Last updated: 2020-04-23

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.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/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.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.

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File Version Author Date Message
Rmd c1bc496 brimittleman 2020-04-23 add interaction density, dapa and e and order proble
html add6b2a brimittleman 2020-04-23 Build site.
Rmd 5168eee brimittleman 2020-04-23 fix expected, and p
html 3790efa brimittleman 2020-04-23 Build site.
Rmd e513e9f brimittleman 2020-04-23 add dot chart
html 53a7570 brimittleman 2020-04-22 Build site.
Rmd b4e617e brimittleman 2020-04-22 add color and prop dom, add decay
html a60094e brimittleman 2020-04-21 Build site.
Rmd 2b63d02 brimittleman 2020-04-21 add new dom and de/dapa

I will look at enrichment for dAPA genes in gene with same and different domiant pas.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(cowplot)

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

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

    ggsave
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ───────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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✖ dplyr::filter()    masks stats::filter()
✖ cowplot::ggsave()  masks ggplot2::ggsave()
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✖ purrr::set_names() masks magrittr::set_names()
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_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10) 
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)

BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header=T,stringsAsFactors=F)
DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header=T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene, "Yes", "No"))

All= BothResAll  %>% select(gene,cut,Set) %>% inner_join(DiffIso, by="gene")

Enrichment:

Pval=c()
Enrich=c()
set=c(10,20,30,40,50,60,70,80,90)
expected=c()
actual=c()
for (i in set){
  x=nrow(All %>% filter(cut==i, Set=="Different", dAPA=="Yes"))
  actual=c(actual,x)
  m=nrow(All %>% filter(cut==i, dAPA=="Yes"))
  n=nrow(All %>% filter(cut==i, dAPA=="No"))
  k=nrow(All %>% filter(cut==i, Set=="Different"))
  N=nrow(All %>% filter(cut==i))
  val=phyper(x-1,m,n,k,lower.tail=F)
  Pval= c(Pval, val)
  en=(x/k)/(m/N)
  #ex=which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
  ex=k*(m/N)
  expected=c(expected,ex)
  Enrich=c(Enrich, en)
}

ResDF=as.data.frame(cbind(set,Pval,Enrich,actual,expected))
ResDF$set=as.factor(ResDF$set)
ResDF$Pval=as.numeric(as.character(ResDF$Pval))
ResDF$Enrich=as.numeric(as.character(ResDF$Enrich))

diffp=ggplot(ResDF,aes(x=set, y=-log10(Pval),fill=set)) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for dAPA and different dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")

diffe=ggplot(ResDF,aes(x=set, y=Enrich, fill=set)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for dAPA and different dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")
ResDF
  set          Pval   Enrich actual   expected
1  10 1.390016e-188 2.414802    704 291.535347
2  20 1.103542e-201 3.206692    467 145.632926
3  30 2.817467e-163 3.933034    283  71.954625
4  40 1.441990e-124 4.641345    178  38.350954
5  50  7.617389e-91 5.107914    118  23.101408
6  60  4.656864e-62 5.800000     75  12.931034
7  70  1.103789e-35 7.043478     39   5.537037
8  80  1.695198e-25 7.242105     27   3.728198
9  90  5.072762e-12 7.343750     12   1.634043
PvalSame=c()
EnrichSame=c()
expectedSame=c()
actualSame=c()

for (i in set){
  x=nrow(All %>% filter(cut==i, Set=="Same", dAPA=="Yes"))
  actualSame=c(actualSame, x)
  m=nrow(All %>% filter(cut==i, dAPA=="Yes"))
  n=nrow(All %>% filter(cut==i, dAPA=="No"))
  k=nrow(All %>% filter(cut==i, Set=="Same"))
  N=nrow(All %>% filter(cut==i))
  val=phyper(x-1,m,n,k,lower.tail=F)
  PvalSame= c(PvalSame, val)
  en=(x/k)/(m/N)
  EnrichSame=c(EnrichSame, en)
  #ex=which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
  ex=k*(m/N)
  expectedSame=c(expectedSame,ex)
}

ResDFSame=as.data.frame(cbind(set,PvalSame,EnrichSame, actualSame,expectedSame))
ResDFSame$set=as.factor(ResDFSame$set)
ResDFSame$PvalSame=as.numeric(as.character(ResDFSame$PvalSame))
ResDFSame$EnrichSame=as.numeric(as.character(ResDFSame$EnrichSame))

samep=ggplot(ResDFSame,aes(x=set, y=-log10(PvalSame),fill=set)) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for dAPA and same dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")

samee=ggplot(ResDFSame,aes(x=set, y=EnrichSame,fill=set)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for dAPA and same dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")
ResDFSame
  set PvalSame EnrichSame actualSame expectedSame
1  10        1  0.6125145        652   1064.46465
2  20        1  0.6496854        596    917.36707
3  30        1  0.7093221        515    726.04538
4  40        1  0.7392900        396    535.64905
5  50        1  0.7590786        299    393.89859
6  60        1  0.7631579        200    262.06897
7  70        1  0.7847528        122    155.46296
8  80        1  0.7450275         68     91.27180
9  90        1  0.6586323         20     30.36596

Proportion of dAPA genes:

PropDapa=as.data.frame(cbind(set, OverlapdAPA=actual, dAPA=rep(nrow(dAPAGenes),9))) %>% mutate(Prop=OverlapdAPA/dAPA)
PropDapa$set=as.factor(PropDapa$set)
ggplot(PropDapa, aes(x=set,y=Prop,fill=set)) + geom_bar(stat="identity")+ labs(title="Proportion of dAPA genes with different dominant PAS", y="Proportion of dAPA genes",x="Dominance Cutoff")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")+geom_text(aes(label=OverlapdAPA), position=position_dodge(width=0.9), vjust=1.5)

Version Author Date
3790efa brimittleman 2020-04-23
53a7570 brimittleman 2020-04-22 
plot_grid(diffe,samee, diffp, samep)

Version Author Date
add6b2a brimittleman 2020-04-23
53a7570 brimittleman 2020-04-22

Plot together:

ResDFfix=ResDF %>% mutate(type="Different")
ResDFSamefix=ResDFSame %>% mutate(type="Same") 

colnames(ResDFSamefix)=colnames(ResDFfix)
ResDFfix_both=ResDFfix %>% bind_rows(ResDFSamefix)
dotplot=ggdotchart(ResDFfix_both,y="Enrich",x= "set", color="set",add = "segments", rotate = TRUE, dot.size = 10,  label = round(ResDFfix_both$Enrich,2), font.label = list(color = "black", size = 10, vjust = 0.5),ggtheme = theme_pubr(), legend="none", palette="RdYlBu", title="Enrichment for dAPA by Domianance") + geom_hline(yintercept = 1) + facet_grid(~type) + labs(x="Dominance Cutoff", y="Enrichement")

dotplot

Wont build with this: use ggsave:

ggsave("../output/dAPAandDomEnrich.png",dotplot, width=12)

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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    tidyverse_1.2.1
 [9] cowplot_0.9.4   ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  
[13] workflowr_1.6.0

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         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       broom_0.5.1        Rcpp_1.0.2        
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] 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       
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1