Last updated: 2020-04-16

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
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    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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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 4cc2f72 brimittleman 2020-04-16 add prop plots and 1v1 lines
html 218a3d4 brimittleman 2020-04-16 Build site.
Rmd 2461fb7 brimittleman 2020-04-16 new dom integration 
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(cowplot)

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

    ggsave
library(RColorBrewer)

I want to look at the specific usages for the dominant PAS using the new method: Include infor about diff used:

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


DiffUsedPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T, stringsAsFactors = F) %>% filter(SigPAU2=="Yes") %>% inner_join(PAS, by=c("chr","start", "end"))
DiffUsedPASAll=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T, stringsAsFactors = F)%>% inner_join(PAS, by=c("chr","start", "end"))

Dominant PAS

#9
HumanDom9=read.table("../data/DomDefGreaterX/Human_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human9") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom9=read.table("../data/DomDefGreaterX/Chimp_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp9")    %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#8 
HumanDom8=read.table("../data/DomDefGreaterX/Human_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human8") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom8=read.table("../data/DomDefGreaterX/Chimp_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp8") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#7 
HumanDom7=read.table("../data/DomDefGreaterX/Human_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human7") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom7=read.table("../data/DomDefGreaterX/Chimp_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp7") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))


#6 
HumanDom6=read.table("../data/DomDefGreaterX/Human_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human6") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom6=read.table("../data/DomDefGreaterX/Chimp_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp6") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#5  
HumanDom5=read.table("../data/DomDefGreaterX/Human_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human5") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom5=read.table("../data/DomDefGreaterX/Chimp_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp5") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#4 
HumanDom4=read.table("../data/DomDefGreaterX/Human_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human4") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom4=read.table("../data/DomDefGreaterX/Chimp_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp4") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#3 
HumanDom3=read.table("../data/DomDefGreaterX/Human_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human3") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom3=read.table("../data/DomDefGreaterX/Chimp_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp3") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#2
HumanDom2=read.table("../data/DomDefGreaterX/Human_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human2") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom2=read.table("../data/DomDefGreaterX/Chimp_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp2") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#1
HumanDom1=read.table("../data/DomDefGreaterX/Human_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human1") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))
ChimpDom1=read.table("../data/DomDefGreaterX/Chimp_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp1") %>% mutate(Diff=ifelse(PAS %in% DiffUsedPAS$PAS,"Yes","No"))

#all  
HumanDomAll= HumanDom1 %>% bind_rows(HumanDom2) %>% bind_rows(HumanDom3) %>% bind_rows(HumanDom4) %>% bind_rows(HumanDom5) %>% bind_rows(HumanDom6) %>% bind_rows(HumanDom7) %>% bind_rows(HumanDom8) %>% bind_rows(HumanDom9) 
ChimpDomAll= ChimpDom1 %>% bind_rows(ChimpDom2) %>% bind_rows(ChimpDom3) %>% bind_rows(ChimpDom4) %>% bind_rows(ChimpDom5) %>% bind_rows(ChimpDom6) %>% bind_rows(ChimpDom7) %>% bind_rows(ChimpDom8) %>% bind_rows(ChimpDom9)

Look at usage correlation for matching genes:

ChimpDom5sm=ChimpDom5 %>% select(gene, Chimp)  
HumanDom5sm =HumanDom5%>% select(gene, Human)

BothDom5=ChimpDom5sm %>% inner_join(HumanDom5sm, by="gene")

cor.test(BothDom5$Chimp, BothDom5$Human)

    Pearson's product-moment correlation

data:  BothDom5$Chimp and BothDom5$Human
t = 23.464, df = 1158, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5273350 0.6054384
sample estimates:
      cor 
0.5676626 
brewer.pal(3, "Dark2")
[1] "#1B9E77" "#D95F02" "#7570B3"
usage9=ggplot(ChimpDom9,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom9, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.9") + geom_abline(slope=1, intercept = 0)

usage8=ggplot(ChimpDom8,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom8, aes(x=Human,y=Chimp), col="#D95F02",alpha=.1) + labs(title="Usage 0.8") + geom_abline(slope=1, intercept = 0)

usage7=ggplot(ChimpDom7,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom7, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.7") + geom_abline(slope=1, intercept = 0)

usage6=ggplot(ChimpDom6,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom6, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.6") + geom_abline(slope=1, intercept = 0)

usage5=ggplot(ChimpDom5,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom5, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.5") + geom_abline(slope=1, intercept = 0)

usage4=ggplot(ChimpDom4,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77",alpha=.1)+ geom_point(data=HumanDom4, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.4") + geom_abline(slope=1, intercept = 0)

usage3=ggplot(ChimpDom3,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom3, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.3") + geom_abline(slope=1, intercept = 0)

usage2=ggplot(ChimpDom2,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom2, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.2") + geom_abline(slope=1, intercept = 0)

usage1=ggplot(ChimpDom1,aes(x=Human,y=Chimp)) + geom_point(col="#1B9E77", alpha=.1)+ geom_point(data=HumanDom1, aes(x=Human,y=Chimp), col="#D95F02", alpha=.1) + labs(title="Usage 0.1") + geom_abline(slope=1, intercept = 0)
plot_grid(usage9,usage8,usage7,usage6,usage5,usage4,usage3,usage2,usage1)

Version Author Date
218a3d4 brimittleman 2020-04-16

Color by diff used:

DiffUsed1=ggplot(ChimpDom1,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom1, aes(x=Human,y=Chimp,col=Diff), alpha=.1) +  scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.1") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)

DiffUsed2=ggplot(ChimpDom2,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom2, aes(x=Human,y=Chimp,col=Diff), alpha=.1) +scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.2") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)


DiffUsed3=ggplot(ChimpDom3,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom3, aes(x=Human,y=Chimp,col=Diff), alpha=.1) + scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.3") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)

DiffUsed4=ggplot(ChimpDom4,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom4, aes(x=Human,y=Chimp,col=Diff), alpha=.1) +scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.4") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)

DiffUsed5=ggplot(ChimpDom5,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom5, aes(x=Human,y=Chimp,col=Diff), alpha=.1)+ scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.5") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)

DiffUsed6=ggplot(ChimpDom6,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom6, aes(x=Human,y=Chimp,col=Diff), alpha=.1)+ scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.6") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)

DiffUsed7=ggplot(ChimpDom7,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom7, aes(x=Human,y=Chimp,col=Diff), alpha=.1)+ scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.7") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)

DiffUsed8=ggplot(ChimpDom8,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom8, aes(x=Human,y=Chimp,col=Diff), alpha=.1)+ scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.8") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)

DiffUsed9=ggplot(ChimpDom9,aes(x=Human,y=Chimp, col=Diff)) + geom_point(alpha=.1)+ geom_point(data=HumanDom9, aes(x=Human,y=Chimp,col=Diff), alpha=.1)+ scale_color_brewer(palette = "Set2") + labs(title="Differentially used 0.9") + theme(legend.position = "none") + geom_abline(slope=1, intercept = 0)
plot_grid(DiffUsed9,DiffUsed8,DiffUsed7,DiffUsed6,DiffUsed5,DiffUsed4,DiffUsed3,DiffUsed2,DiffUsed1)

Version Author Date
218a3d4 brimittleman 2020-04-16

Look at number that are differentially used:

ChimpDomAll %>% group_by(set, Diff) %>% summarise(nPAS=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nPAS), Prop=nPAS/nSet) %>% filter(Diff=="Yes") %>% select(set, Prop)
# A tibble: 9 x 2
# Groups:   set [9]
  set     Prop
  <chr>  <dbl>
1 Chimp1 0.181
2 Chimp2 0.188
3 Chimp3 0.188
4 Chimp4 0.176
5 Chimp5 0.157
6 Chimp6 0.130
7 Chimp7 0.103
8 Chimp8 0.103
9 Chimp9 0.116
HumanDomAll %>% group_by(set, Diff) %>% summarise(nPAS=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nPAS), Prop=nPAS/nSet) %>% filter(Diff=="Yes") %>% select(set, Prop)
# A tibble: 9 x 2
# Groups:   set [9]
  set      Prop
  <chr>   <dbl>
1 Human1 0.153 
2 Human2 0.143 
3 Human3 0.126 
4 Human4 0.109 
5 Human5 0.102 
6 Human6 0.0976
7 Human7 0.0989
8 Human8 0.123 
9 Human9 0.185

Look for enrichment:

Number of Diff Used= 2342

ChimpSet=c('Chimp1','Chimp2', 'Chimp3', 'Chimp4', 'Chimp5', 'Chimp6', 'Chimp7', 'Chimp8','Chimp9')
EnrichChimp=c()
PvalueChimp=c()
for (i in ChimpSet){
  x=nrow(ChimpDomAll %>% filter(set==i, Diff=="Yes"))
  m=nrow(DiffUsedPAS)
  n=nrow(DiffUsedPASAll) - nrow(DiffUsedPAS)
  k=nrow(ChimpDomAll %>% filter(set==i))
  N=nrow(DiffUsedPASAll)
  PvalueChimp=c(PvalueChimp, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichChimp=c(EnrichChimp, enrich)
}

PvalueChimp
[1] 1.309943e-285 6.767822e-224 1.175091e-170 5.840054e-113  4.512400e-64
[6]  1.163307e-28  2.453618e-10  1.013167e-06  4.981763e-04
EnrichChimp
[1] 3.322681 3.449294 3.449573 3.233257 2.879704 2.395185 1.897135 1.898764
[9] 2.124413
HumanSet=c('Human1','Human2', 'Human3', 'Human4', 'Human5', 'Human6', 'Human7', 'Human8','Human9')
EnrichHuman=c()
PvalueHuman=c()
for (i in HumanSet){
  x=nrow(HumanDomAll %>% filter(set==i, Diff=="Yes"))
  m=nrow(DiffUsedPAS)
  n=nrow(DiffUsedPASAll) - nrow(DiffUsedPAS)
  k=nrow(HumanDomAll %>% filter(set==i))
  N=nrow(DiffUsedPASAll)
  PvalueHuman=c(PvalueHuman, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichHuman=c(EnrichHuman, enrich)
}

PvalueHuman
[1] 9.042014e-150  6.644427e-86  5.864629e-44  1.517557e-21  2.167131e-13
[6]  8.227226e-09  2.073167e-06  8.079790e-07  4.076070e-05
EnrichHuman
[1] 2.812383 2.626817 2.309448 2.005038 1.871647 1.794124 1.817424 2.253682
[9] 3.392524

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] RColorBrewer_1.1-2 cowplot_0.9.4      forcats_0.3.0     
 [4] stringr_1.3.1      dplyr_0.8.0.1      purrr_0.3.2       
 [7] readr_1.3.1        tidyr_0.8.3        tibble_2.1.1      
[10] 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  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [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] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10  
[49] httr_1.3.1       rstudioapi_0.10  R6_2.3.0         nlme_3.1-137    
[53] git2r_0.26.1     compiler_3.5.1