Last updated: 2020-06-01

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/incorporateQTLsAncestral.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File Version Author Date Message
Rmd c4db330 brimittleman 2020-06-01 change to stand
html 2d70737 brimittleman 2020-05-22 Build site.
Rmd c5cb2cc brimittleman 2020-05-22 add prot mod and effect sizes 
library(cowplot)
Loading required package: ggplot2

Attaching package: 'cowplot'
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library(workflowr)
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library(tidyverse)
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library(ggpubr)
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Test protien modifications for the genes that have differences at the protein level and in APA but not in the expression data. Using the same database that Sidney used for the translation paper.

PhosphoSitePlus - https://www.phosphosite.org/staticDownloads

I will use the newest version for now.

050320 PhosphoSitePlus(R) (PSP) was created by Cell Signaling Technology Inc. It is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. When using PSP data or analyses in printed publications or in online resources, the following acknowledgements must be included: (a) the words “PhosphoSitePlus(R), www.phosphosite.org” must be included at appropriate places in the text or webpage, and (b) the following citation must be included in the bibliography: “Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015 43:D512-20. PMID: 25514926.”

download on 5/22/20

mkdir ../data/PTM

Download the protein length data from UniProtKB 5/22/20
use the primary gene name and the length. subset to the reviewed for high confidence

ProtInfo=read.table("../data/PTM/ProtLength.txt", sep = "\t",stringsAsFactors = F,header = T,col.names = c("entry","organism", "nAA", "gene")) %>% select(nAA, gene)

Ubiq=read.table("../data/PTM/Ubiquitination_site_dataset", header = T, sep="\t",stringsAsFactors = FALSE) %>% filter(ORGANISM=="human",GENE!="") %>% rename("gene"=GENE)


NUbiqSites=Ubiq %>% group_by(gene) %>% summarise(nUBsites=n())  %>% inner_join(ProtInfo,by="gene") %>% mutate(nUBNorm=nUBsites/nAA)

nrow(NUbiqSites)
[1] 11509

Pull in P not e genes.

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) 


Meta_genes= Meta %>% select(gene) %>% unique()

Meta_PAS=Meta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No")) 

dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)

dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))

dIConly=dAPAandDic %>%filter(OnlyIC=="Yes")

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)

DiffExp=read.table("../data/DiffExpression/DEtested_allres.txt",stringsAsFactors = F,header = F, col.names = c("Gene_stable_ID" ,"logFC" ,"AveExpr" , "t" ,  "P.Value" ,  "adj.P.Val", "B"  )) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::select(-Gene_stable_ID) %>% mutate(DE=ifelse(adj.P.Val<.05, "Yes", "No")) %>% select(gene,DE) %>% filter(DE=="Yes")

Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T,stringsAsFactors = F)  %>% mutate(dP=ifelse(pval<0.05, "Yes", "No")) 
Protsm=Prot %>% select(gene,dP)
Prot_sig= Prot %>% filter(dP=="Yes")


Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No")) %>% filter(dTE=="Yes")


RiboAll=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No"))
dAPAandDic_wP=dAPAandDic %>% inner_join(Protsm, by="gene") %>% mutate(dE=ifelse(gene %in%DiffExp$gene, "Yes", "No" ), dPnotDE=ifelse(dE=="No"&dP=="Yes", "Yes","No"), dte=ifelse(gene %in% Ribo$gene, "Yes", "No"))  %>% inner_join(NUbiqSites,by="gene")


dAPAandDic_wP_dAPA= dAPAandDic_wP %>% filter(OnlyAPA=="Yes") %>% mutate(set="Site")%>% mutate(dpnoTE=ifelse(dPnotDE=="Yes" & dte=="No","Yes", "No"))
dAPAandDic_wP_dAPA %>% group_by(dPnotDE) %>% summarise(n())
# A tibble: 2 x 2
  dPnotDE `n()`
  <chr>   <int>
1 No        233
2 Yes        74
dAPAandDic_wP_dIC= dAPAandDic_wP %>% filter(OnlyIC=="Yes") %>% mutate(set="Diversity")%>% mutate(dpnoTE=ifelse(dPnotDE=="Yes" & dte=="No","Yes", "No"))
dAPAandDic_wP_dIC %>% group_by(dPnotDE) %>% summarise(n())
# A tibble: 2 x 2
  dPnotDE `n()`
  <chr>   <int>
1 No        143
2 Yes        38
dAPAandDic_wP_both= dAPAandDic_wP %>% filter(Both=="Yes") %>% mutate(set="Both")%>% mutate(dpnoTE=ifelse(dPnotDE=="Yes" & dte=="No","Yes", "No"))
dAPAandDic_wP_both %>% group_by(dPnotDE) %>% summarise(n())
# A tibble: 2 x 2
  dPnotDE `n()`
  <chr>   <int>
1 No        117
2 Yes        32
anyAPA=c(dAPAandDic_wP_dAPA$gene, dAPAandDic_wP_dIC$gene,dAPAandDic_wP_both$gene )

TogetherWP= dAPAandDic_wP_dAPA %>% bind_rows(dAPAandDic_wP_dIC) %>% bind_rows(dAPAandDic_wP_both) %>% select(gene, set,dE, dP, dPnotDE, dte,nUBNorm ,dpnoTE) 


AllGenestest= dAPAandDic_wP %>%  mutate(AnyAPA=ifelse(gene %in% anyAPA, "Yes", "No"))

Plot:

ggplot(TogetherWP, aes(x=set, y=nUBNorm, by=dPnotDE,fill=dPnotDE)) + geom_boxplot() + stat_compare_means()

Version Author Date
2d70737 brimittleman 2020-05-22 
ggplot(TogetherWP, aes(x=set, y=nUBNorm, by=dpnoTE,fill=dpnoTE)) + geom_boxplot() + stat_compare_means()

Version Author Date
2d70737 brimittleman 2020-05-22

Any apa and protein

ggplot(AllGenestest, aes(x=dP,by=AnyAPA, y=nUBNorm,fill=AnyAPA)) +geom_boxplot() +stat_compare_means()

Version Author Date
2d70737 brimittleman 2020-05-22 
ggplot(AllGenestest, aes(x=dPnotDE,by=AnyAPA, y=nUBNorm,fill=AnyAPA)) +geom_boxplot() +stat_compare_means()

Version Author Date
2d70737 brimittleman 2020-05-22

What about just any difference in APA

Meta_genes= Meta %>% select(gene) %>% unique()

Meta_PAS=Meta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No"))
dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)
dICdata_sig= dICdata %>% filter(sIC=="Yes")
dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))


OnlyAPAGenes= dAPAandDic %>% filter(OnlyAPA=="Yes") %>% select(gene) %>% mutate(set="Site")
IsoformGenes=  dAPAandDic %>% filter(OnlyIC=="Yes") %>% select(gene) %>% mutate(set="Isoform")
BothGenes=  dAPAandDic %>% filter(Both=="Yes") %>% select(gene) %>% mutate(set="Both")
NoneGenes=dAPAandDic %>% filter(dAPA=="No" & sIC=="No" ) %>% select(gene) %>% mutate(set="Conserved")


CharacterizeAllGenes= OnlyAPAGenes %>% bind_rows(IsoformGenes) %>% bind_rows(BothGenes)%>% bind_rows(BothGenes) %>% bind_rows(NoneGenes) %>% mutate(OverAllCons=ifelse(set=="Conserved", "Yes","No")) %>% inner_join(NUbiqSites,by="gene")
nrow(CharacterizeAllGenes)
[1] 7022
ggplot(CharacterizeAllGenes, aes(x=OverAllCons, y=nUBNorm, fill=OverAllCons)) + geom_boxplot() + stat_compare_means()  +scale_fill_brewer(palette = "Set1")+ labs(x="Differences in P and APA", y="Ubiquitnation", title="genes with site level differences")

Version Author Date
2d70737 brimittleman 2020-05-22

Nothing super interesting here..

correlation between the nUBNorm and effect size…

Look at the UTR dAPA PAS

DiffIsoUTR=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene"))  %>%  inner_join(NUbiqSites,by="gene") %>% filter(loc=="utr3")

DiffIsoIntron=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene"))  %>%  inner_join(NUbiqSites,by="gene") %>% filter(loc=="intron")

DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene"))  %>%  inner_join(NUbiqSites,by="gene") 

DiffIsoSig= DiffIso %>%filter(SigPAU2=="Yes")
DiffIsoIntronSig=DiffIsoIntron %>% filter(SigPAU2=="Yes")
DiffIsoUTRsig= DiffIsoUTR %>% filter(SigPAU2=="Yes")
UTR=ggplot(DiffIsoUTRsig,aes(x=abs(deltaPAU), y=nUBNorm) ) + geom_point() + stat_cor(col="red", label.x=.5) +geom_smooth(col="red", method="lm")+labs(y="Standardized Ubiquitination", title="Genes with differences in 3' UTR APA sites \n correlated with mark signalling protein decay")+theme_classic()
UTR

Version Author Date
2d70737 brimittleman 2020-05-22 
Intronic=ggplot(DiffIsoIntronSig,aes(x=abs(deltaPAU), y=nUBNorm) ) + geom_point() + stat_cor(col="blue", label.x=.5) +geom_smooth(col="blue", method="lm")+labs(y="Standardized Ubiquitination", title="Genes with differences in intronic APA site \n are not correlated with mark signalling protein decay") +theme_classic()
Intronic

Version Author Date
2d70737 brimittleman 2020-05-22 
all=ggplot(DiffIsoSig,aes(x=abs(deltaPAU), y=nUBNorm) ) + geom_point() + stat_cor(col="purple", label.x=.5) +geom_smooth(col="purple", method="lm")+labs(y="Standardized Ubiquitination", title="Genes with differences in APA sites\n correlated with mark signalling protein decay")+theme_classic()
all

Version Author Date
2d70737 brimittleman 2020-05-22 
plot_grid(UTR,Intronic )

Version Author Date
2d70737 brimittleman 2020-05-22

subset to those that also have protien diff:

DiffIsosigP= DiffIsoSig %>%  inner_join(Prot, by="gene")
dAPAandDic_wPnotE= dAPAandDic_wP %>% select(gene, dPnotDE, dte)
DiffIsosigPnotE= DiffIsoSig %>%  inner_join(dAPAandDic_wPnotE, by="gene")



ggplot(DiffIsosigP,aes(x=abs(deltaPAU), y=nUBNorm) ) + geom_point() + stat_cor(col="red", label.x=.5) +geom_smooth(col="red", method="lm") +facet_grid(~dP)

Version Author Date
2d70737 brimittleman 2020-05-22 
ggplot(DiffIsosigPnotE,aes(x=abs(deltaPAU), y=nUBNorm) ) + geom_point() + stat_cor(col="red", label.x=.5) +geom_smooth(col="red", method="lm") +facet_grid(dPnotDE~dte ) +labs(title="rows=dPnotDE, col=dTE")

Version Author Date
2d70737 brimittleman 2020-05-22

Check location results with a second mark tested in translation paper

Acytelation

PhosphoSitePlus(R) (PSP) was created by Cell Signaling Technology Inc. It is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. When using PSP data or analyses in printed publications or in online resources, the following acknowledgements must be included: (a) the words “PhosphoSitePlus(R), www.phosphosite.org” must be included at appropriate places in the text or webpage, and (b) the following citation must be included in the bibliography: “Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015 43:D512-20. PMID: 25514926.”

Acty=read.table("../data/PTM/Acetylation_site_dataset", header = T, sep="\t",stringsAsFactors = FALSE) %>% filter(ORGANISM=="human",GENE!="") %>% rename("gene"=GENE)
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec =
dec, : EOF within quoted string
Warning in scan(file = file, what = what, sep = sep, quote = quote, dec =
dec, : number of items read is not a multiple of the number of columns
NActySites=Acty %>% group_by(gene) %>% summarise(nACsites=n())  %>% inner_join(ProtInfo,by="gene") %>% mutate(nAcNorm=nACsites/nAA)
DiffIsoUTR=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene"))  %>%  inner_join(NActySites,by="gene") %>% filter(loc=="utr3")

DiffIsoIntron=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene"))  %>%  inner_join(NActySites,by="gene") %>% filter(loc=="intron")

DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene"))  %>%  inner_join(NActySites,by="gene") 

DiffIsoSig= DiffIso %>%filter(SigPAU2=="Yes")
DiffIsoIntronSig=DiffIsoIntron %>% filter(SigPAU2=="Yes")
DiffIsoUTRsig= DiffIsoUTR %>% filter(SigPAU2=="Yes")
UTRAC=ggplot(DiffIsoUTRsig,aes(x=abs(deltaPAU), y=nAcNorm) ) + geom_point() + stat_cor(col="red", label.x=.5) +geom_smooth(col="red", method="lm")+labs(y="Normalized Acytelation")+theme_classic()
UTRAC

Version Author Date
2d70737 brimittleman 2020-05-22 
IntronicAC=ggplot(DiffIsoIntronSig,aes(x=abs(deltaPAU), y=nAcNorm) ) + geom_point() + stat_cor(col="blue", label.x=.5) +geom_smooth(col="blue", method="lm")+labs(y="Normalized Acytelation" ) +theme_classic()
IntronicAC

Version Author Date
2d70737 brimittleman 2020-05-22 
allAC=ggplot(DiffIsoSig,aes(x=abs(deltaPAU), y=nAcNorm) ) + geom_point() + stat_cor(col="purple", label.x=.5) +geom_smooth(col="purple", method="lm")+labs(y="Normalized Acytelation")+theme_classic()
allAC

Version Author Date
2d70737 brimittleman 2020-05-22 

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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    tidyverse_1.2.1 workflowr_1.6.0 cowplot_0.9.4  
[13] ggplot2_3.1.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         utf8_1.1.4        
[10] rlang_0.4.0        later_0.7.5        pillar_1.3.1      
[13] glue_1.3.0         withr_2.1.2        RColorBrewer_1.1-2
[16] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[19] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[22] rvest_0.3.2        evaluate_0.12      labeling_0.3      
[25] knitr_1.20         httpuv_1.4.5       fansi_0.4.0       
[28] broom_0.5.1        Rcpp_1.0.4.6       promises_1.0.1    
[31] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[34] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[37] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[40] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[43] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[46] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[52] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[55] compiler_3.5.1