Last updated: 2020-01-23

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
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    Modified:   analysis/speciesSpecific.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 95a319f brimittleman 2020-01-24 add hypergeo pvalues
html 8536528 brimittleman 2020-01-22 Build site.
Rmd 07fd459 brimittleman 2020-01-22 DF for upset and translation protein ven

In this analysis I will use the UpSetR package to look at all of the differential gene regulation phenotype results in one plot. This should be easier to visualize than the venn diagrams.

library(UpSetR)
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
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()
✖ dplyr::lag()    masks stats::lag()

Input the datasets:

#protein
Protein=read.table("../data/Khan_prot/HC_SigProtein.txt", header = T, stringsAsFactors = F)
#trans
Translation=read.table("../data/Wang_ribo/HC_SigTranslation.txt", header = T, stringsAsFactors = F)
#expression
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DEgenes=read.table("../data/DiffExpression/DE_genes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name)
#nuclear apa  
NucAPA=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T,stringsAsFactors = F)

DSgenes=read.table("../data/DiffSplice_liftedJunc/orderedGeneListFixed.txt",stringsAsFactors = F, col.names = "DS")

Create a named list object

listInput_nucOnly <- list(DE=DEgenes$Gene.name, DS=DSgenes$DS, DAPA=NucAPA$gene, DT=Translation$Gene, DP=Protein$gene.symbol)

upset(fromList(listInput_nucOnly), order.by = "freq", keep.order = T,empty.intersections = "on")

Version Author Date
8536528 brimittleman 2020-01-22

Add colors for certain queries:

#upset(movies, queries = list(list(query = intersects, params = list("Drama", 
#    "Comedy", "Action"), color = "orange", active = T), list(query = intersects, 
 #   params = list("Drama"), color = "red", active = F), list(query = intersects, 
 #   params = list("Action", "Drama"), active = T)))


upset(fromList(listInput_nucOnly), queries = list(list(query=intersects, params=list("DAPA", "DT", "DP"), color="red", active=T,query.name="APA,Ribo, Protein"),list(query=intersects, params=list("DE", "DT", "DP"), color="orange", active=T, query.name="Expression,Ribo, Protein"), list(query=intersects, params=list("DS", "DT", "DP"), color="green", active=T,query.name="Splicing ,Ribo, Protein"),list(query=intersects, params=list("DAPA", "DT"), color="blue", active=T, query.name="APA,Ribo") ,list(query=intersects, params=list("DAPA", "DP"), color="purple", active=T, query.name="APA, Protein")), order.by = "freq", query.legend = "bottom")

Version Author Date
8536528 brimittleman 2020-01-22

Will use hypergeometric to test overlaps.

phyper(success in sample, sucesss in possible, failure possible, sample size)

Success is the overlap, de, not DE, sample size is the apagenes tested in DE

I need the full lists for this. Not just the significant ones.

I will start with expression and apa. I need all of the genes tested for both.

DEgenestested=read.table("../data/DiffExpression/DE_Testedgenes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name)
apaTested=read.table("../data/DiffIso_Nuclear_DF/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") %>% separate(cluster, into=c("chr", "Gene.name"),sep=":")

DEtestedandAPA=NucAPA %>%rename("Gene.name"=gene) %>% inner_join(DEgenestested, by="Gene.name") %>% nrow()
DeandAPA=NucAPA %>%rename("Gene.name"=gene) %>%  inner_join(DEgenes, by="Gene.name")%>% nrow()
NotDe= nrow(DEgenestested)-nrow(DEgenes)

  
phyper(DeandAPA, nrow(DEgenes), NotDe, DEtestedandAPA,lower.tail=F)
[1] 0.0009561245

Translation:
Success is the overlap, T, not T, sample size is the apagenes tested in DE

TranslationTested=read.table("../data/Wang_ribo/HC_AllTestedTranslation.txt",header = T,stringsAsFactors = F) %>% rename("gene"=Gene)
TranslationNotTE= TranslationTested %>% filter(HvC.FDR>=.05)
#actual overlap
TeandAPA=Translation %>% rename("gene"=Gene) %>% inner_join(NucAPA,by="gene")
TranslationTestedandAPA= TranslationTested %>% inner_join(NucAPA,by="gene")

phyper(nrow(TeandAPA), nrow(Translation), nrow(TranslationNotTE), nrow(TranslationTestedandAPA),lower.tail=F)
[1] 0.01307088

Protein

Success is the overlap, dp, not no dp, sample size is the apagenes tested in dp

ProtTested=read.table("../data/Khan_prot/HC_AlltestedProtein.txt",header = T,stringsAsFactors = F) %>% rename("gene"=gene.symbol)
nrow(ProtTested)
[1] 3390
ProtNotPE= ProtTested %>% filter(HC.qvalues.protein>=.05)
nrow(ProtNotPE)
[1] 2128
#actual overlap
PEandAPA=Protein %>% rename("gene"=gene.symbol) %>% inner_join(NucAPA,by="gene")
nrow(PEandAPA)
[1] 214
ProtTestedandAPA= ProtTested %>% inner_join(NucAPA,by="gene")
nrow(ProtTestedandAPA)
[1] 593
phyper(nrow(PEandAPA), nrow(Protein), nrow(ProtNotPE), nrow(ProtTestedandAPA),lower.tail=F)
[1] 0.7201916

APA, protein, and translation:

possible hits, not possible

Success is the overlap, dp and dt, not dp and dt, sample size is the apagenes tested in both dp and dt

ApaProtTrans=PEandAPA  %>% inner_join(TeandAPA, by="gene")

PeandTE=Translation %>% inner_join(Protein) %>% rename("gene"=Gene)
Joining, by = "ENSG"
#not dp and dt is all tested in both - pe and te set
PeandTEtested= ProtTested %>% full_join(TranslationTested, by="gene") %>% anti_join(PeandTE,by="gene")
  
apaTestedinboth= ProtTested %>% full_join(TranslationTested, by="gene") %>% inner_join(NucAPA, by="gene")

phyper(nrow(ApaProtTrans), nrow(PeandTE), nrow(PeandTEtested), nrow(apaTestedinboth),lower.tail=F)
[1] 0.0803436

Expression te and pe

APA, protein, and translation:

possible hits, not possible

Success is the overlap, dp and dt, not dp and dt, sample size is the egenes tested in both dp and dt

Protein_g = Protein %>% rename("gene"=gene.symbol)
translation_g= Translation%>% rename("gene"=Gene)
DEgenes_g= DEgenes %>% rename("gene"=Gene.name)

EProtTrans=DEgenes %>% rename("gene"=Gene.name) %>% inner_join(Protein_g, by="gene") %>% inner_join(translation_g, by="gene")

PeandTE=Translation %>% inner_join(Protein, by = "ENSG") %>% rename("gene"=Gene)

#not dp and dt is all tested in both - pe and te set
PeandTEtested= ProtTested %>% full_join(TranslationTested, by="gene") %>% anti_join(PeandTE,by="gene")
  
expressioninboth= ProtTested %>% full_join(TranslationTested, by="gene") %>% inner_join(DEgenes_g, by="gene")

phyper(nrow(EProtTrans), nrow(PeandTE), nrow(PeandTEtested), nrow(expressioninboth),lower.tail=F)
[1] 3.771448e-30

Skipping splicing for now due to gene list problem.


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_1.5.0 UpSetR_1.3.3   

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       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     withr_2.1.2      glue_1.3.0      
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         gridExtra_2.3    hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     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