New dominance and dAPA

Last updated: 2020-04-21

Checks: 7 0

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/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.

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	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(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()

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))

ggplot(ResDF,aes(x=set, y=-log10(Pval))) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for dAPA and different dominant \n condition on tested in both")

ggplot(ResDF,aes(x=set, y=Enrich)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for dAPA and different dominant \n condition on tested in both")

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))

ggplot(ResDFSame,aes(x=set, y=-log10(PvalSame))) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for dAPA and same dominant \n condition on tested in both")

ggplot(ResDFSame,aes(x=set, y=EnrichSame)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for dAPA and same dominant \n condition on tested in both")

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

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    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4

New dominance and dAPA

Briana Mittleman

4/20/2020