Last updated: 2020-03-07

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/upsetter_DF.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd c6a11f0 brimittleman 2020-03-07 add expression indep
html 22926ec brimittleman 2020-03-06 Build site.
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 ──
✔ tibble  2.1.1       ✔ purrr   0.3.2  
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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
write.table(GenesWint_g, "../data/bioGRID/GeneswInteractions.txt",col.names = T, row.names = F, quote = F)
ggplot(NucResAll,aes(x=dAPA, y=log10(nInt +1))) + geom_boxplot() + stat_compare_means()

Version Author Date
22926ec brimittleman 2020-03-06

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