6_2.5.0 git2r_0.28.0 compiler_4.0.3