dAPA dIC together and sep

Last updated: 2020-05-07

Checks: 7 0

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/establishCutoffs.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    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	341a5c0	brimittleman	2020-05-07	add seperation with dapa and dic

library(workflowr)

This is workflowr version 1.6.0
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library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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✔ 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()

library(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    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

Expression

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

Translation

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

Protein

(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

Plot together:

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

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

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

plot together:

plot_grid(enrichpoint,pvalplot, 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

dAPA dIC together and sep

Briana Mittleman

5/7/2020

Expression

Translation

Protein

Plot together: