Last updated: 2020-04-22

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    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/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 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(cowplot)
Loading required package: ggplot2

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

    ggsave
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter()   masks stats::filter()
✖ cowplot::ggsave() masks ggplot2::ggsave()
✖ dplyr::lag()      masks stats::lag()
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,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)))
  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 1.797208e-189 2.414802    704      291
2  20 6.026541e-203 3.206692    467      145
3  30 5.272942e-165 3.933034    283       72
4  40 5.937071e-127 4.641345    178       38
5  50  0.000000e+00 5.107914    118       23
6  60  0.000000e+00 5.800000     75       13
7  70  0.000000e+00 7.043478     39        5
8  80  0.000000e+00 7.242105     27        3
9  90  0.000000e+00 7.343750     12        1
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,m,n,k,lower.tail=F)
  PvalSame= c(PvalSame, round(val,3))
  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)))
  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          652
2  20        1  0.6496854        596          596
3  30        1  0.7093221        515          515
4  40        1  0.7392900        396          396
5  50        1  0.7590786        299          299
6  60        1  0.7631579        200          200
7  70        1  0.7847528        122          122
8  80        1  0.7450275         68           68
9  90        1  0.6586323         20           20

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", alpha=.5)+ labs(title="Proportion of dAPA genes with different dominant PAS", y="Proportion of dAPA genes")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")+geom_text(aes(label=OverlapdAPA), position=position_dodge(width=0.9), vjust=1.5)

plot_grid(diffe,samee, diffp, samep)


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   ggplot2_3.1.1   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         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[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