Last updated: 2020-05-15

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DICNotDEDP.Rmd
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.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/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 368ca9a brimittleman 2020-05-15 plots for com meet
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Rmd 341a5c0 brimittleman 2020-05-07 add seperation with dapa and dic
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Rmd b140367 brimittleman 2020-05-04 add pie charts

I want to make pie charts to show differential usage at all three levels.

library(workflowr)
This is workflowr version 1.6.0
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()
library(UpSetR)
library(cowplot)

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

    ggsave
useCOl <- c("#d73027", "#4575b4","#fee090")

dAPA, pas and gene level dIC diff Dominant

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)
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 ) 

NotTestedPAS= Meta_PAS %>% anti_join(dAPAPAS,by="PAS") %>% mutate(SigPAU2="notTested")

dAPAwithNottested=NotTestedPAS %>% bind_rows(dAPAPAS)

dAPAPASGrouped= dAPAwithNottested %>%  group_by(SigPAU2) %>% summarise(n=n()) %>% mutate(genes=sum(n), prop=n/genes)



pasplot=ggplot(dAPAPASGrouped,aes(by=SigPAU2, y=prop, x="", fill=SigPAU2)) + geom_bar(stat="identity",width=1, color="white")+  coord_polar("y", start=0) +theme_void() + scale_fill_manual(values=useCOl, name="dAPA PAS") +labs(title="Differential APA PAS 5% FDR")

pasplot

Version Author Date
a9de4a1 brimittleman 2020-05-04

Gene level:

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()
NotTestedGenes=Meta_genes %>% anti_join(dAPAPAS_genes, by="gene") %>% mutate(dAPA="NotTested")


dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No")) %>% bind_rows(NotTestedGenes)%>% group_by(dAPA) %>% summarise(n=n()) %>% mutate(genes=sum(n), prop=n/genes)
genesplot=ggplot(dAPATestedGenes,aes(by=dAPA, y=prop, x="", fill=dAPA)) + geom_bar(stat="identity",width=1, color="white")+  coord_polar("y", start=0) +theme_void() + scale_fill_manual(values=useCOl, name="dAPA Genes") +labs(title="Differential APA genes 5% FDR")
genesplot

Version Author Date
a9de4a1 brimittleman 2020-05-04

dInfo:

dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance_10FDR.txt", header = T, stringsAsFactors = F)%>% select(sIC10,gene)

Nottested= Meta_genes %>% anti_join(dICdata, by="gene") %>% mutate(sIC10="notTested")

dICdata_wnot= dICdata %>% bind_rows(Nottested) %>% group_by(sIC10) %>% summarise(n=n()) %>% mutate(genes=sum(n), prop=n/genes)

dICplot=ggplot(dICdata_wnot,aes(by=sIC10, y=prop, x="", fill=sIC10)) + geom_bar(stat="identity",width=1, color="white")+  coord_polar("y", start=0) +theme_void() + scale_fill_manual(values=useCOl, name="dIC Genes") +labs(title="Differential information content\n 10% FDR")
dICplot

Version Author Date
a9de4a1 brimittleman 2020-05-04 
dICdata5= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)

Nottested5= Meta_genes %>% anti_join(dICdata5, by="gene") %>% mutate(sIC="notTested")

dICdata5_wnot= dICdata5 %>% bind_rows(Nottested5) %>% group_by(sIC) %>% summarise(n=n()) %>% mutate(genes=sum(n), prop=n/genes)

dIC5plot=ggplot(dICdata5_wnot,aes(by=sIC, y=prop, x="", fill=sIC)) + geom_bar(stat="identity",width=1, color="white")+  coord_polar("y", start=0) +theme_void() + scale_fill_manual(values=useCOl, name="dIC Genes") +labs(title="Differential information content\n 5% FDR")
dIC5plot

Version Author Date
3fe9930 brimittleman 2020-05-07
a9de4a1 brimittleman 2020-05-04 
HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)

ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)

BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40) %>% select(gene, Set)

NoDom= Meta_genes %>% anti_join(BothRes_40,by="gene") %>% mutate(Set="NoDominant")

BothRes_40wNo=BothRes_40 %>% bind_rows(NoDom) %>% group_by(Set)  %>% summarise(n=n()) %>% mutate(genes=sum(n), prop=n/genes)

BothRes_40wNo$Set=factor(BothRes_40wNo$Set, levels=c('Same', 'NoDominant', 'Different'))



Domplot=ggplot(BothRes_40wNo,aes(by=Set, y=prop, x="", fill=Set)) + geom_bar(stat="identity",width=1, color="white")+  coord_polar("y", start=0) +theme_void() + scale_fill_manual(values=useCOl, name="Genes with \nDominant PAS") +labs(title="Same and Different Dominant PAS")
Domplot

Version Author Date
3fe9930 brimittleman 2020-05-07
a9de4a1 brimittleman 2020-05-04 
plot_grid(pasplot, genesplot, dIC5plot, Domplot)

Version Author Date
3fe9930 brimittleman 2020-05-07
84965f6 brimittleman 2020-05-06 
plot_grid(pasplot, genesplot)

Version Author Date
3fe9930 brimittleman 2020-05-07
84965f6 brimittleman 2020-05-06

Use upsetter to compare all of these:

BothRes_40_diff= BothRes_40 %>% filter(Set=="Different")
dICdata_sig= dICdata5 %>% filter(sIC=="Yes")
listInput <- list(dIC=dICdata_sig$gene, DAPA=dAPAGenes$gene, dDom=BothRes_40_diff$gene)

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

Version Author Date
3fe9930 brimittleman 2020-05-07 
listInputNoDD <- list(dIC=dICdata_sig$gene, DAPA=dAPAGenes$gene )

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

How many genes in none:

DiffAny= dICdata_sig %>% select(gene) %>% bind_rows(dAPAGenes %>% select(gene)) %>% bind_rows(BothRes_40_diff %>% select(gene)) %>% unique()

all tested:

1-nrow(DiffAny)/nrow(Meta_genes)
[1] 0.7757932

dAPA location

dAPAPASYes=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% filter(SigPAU2=="Yes") %>% select(PAS,loc )  %>% group_by(loc)  %>% summarise(n=n()) %>% mutate(PAS=sum(n), prop=n/PAS)

dAPAPASYes
# A tibble: 5 x 4
  loc        n   PAS   prop
  <chr>  <int> <int>  <dbl>
1 cds      262  2342 0.112 
2 end      172  2342 0.0734
3 intron   547  2342 0.234 
4 utr3    1280  2342 0.547 
5 utr5      81  2342 0.0346
pasLocplot=ggplot(dAPAPASYes,aes(by=loc, y=prop, x="", fill=loc)) + geom_bar(stat="identity",width=1, color="white")+  coord_polar("y", start=0) +theme_void() + scale_fill_brewer(palette="RdYlBu", name="dAPA PAS") +labs(title="Differential APA PAS 5% FDR")


pasLocplot

are these enriched:

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

for (i in c("cds", "end", "intron", "utr3", "utr5")){
  x=nrow(dAPAPASallwithloc %>% filter(SigPAU2=="Yes", loc==i))
  m=nrow(dAPAPASallwithloc %>% filter( loc==i))
  n=nrow(dAPAPASallwithloc %>% filter(loc!=i))
  k=nrow(dAPAPASallwithloc %>% filter(SigPAU2=="Yes"))
  N=nrow(dAPAPASallwithloc)
  pval=c(pval, phyper(x-1,m,n,k,lower.tail=F))
  enrichval=(x/k)/(m/N)
  enrich=c(enrich, enrichval)
}

loc=c("cds", "end", "intron", "utr3", "utr5")
enrich
[1] 0.6583560 0.8428539 0.7156830 1.4093171 1.2013480
pval
[1] 1.000000e+00 9.938636e-01 1.000000e+00 1.332539e-57 5.118903e-02
enrichlocdf=as.data.frame(cbind(loc, enrich,pval))
enrichlocdf
     loc            enrich                 pval
1    cds 0.658356029405188                    1
2    end 0.842853857102192    0.993863593282057
3 intron 0.715682968523463                    1
4   utr3  1.40931713534442 1.33253850202655e-57
5   utr5  1.20134795077984   0.0511890333535472

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   UpSetR_1.3.3    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    ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[16] readxl_1.1.0       plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       fansi_0.4.0        broom_0.5.1       
[28] Rcpp_1.0.4.6       promises_1.0.1     scales_1.0.0      
[31] backports_1.1.2    jsonlite_1.6       fs_1.3.1          
[34] gridExtra_2.3      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        magrittr_1.5      
[43] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[46] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[49] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[52] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[55] git2r_0.26.1       compiler_3.5.1