Last updated: 2020-03-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
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Rmd 680d435 brimittleman 2020-03-06 add ptm analysis 
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(workflowr)
This is workflowr version 1.6.0
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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Looking at protien interactions. https://downloads.thebiogrid.org/BioGRID/Release-Archive/BIOGRID-3.5.182/

Are genes with dAPA more likely to be in genes with known protein protein interactions. Given the regulatory elements in the 3’ UTR. This could help us understand a mechanism.

( interactions, chemical associations, and post-translational modifications (PTM))

mkdir ../data/bioGRID

Look at data:

Fix colnames

Biogrid=read_tsv("../data/bioGRID/BIOGRID-ORGANISM-Homo_sapiens-3.5.182.tab2.txt")
Parsed with column specification:
cols(
  .default = col_character(),
  `#BioGRID Interaction ID` = col_double(),
  `Entrez Gene Interactor A` = col_double(),
  `Entrez Gene Interactor B` = col_double(),
  `BioGRID ID Interactor A` = col_double(),
  `BioGRID ID Interactor B` = col_double(),
  `Pubmed ID` = col_double(),
  `Organism Interactor A` = col_double(),
  `Organism Interactor B` = col_double()
)
See spec(...) for full column specifications.
colnames(Biogrid)= c( "BioGRID_Interaction_ID",  "Entrez_Gene_Interactor_A", "Entrez_Gene_Interactor_B", "BioGRID_ID_Interactor_A",  "BioGRID_ID_Interactor_B"   ,   "Systematic_Name_Interactor_A","Systematic_Name_Interactor_B", "Official_Symbol_Interactor_A", "Official_Symbol_Interactor_B","Synonyms_Interactor_A", "Synonyms_Interactor_B" , "Experimental_System", "Experimental_System_Type" ,"Author" , "Pubmed_ID" ,"Organism_Interactor_A", "Organism_Interactor_B", "Throughput","Score", "Modification" , "Phenotypes","Qualifications", "Tags" , "Source Database" )

Select the official names for the interactors:

Biogridsmall=Biogrid %>% dplyr::select(Official_Symbol_Interactor_A, Official_Symbol_Interactor_B,Score, Modification, Phenotypes, Tags)

I need a way to remove duplicates. I can do this by making unordered sets of these. I will need the uniq sets.

Make a set with the pasted version of A:B and B:A, keep the unique set

BioGridsets=Biogridsmall %>% mutate(Afirst=paste(Official_Symbol_Interactor_A, Official_Symbol_Interactor_B, sep="_:_"), Bfirst=paste(Official_Symbol_Interactor_B, Official_Symbol_Interactor_A, sep="_:_")) 

Allsets= as.data.frame(c(BioGridsets$Afirst, BioGridsets$Bfirst)) %>%  unique()

colnames(Allsets)=c("Interaction")


AllGenes=as.data.frame(c(Biogridsmall$Official_Symbol_Interactor_A, Biogridsmall$Official_Symbol_Interactor_B)) %>% unique()

colnames(AllGenes)=c("Genes")

I want to know if my genes are in either of these sets. I also need the set of all genes that are involved.

Allsets_sep= Allsets %>% separate(Interaction, into=c("a","b"), sep="_:_")

Get all of the genes together in one column (not unique) , group by the gene and count how many interactions

GenesWint= as.data.frame(c(Allsets_sep$a, Allsets_sep$b))
colnames(GenesWint)= c("gene")
GenesWint_g= GenesWint %>% group_by(gene) %>% summarise(nInt=n())

Join this with the genes I test.

NucRes=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% group_by(gene, SigPAU2) %>% summarise(N=n()) %>% spread(SigPAU2,N) %>% replace_na(list(Yes=0)) %>% mutate(dAPA=ifelse(Yes>=1, "Yes", "No")) %>% dplyr::select(-Yes, -No)

NucResAll= NucRes  %>% left_join(GenesWint_g, by="gene") %>%  replace_na(list(nInt=0)) 
Warning: Column `gene` joining character vector and factor, coercing into
character vector
ggplot(NucResAll,aes(x=dAPA, y=log10(nInt +1))) + geom_boxplot() + stat_compare_means()

Does not look like this can explain the differences.

Enriched for non 0?

NucResAll_g= NucResAll %>% mutate(HasInteraction=ifelse(nInt>0, "Yes", "No")) %>% group_by(dAPA, HasInteraction) %>% summarise(nWithSet=n())

NucResAll_g
# A tibble: 4 x 3
# Groups:   dAPA [2]
  dAPA  HasInteraction nWithSet
  <chr> <chr>             <int>
1 No    No                  814
2 No    Yes                5581
3 Yes   No                  280
4 Yes   Yes                1869
5581/(5581+814)
[1] 0.8727131
1869/(1869+280)
[1] 0.8697068

Doesnt look like differentail are more likely to have an interaction.

filter for modifications

Biogridsmall %>% dplyr::select(Modification) %>% unique()
# A tibble: 19 x 1
   Modification          
   <chr>                 
 1 -                     
 2 No Modification       
 3 Proteolytic Processing
 4 Phosphorylation       
 5 Ubiquitination        
 6 Methylation           
 7 Dephosphorylation     
 8 Sumoylation           
 9 Deacetylation         
10 Acetylation           
11 Glycosylation         
12 Deubiquitination      
13 Demethylation         
14 Ribosylation          
15 Desumoylation         
16 Nedd(Rub1)ylation     
17 Deneddylation         
18 FAT10ylation          
19 Neddylation           

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    tidyverse_1.2.1
 [9] workflowr_1.6.0 ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1