Last updated: 2019-01-17

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    File Version Author Date Message
    Rmd 7eaae54 Briana Mittleman 2019-01-17 select peaks to use in deeptools plot

I want to show RNA seq vs 3’ seq in peaks that are internal.

To do this I need to pull in the peaks (use the filtered ones from peakQCPlots) to get peaks that are used but are not the most distal for the gene. I need to split by strand when i do this becasue most distal is different in both cases.

Load Libraries

library(data.table)
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()   masks data.table::between()
✖ dplyr::filter()    masks stats::filter()
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✖ dplyr::lag()       masks stats::lag()
✖ dplyr::last()      masks data.table::last()
✖ purrr::transpose() masks data.table::transpose()
library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

Load peaks for total and nuclear post filter:

total_PeakUsMean=read.table("../data/PeakUsage/PeakUsageMeanPost5percFilter.Total.txt", head = T)
nuclear_PeakUsMean=read.table("../data/PeakUsage/PeakUsageMeanPost5percFilter.Nuclear.txt", head=T)

Seperate positive and negative:

  • Total
total_PeakUsMean_pos=total_PeakUsMean %>% filter(strand=="+")
total_PeakUsMean_neg=total_PeakUsMean %>% filter(strand=="-")
  • Nuclear
nuclear_PeakUsMean_pos=nuclear_PeakUsMean %>% filter(strand=="+")
nuclear_PeakUsMean_neg=nuclear_PeakUsMean %>% filter(strand=="-")

Group by gene and keep internal (remove genes with only 1)

  • For positive strand keep bottom one with top_n() peak # (most internal)
total_PeakUsMean_pos_internal=total_PeakUsMean_pos %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(1,peak)

nuclear_PeakUsMean_pos_internal=nuclear_PeakUsMean_pos %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(1,peak)

*For negative strand use top_n(-1)

total_PeakUsMean_neg_internal=total_PeakUsMean_neg %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(-1,peak)

nuclear_PeakUsMean_neg_internal=nuclear_PeakUsMean_neg %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(-1,peak)

Bind the total and nuclear rows back together:

Total

total_PeakUsMean_internal=as.data.frame(rbind(total_PeakUsMean_pos_internal,total_PeakUsMean_neg_internal)) %>% arrange(chr, start, end)

Nuclear:

nuclear_PeakUsMean_internal=as.data.frame(rbind(nuclear_PeakUsMean_pos_internal,nuclear_PeakUsMean_neg_internal)) %>% arrange(chr, start, end)

Use these peak numbers to filter the bed file that I use for deep tools. I can do this in python with a dictionary of the peaks to keep.

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2    cowplot_0.9.3     workflowr_1.1.1  
 [4] forcats_0.3.0     stringr_1.3.1     dplyr_0.7.6      
 [7] purrr_0.2.5       readr_1.1.1       tidyr_0.8.1      
[10] tibble_1.4.2      ggplot2_3.0.0     tidyverse_1.2.1  
[13] data.table_1.11.8

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     knitr_1.20        broom_0.5.0      
[25] Rcpp_0.12.19      backports_1.1.2   scales_1.0.0     
[28] jsonlite_1.5      hms_0.4.2         digest_0.6.17    
[31] stringi_1.2.4     grid_3.5.1        rprojroot_1.3-2  
[34] cli_1.0.1         tools_3.5.1       magrittr_1.5     
[37] lazyeval_0.2.1    crayon_1.3.4      whisker_0.3-2    
[40] pkgconfig_2.0.2   xml2_1.2.0        lubridate_1.7.4  
[43] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
[46] rstudioapi_0.8    R6_2.3.0          nlme_3.1-137     
[49] git2r_0.23.0      compiler_3.5.1

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