Last updated: 2019-06-28
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Knit directory: apaQTL/analysis/
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File | Version | Author | Date | Message |
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Rmd | f00669b | brimittleman | 2019-06-28 | add GO |
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ──────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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I want to do a quick gene set enrichemnt for the apaQTLs using http://cbl-gorilla.cs.technion.ac.il. I need to get the genes we test for apaQTLs in each fraction as a background set and those we find as significant as the test set.
totQTL=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt", header = T, stringsAsFactors = F) %>% select(Gene) %>% unique()
write.table(totQTL, file="../data/apaQTLs/TotalapaQTLGenes.txt", col.names = F, row.names = F, quote = F)
nucQTL=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", stringsAsFactors = F, header = T) %>% select(Gene) %>% unique()
write.table(nucQTL, file="../data/apaQTLs/NuclearapaQTLGenes.txt", col.names = F, row.names = F, quote = F)
Genes we tested:
tottested=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total_permResBH.txt", header = T, stringsAsFactors = F) %>% separate(pid, into=c("chr", "start", "end", "peakID"),sep=":") %>% separate(peakID, into=c("gene", "loc", "strand", "PAS"), sep="_") %>% select(gene) %>% unique()
write.table(tottested, file="../data/apaQTLs/TestedTotalapaQTLGenes.txt", col.names = F, row.names = F, quote = F)
nuctested=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", header = T, stringsAsFactors = F) %>% separate(pid, into=c("chr", "start", "end", "peakID"),sep=":") %>% separate(peakID, into=c("gene", "loc", "strand", "PAS"), sep="_") %>% select(gene) %>% unique()
write.table(nuctested, file="../data/apaQTLs/TestedNuclearapaQTLGenes.txt", col.names = F, row.names = F, quote = F)
Total res:
Processes - cellular detoxification of aldehyde, developmental growth,growth, nucleotide transmembrane transport,cellular amide metabolic process function- aminopeptidase activity, nucleotide transmembrane transporter activity, metalloaminopeptidase activity, SNAP receptor activity component- SPOTS complex, endoplasmic reticulum palmitoyltransferase complex, protein-cysteine S-palmitoyltransferase complex, serine C-palmitoyltransferase complex
Nuclear res:
no results
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1 workflowr_1.4.0
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 stringi_1.2.4
[45] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1 crayon_1.3.4