Last updated: 2019-06-25
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Knit directory: apaQTL/analysis/
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Unstaged changes:
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File | Version | Author | Date | Message |
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Rmd | 3e288f8 | brimittleman | 2019-06-25 | add tf analysis |
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
I want to understand what mechanisms could be driving the apaQTLs. T odo this I am going to look for TF motifs that are enriched for disruption in the apaQTLs. I am using https://ccg.epfl.ch/snp2tfbs/pwmviewer.php and am giving it the list of uniq rsids for the apaQTLs.
total_tf=read.table("../data/TF_motifdisruption/TotalTFenrich_output_27069.txt", stringsAsFactors = F, col.names = c("name", "genomeMatches","setMatches", "fractionHits", "enrichment","pval"))
total_tf_sig=total_tf %>% filter(pval<.05)
total_matches=read.table("../data/TF_motifdisruption/TotalTFmatchesAnn_output_27069.txt", stringsAsFactors = F,col.names = c("chr", "pos", "ref", "alt", "loc","gene", "score1", "score2", "snp", "TF"))
total_matches_sig=total_matches %>% filter(TF %in% total_tf_sig$name)
ggplot(total_matches_sig, aes(x=TF,fill=loc)) + geom_bar()+ labs(y="Number of SNP", title="Location of Enriched TF motifs \n Total apaQTLs")
nuclear_tf=read.table("../data/TF_motifdisruption/NuclearTFenrich_output_25094.txt", stringsAsFactors = F, col.names = c("name", "genomeMatches","setMatches", "fractionHits", "enrichment","pval"))
nuclear_tf_sig=nuclear_tf %>% filter(pval<.05)
nuclear_matches=read.table("../data/TF_motifdisruption/NuclearTFmatchesAnn_output_25094.txt", stringsAsFactors = F,col.names = c("chr", "pos", "ref", "alt", "loc","gene", "score1", "score2", "snp", "TF"))
nuclear_matches_sig=nuclear_matches %>% filter(TF %in% nuclear_tf_sig$name)
ggplot(nuclear_matches_sig, aes(x=TF,fill=loc)) + geom_bar()+theme(axis.text.x = element_text(angle = 90, hjust = 1)) +labs(y="Number of SNP", title="Location of Enriched TF motifs \n Nuclear apaQTLs")
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.3.0 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 pillar_1.3.1 compiler_3.5.1
[5] git2r_0.25.2 plyr_1.8.4 tools_3.5.1 digest_0.6.18
[9] lubridate_1.7.4 jsonlite_1.6 evaluate_0.12 nlme_3.1-137
[13] gtable_0.2.0 lattice_0.20-38 pkgconfig_2.0.2 rlang_0.3.1
[17] cli_1.0.1 rstudioapi_0.10 yaml_2.2.0 haven_1.1.2
[21] withr_2.1.2 xml2_1.2.0 httr_1.3.1 knitr_1.20
[25] hms_0.4.2 generics_0.0.2 fs_1.2.6 rprojroot_1.3-2
[29] grid_3.5.1 tidyselect_0.2.5 glue_1.3.0 R6_2.3.0
[33] readxl_1.1.0 rmarkdown_1.10 modelr_0.1.2 magrittr_1.5
[37] whisker_0.3-2 backports_1.1.2 scales_1.0.0 htmltools_0.3.6
[41] rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2 labeling_0.3
[45] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1
[49] crayon_1.3.4