Last updated: 2021-06-22
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Knit directory: muse/
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|---|---|---|---|---|
| Rmd | 8ae8e21 | Dave Tang | 2021-06-22 | Post on splitting a single column of annotations |
Use importbio to load a VCF file (or load any file that has a single column containing multiple annotations that are consistently defined).
library(importbio)
my_vcf <- importvcf("https://raw.githubusercontent.com/davetang/learning_vcf_file/master/eg/Pfeiffer.vcf")
head(my_vcf)# A tibble: 6 x 10
vid chrom pos id ref alt qual filter info type
<chr> <fct> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1_86651~ 1 866511 rs607~ C CCCCT 258.~ PASS AC=2;AF=1.00;AN=2~ ins
2 1_87931~ 1 879317 rs752~ C T 150.~ PASS AC=1;AF=0.50;AN=2~ snv
3 1_87948~ 1 879482 . G C 484.~ PASS AC=1;AF=0.50;AN=2~ snv
4 1_88039~ 1 880390 rs374~ C A 288.~ PASS AC=1;AF=0.50;AN=2~ snv
5 1_88162~ 1 881627 rs227~ G A 486.~ PASS AC=1;AF=0.50;AN=2~ snv
6 1_88409~ 1 884091 rs752~ C G 65.46 PASS AC=1;AF=0.50;AN=2~ snv We will be functions within the tidyverse.
library(tidyverse)-- Attaching packages --------------------------------------- tidyverse 1.3.1 --v ggplot2 3.3.3 v purrr 0.3.4
v tibble 3.1.1 v dplyr 1.0.6
v tidyr 1.1.3 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.1-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Use separate_rows to create a new row for each annotation. The mutate call is to remove a trailing semicolon, if it exists.
library(tidyverse)
my_vcf %>%
mutate(info = sub(pattern = ";$", replacement = "", x = .data$info)) %>%
separate_rows(info, sep = ";\\s*") %>%
head()# A tibble: 6 x 10
vid chrom pos id ref alt qual filter info type
<chr> <fct> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1_866511_C_CCC~ 1 866511 rs60722469 C CCCCT 258.~ PASS AC=2 ins
2 1_866511_C_CCC~ 1 866511 rs60722469 C CCCCT 258.~ PASS AF=1.00 ins
3 1_866511_C_CCC~ 1 866511 rs60722469 C CCCCT 258.~ PASS AN=2 ins
4 1_866511_C_CCC~ 1 866511 rs60722469 C CCCCT 258.~ PASS DB ins
5 1_866511_C_CCC~ 1 866511 rs60722469 C CCCCT 258.~ PASS DP=11 ins
6 1_866511_C_CCC~ 1 866511 rs60722469 C CCCCT 258.~ PASS FS=0.0~ ins Use separate to split key-value annotation pair (separated by an equal sign) into two columns. Missing values will have a NA value.
my_vcf %>%
mutate(info = sub(pattern = ";$", replacement = "", x = .data$info)) %>%
separate_rows(info, sep = ";\\s*") %>%
separate(info, c('key', 'value'), sep = "=") %>%
head(10)# A tibble: 10 x 11
vid chrom pos id ref alt qual filter key value type
<chr> <fct> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS AC 2 ins
2 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS AF 1.00 ins
3 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS AN 2 ins
4 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS DB <NA> ins
5 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS DP 11 ins
6 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS FS 0.000 ins
7 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS HRun 0 ins
8 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS Haploty~ 41.3~ ins
9 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS MQ0 0 ins
10 1_866511~ 1 866511 rs6072~ C CCCCT 258.62 PASS MQ 61.94 ins Finally, use pivot_wider to convert the data back into wide format. In the example below, I show three new columns created from splitting the info column.
my_vcf %>%
mutate(info = sub(pattern = ";$", replacement = "", x = .data$info)) %>%
separate_rows(info, sep = ";\\s*") %>%
separate(info, c('key', 'value'), sep = "=") %>%
distinct() %>% # remove duplication annotations, if any
filter(key == "AC" | key == "DP" | key == "MQ") %>%
pivot_wider(id_cols = vid, names_from = key, values_from = value)Warning: Expected 2 pieces. Missing pieces filled with `NA` in 35503 rows [4,
17, 48, 64, 80, 96, 112, 128, 144, 159, 172, 188, 204, 220, 236, 251, 266, 282,
298, 314, ...].# A tibble: 37,708 x 4
vid AC DP MQ
<chr> <chr> <chr> <chr>
1 1_866511_C_CCCCT 2 11 61.94
2 1_879317_C_T 1 21 60.00
3 1_879482_G_C 1 48 59.13
4 1_880390_C_A 1 29 56.93
5 1_881627_G_A 1 33 60.00
6 1_884091_C_G 1 12 53.22
7 1_884101_A_C 1 12 53.22
8 1_892460_G_C 1 152 58.85
9 1_897730_C_T 1 21 58.89
10 1_909238_G_C 1 19 60.00
# ... with 37,698 more rowsseparate_rows to split a single column with multiple annotations into rowsseparate to split a key-value annotation pair into two columnspivot_wider to convert the table in long format back to wide format
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
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
system code page: 932
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6
[4] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[7] tibble_3.1.1 ggplot2_3.3.3 tidyverse_1.3.1
[10] importbio_0.0.0.9000 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.22 haven_2.4.1 colorspace_2.0-1
[5] vctrs_0.3.8 generics_0.1.0 htmltools_0.5.1.1 yaml_2.2.1
[9] utf8_1.2.1 rlang_0.4.11 later_1.2.0 pillar_1.6.1
[13] withr_2.4.2 glue_1.4.2 DBI_1.1.1 dbplyr_2.1.1
[17] readxl_1.3.1 modelr_0.1.8 lifecycle_1.0.0 cellranger_1.1.0
[21] munsell_0.5.0 gtable_0.3.0 rvest_1.0.0 evaluate_0.14
[25] knitr_1.33 httpuv_1.6.1 ps_1.6.0 curl_4.3.1
[29] fansi_0.4.2 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_1.1.0 digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2
[41] grid_4.0.5 cli_2.5.0 tools_4.0.5 magrittr_2.0.1
[45] crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.2
[49] xml2_1.3.2 reprex_2.0.0 lubridate_1.7.10 httr_1.4.2
[53] assertthat_0.2.1 rmarkdown_2.8 rstudioapi_0.13 R6_2.5.0
[57] git2r_0.28.0 compiler_4.0.5