Last updated: 2020-05-07
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Knit directory: Comparative_APA/analysis/
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library(workflowr)
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Run ?workflowr for help getting started
library(tidyverse)
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library(cowplot)
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ggsave
I want to look at regulatory phenotype regulation based on dAPA, both, or dIC.
For this analysis I will use dIC at 5% FDR. Numbers are smaller but overlaps suggest it is more biological.
I will look at genes tested in all analysis then proportion results to only dAPA, dIC and dAPA, or dIC only. I will test for enrichement in each of these sets with expression, translation, and protein.
Load APA data:
For apa I reduce to gene level and count it as sig if at least one PAS is different.
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"))
nrow(dAPAandDic)
[1] 8422
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)
DEandAPA=DiffExp %>% inner_join(dAPAandDic,by="gene")
nrow(DEandAPA)
[1] 7465
Erichment for only APA:
sets=c("OnlyAPA", "OnlyIC", "Both")
DE_pval=c()
DE_enrich=c()
x=nrow(DEandAPA %>% filter(OnlyAPA=="Yes", DE=="Yes"))
m=nrow(DEandAPA %>% filter(DE=="Yes"))
n=nrow(DEandAPA %>% filter(DE=="No"))
k=nrow(DEandAPA %>% filter(OnlyAPA=="Yes"))
N=nrow(DEandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.0005193482
DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 447
DE_enrich=c(DE_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.122516
Only dIC
x=nrow(DEandAPA %>% filter(OnlyIC=="Yes", DE=="Yes"))
m=nrow(DEandAPA %>% filter(DE=="Yes"))
n=nrow(DEandAPA %>% filter(DE=="No"))
k=nrow(DEandAPA %>% filter(OnlyIC=="Yes"))
N=nrow(DEandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.2216107
DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 154
DE_enrich=c(DE_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.052215
Both:
x=nrow(DEandAPA %>% filter(Both=="Yes", DE=="Yes"))
m=nrow(DEandAPA %>% filter(DE=="Yes"))
n=nrow(DEandAPA %>% filter(DE=="No"))
k=nrow(DEandAPA %>% filter(Both=="Yes"))
N=nrow(DEandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.02681958
DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 163
DE_enrich=c(DE_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.128023
All de res:
DEdf=as.data.frame(cbind(sets,DE_pval, DE_enrich)) %>% rename(Pval=DE_pval, Enrichment=DE_enrich) %>% mutate(Pheno="Expression")
DEdf
sets Pval Enrichment Pheno
1 OnlyAPA 0.000519348163532497 1.12251636247181 Expression
2 OnlyIC 0.221610701204403 1.05221488574561 Expression
3 Both 0.0268195798877835 1.12802297586811 Expression
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"))
RiboSmall= Ribo %>% select(gene,dTE)
DTandAPA=Ribo %>% inner_join(dAPAandDic,by="gene")
nrow(DTandAPA)
[1] 6477
#sets=c("OnlyAPA", "OnlyIC", "Both")
DT_pval=c()
DT_enrich=c()
only APA
x=nrow(DTandAPA %>% filter(OnlyAPA=="Yes", dTE=="Yes"))
m=nrow(DTandAPA %>% filter(dTE=="Yes"))
n=nrow(DTandAPA %>% filter(dTE=="No"))
k=nrow(DTandAPA %>% filter(OnlyAPA=="Yes"))
N=nrow(DTandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.06327494
DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 210
DT_enrich=c(DT_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.093646
Only dIC
x=nrow(DTandAPA %>% filter(OnlyIC=="Yes", dTE=="Yes"))
m=nrow(DTandAPA %>% filter(dTE=="Yes"))
n=nrow(DTandAPA %>% filter(dTE=="No"))
k=nrow(DTandAPA %>% filter(OnlyIC=="Yes"))
N=nrow(DTandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.02205198
DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 94
DT_enrich=c(DT_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.205226
x=nrow(DTandAPA %>% filter(Both=="Yes", dTE=="Yes"))
m=nrow(DTandAPA %>% filter(dTE=="Yes"))
n=nrow(DTandAPA %>% filter(dTE=="No"))
k=nrow(DTandAPA %>% filter(Both=="Yes"))
N=nrow(DTandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.01134051
DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 91
DT_enrich=c(DT_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.240121
DTdf=as.data.frame(cbind(sets,DT_pval, DT_enrich)) %>% rename(Pval=DT_pval, Enrichment=DT_enrich) %>% mutate(Pheno="Translation")
DTdf
sets Pval Enrichment Pheno
1 OnlyAPA 0.0632749366195107 1.09364622715088 Translation
2 OnlyIC 0.0220519791477263 1.20522601526234 Translation
3 Both 0.0113405052499701 1.24012060208466 Translation
(pval is adjusted already)
Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T,stringsAsFactors = F) %>% mutate(dP=ifelse(pval<0.05, "Yes", "No"))
ProtSmall=Prot %>% select(gene, dP)
DPandAPA=Prot %>% inner_join(dAPAandDic,by="gene")
nrow(DPandAPA)
[1] 2641
#sets=c("OnlyAPA", "OnlyIC", "Both")
DP_pval=c()
DP_enrich=c()
only APA
x=nrow(DPandAPA %>% filter(OnlyAPA=="Yes", dP=="Yes"))
m=nrow(DPandAPA %>% filter(dP=="Yes"))
n=nrow(DPandAPA %>% filter(dP=="No"))
k=nrow(DPandAPA %>% filter(OnlyAPA=="Yes"))
N=nrow(DPandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.2293006
DP_pval=c(DP_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 130
DP_enrich=c(DP_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.052798
Only dIC
x=nrow(DPandAPA %>% filter(OnlyIC=="Yes", dP=="Yes"))
m=nrow(DPandAPA %>% filter(dP=="Yes"))
n=nrow(DPandAPA %>% filter(dP=="No"))
k=nrow(DPandAPA %>% filter(OnlyIC=="Yes"))
N=nrow(DPandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.8222889
DP_pval=c(DP_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 68
DP_enrich=c(DP_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 0.925635
x=nrow(DPandAPA %>% filter(Both=="Yes", dP=="Yes"))
m=nrow(DPandAPA %>% filter(dP=="Yes"))
n=nrow(DPandAPA %>% filter(dP=="No"))
k=nrow(DPandAPA %>% filter(Both=="Yes"))
N=nrow(DPandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.8802321
DP_pval=c(DP_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 56
DP_enrich=c(DP_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 0.895688
DPdf=as.data.frame(cbind(sets,DP_pval, DP_enrich)) %>% rename(Pval=DP_pval, Enrichment=DP_enrich) %>% mutate(Pheno="Protein")
DPdf
sets Pval Enrichment Pheno
1 OnlyAPA 0.229300585846805 1.05279781179472 Protein
2 OnlyIC 0.822288948709401 0.925634999175326 Protein
3 Both 0.880232087900127 0.895687984496124 Protein
AllDF= DEdf %>% bind_rows(DTdf) %>% bind_rows(DPdf)
Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
AllDF$Pval=as.numeric(AllDF$Pval)
AllDF$Enrichment=as.numeric(AllDF$Enrichment)
AllDF$Pheno=factor(AllDF$Pheno, levels=c("Expression", "Translation", "Protein"))
useCOl <- c("#d73027", "#4575b4","#fee090")
enrichplot=ggplot(AllDF,aes(x=Pheno, by=sets, y=Enrichment,fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1) + scale_fill_manual(values=useCOl)
enrichplot
Version | Author | Date |
---|---|---|
3fe9930 | brimittleman | 2020-05-07 |
enrichpoint=ggplot(AllDF,aes(x=sets,col=sets,y=Enrichment,label = round(Enrichment,3)))+ geom_bar(stat="identity",color="grey",aes(y=AllDF$Enrichment),width=.01)+geom_point(size=10) + coord_flip() + geom_hline(yintercept = 1) + facet_grid(~Pheno)+scale_color_manual(values=useCOl)+ labs( title="Enrichment for APA phenotype differences in other regulatory phenotypes",x="Set", y="Enrichement")+geom_text(color = "black", size = 3) + theme(legend.position = "none")
enrichpoint
Version | Author | Date |
---|---|---|
3fe9930 | brimittleman | 2020-05-07 |
pvalplot=ggplot(AllDF,aes(x=Pheno, by=sets, y=-log10(Pval),fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1.3)+ scale_fill_manual(values=useCOl)+ theme(legend.position = "bottom")
pvalplot
Version | Author | Date |
---|---|---|
3fe9930 | brimittleman | 2020-05-07 |
plot together:
plot_grid(enrichpoint,pvalplot, nrow=2)
Version | Author | Date |
---|---|---|
3fe9930 | brimittleman | 2020-05-07 |
Plot without protien:
DETEDF= DEdf %>% bind_rows(DTdf)
Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
DETEDF$Pval=as.numeric(DETEDF$Pval)
DETEDF$Enrichment=as.numeric(DETEDF$Enrichment)
DETEDF$Pheno=factor(DETEDF$Pheno, levels=c("Expression", "Translation", "Protein"))
enrichpointnoP=ggplot(DETEDF,aes(x=sets,col=sets,y=Enrichment,label = round(Enrichment,3)))+ geom_bar(stat="identity",color="grey",aes(y=DETEDF$Enrichment),width=.01)+geom_point(size=10) + coord_flip() + geom_hline(yintercept = 1) + facet_grid(~Pheno)+scale_color_manual(values=useCOl)+ labs( title="Enrichment for APA phenotype differences in other regulatory phenotypes",x="Set", y="Enrichement")+geom_text(color = "black", size = 3) + theme(legend.position = "none")
enrichpointnoP
pvalplotnoP=ggplot(DETEDF,aes(x=Pheno, by=sets, y=-log10(Pval),fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1.3)+ scale_fill_manual(values=useCOl)+ theme(legend.position = "bottom")
pvalplotnoP
plot_grid(enrichpointnoP,pvalplotnoP, nrow=2)
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] cowplot_0.9.4 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.1 tidyverse_1.2.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 reshape2_1.4.3 haven_1.1.2 lattice_0.20-38
[5] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6 yaml_2.2.0
[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] broom_0.5.1 Rcpp_1.0.4.6 promises_1.0.1 scales_1.0.0
[29] backports_1.1.2 jsonlite_1.6 fs_1.3.1 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