Protein modificiations
Briana Mittleman
5/22/2020
Last updated: 2020-05-22
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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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.
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c5cb2cc | brimittleman | 2020-05-22 | add prot mod and effect sizes |
library(cowplot)Loading required package: ggplot2
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsavelibrary(workflowr)This is workflowr version 1.6.0
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extract
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get_legendTest 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/PTMDownload 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] 11509Pull 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$gene, "Yes", "No")) %>% inner_join(NUbiqSites,by="gene")
dAPAandDic_wP_dAPA= dAPAandDic_wP %>% filter(OnlyAPA=="Yes") %>% mutate(set="Site")%>% mutate(dpnoTE=ifelse(dPnotDE=="Yes" & dte=="No","Yes", "No"))
dAPAandDic_wP_dAPA %>% group_by(dPnotDE) %>% summarise(n())# A tibble: 2 x 2
dPnotDE `n()`
<chr> <int>
1 No 233
2 Yes 74dAPAandDic_wP_dIC= dAPAandDic_wP %>% filter(OnlyIC=="Yes") %>% mutate(set="Diversity")%>% mutate(dpnoTE=ifelse(dPnotDE=="Yes" & dte=="No","Yes", "No"))
dAPAandDic_wP_dIC %>% group_by(dPnotDE) %>% summarise(n())# A tibble: 2 x 2
dPnotDE `n()`
<chr> <int>
1 No 143
2 Yes 38dAPAandDic_wP_both= dAPAandDic_wP %>% filter(Both=="Yes") %>% mutate(set="Both")%>% mutate(dpnoTE=ifelse(dPnotDE=="Yes" & dte=="No","Yes", "No"))
dAPAandDic_wP_both %>% group_by(dPnotDE) %>% summarise(n())# A tibble: 2 x 2
dPnotDE `n()`
<chr> <int>
1 No 117
2 Yes 32anyAPA=c(dAPAandDic_wP_dAPA$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()
ggplot(TogetherWP, aes(x=set, y=nUBNorm, by=dpnoTE,fill=dpnoTE)) + geom_boxplot() + stat_compare_means()
Any apa and protein
ggplot(AllGenestest, aes(x=dP,by=AnyAPA, y=nUBNorm,fill=AnyAPA)) +geom_boxplot() +stat_compare_means()
ggplot(AllGenestest, aes(x=dPnotDE,by=AnyAPA, y=nUBNorm,fill=AnyAPA)) +geom_boxplot() +stat_compare_means()
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] 7022ggplot(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")
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="Normalized Ubiquitination", title="Genes with differences in 3' UTR APA sites \n correlated with mark signalling protein decay")+theme_classic()
UTR
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="Normalized Ubiquitination", title="Genes with differences in intronic APA site \n are not correlated with mark signalling protein decay") +theme_classic()
Intronic
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="Normalized Ubiquitination", title="Genes with differences in APA sites\n correlated with mark signalling protein decay")+theme_classic()
all
plot_grid(UTR,Intronic )
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)
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")
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 stringWarning in scan(file = file, what = what, sep = sep, quote = quote, dec =
dec, : number of items read is not a multiple of the number of columnsNActySites=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
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
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
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