Last updated: 2020-04-17
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Knit directory: Comparative_APA/analysis/
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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
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── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
library(RColorBrewer)
I will ask if there if dominance and DE are related. First I can ask if genes with dominant PAS are enriched in the DE genes.
PAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
MetaCol=colnames(PAS)
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)
DE_yes= DE %>% filter(DE=="Yes")
Domiance
#9
HumanDom9=read.table("../data/DomDefGreaterX/Human_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human9") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom9=read.table("../data/DomDefGreaterX/Chimp_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp9") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#8
HumanDom8=read.table("../data/DomDefGreaterX/Human_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human8")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom8=read.table("../data/DomDefGreaterX/Chimp_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp8") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#7
HumanDom7=read.table("../data/DomDefGreaterX/Human_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human7")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom7=read.table("../data/DomDefGreaterX/Chimp_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp7")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#6
HumanDom6=read.table("../data/DomDefGreaterX/Human_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human6") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom6=read.table("../data/DomDefGreaterX/Chimp_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp6") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#5
HumanDom5=read.table("../data/DomDefGreaterX/Human_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human5") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom5=read.table("../data/DomDefGreaterX/Chimp_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp5")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#4
HumanDom4=read.table("../data/DomDefGreaterX/Human_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human4")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom4=read.table("../data/DomDefGreaterX/Chimp_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp4")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#3
HumanDom3=read.table("../data/DomDefGreaterX/Human_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human3") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom3=read.table("../data/DomDefGreaterX/Chimp_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp3")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#2
HumanDom2=read.table("../data/DomDefGreaterX/Human_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human2")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom2=read.table("../data/DomDefGreaterX/Chimp_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp2")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
#1
HumanDom1=read.table("../data/DomDefGreaterX/Human_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human1")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom1=read.table("../data/DomDefGreaterX/Chimp_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp1") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"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)
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, DE=="Yes"))
m=nrow(DE_yes)
n=nrow(DE) - nrow(DE_yes)
k=nrow(ChimpDomAll %>% filter(set==i))
N=nrow(DE)
PvalueChimp=c(PvalueChimp, phyper(x,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichChimp=c(EnrichChimp, enrich)
}
PvalueChimp
[1] 1.0000000 1.0000000 1.0000000 0.9999842 0.9999501 0.9971507 0.9994521
[8] 0.9839957 0.5079290
EnrichChimp
[1] 0.8849697 0.8877147 0.8880516 0.9057939 0.8959653 0.9108136 0.8719019
[8] 0.8855119 0.9896978
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, DE=="Yes"))
m=nrow(DE_yes)
n=nrow(DE) - nrow(DE_yes)
k=nrow(HumanDomAll %>% filter(set==i))
N=nrow(DE)
PvalueHuman=c(PvalueHuman, phyper(x,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichHuman=c(EnrichHuman, enrich)
}
PvalueHuman
[1] 1.0000000 1.0000000 0.9999997 0.9999064 0.9992779 0.9991470 0.9853263
[8] 0.6526253 0.6747532
EnrichHuman
[1] 0.8885286 0.8749491 0.8850791 0.8975969 0.8974753 0.8785672 0.8903590
[8] 0.9674743 0.9054783
No enrichment for these. The real question is if genes with different dominant PAS are DE. This requires chosing how to call different dominant.
Are genes with different dominant at the cutoff .4 cutoff enriched for DE:
FourRes=read.table("../data/DomStructure_4/InclusiveDominantPASat4.txt", header = T,stringsAsFactors = F)
FourRes_diff= FourRes %>% filter(Set=="Different")
FourRes_same= FourRes %>% filter(Set=="Same")
x=length(intersect(FourRes_diff$gene,DE_yes$gene))
m=nrow(DE_yes)
n=nrow(DE) - nrow(DE_yes)
k=nrow(FourRes %>% filter(Set=="Different"))
N=nrow(DE)
phyper(x,m,n,k,lower.tail=F)
[1] 0.09585894
(x/k)/(m/N)
[1] 1.117149
x=length(intersect(FourRes_same$gene,DE_yes$gene))
m=nrow(DE_yes)
n=nrow(DE) - nrow(DE_yes)
k=nrow(FourRes %>% filter(Set=="Same"))
N=nrow(DE)
phyper(x,m,n,k,lower.tail=F)
[1] 0.9999999
(x/k)/(m/N)
[1] 0.883703
This is conditioned on the gene having a dominant PAS.
do this based on a set tested both
All4= FourRes %>% select(gene,Set) %>% inner_join(DE, by="gene")
x=nrow(All4 %>% filter(Set=="Different", DE=="Yes"))
m=nrow(All4 %>% filter( DE=="Yes"))
n=nrow(All4 %>% filter( DE=="No"))
k=nrow(All4 %>% filter(Set=="Different"))
N=nrow(All4)
phyper(x,m,n,k,lower.tail=F)
[1] 0.0009822532
(x/k)/(m/N)
[1] 1.305745
I am not sure what the set should be.
x=nrow(All4 %>% filter(Set=="Same", DE=="Yes"))
m=nrow(All4 %>% filter(DE=="Yes"))
n=nrow(All4 %>% filter(DE=="No"))
k=nrow(All4 %>% filter(Set=="Same"))
N=nrow(All4)
phyper(x,m,n,k,lower.tail=F)
[1] 0.9982958
(x/k)/(m/N)
[1] 0.9792784
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)
Pval=c()
Enrich=c()
set=c(10,20,30,40,50,60,70,80,90)
All4= BothResAll %>% select(gene,cut,Set) %>% inner_join(DE, by="gene")
for (i in set){
x=nrow(All4 %>% filter(cut==i, Set=="Different", DE=="Yes"))
m=nrow(All4 %>% filter(cut==i, DE=="Yes"))
n=nrow(All4 %>% filter(cut==i, DE=="No"))
k=nrow(All4 %>% filter(cut==i, Set=="Different"))
N=nrow(All4 %>% filter(cut==i))
val=phyper(x,m,n,k,lower.tail=F)
Pval= c(Pval, val)
en=(x/k)/(m/N)
Enrich=c(Enrich, en)
}
ResDF=as.data.frame(cbind(set,Pval,Enrich))
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 DE 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 DE and different dominant \n condition on tested in both")
PvalSame=c()
EnrichSame=c()
for (i in set){
x=nrow(All4 %>% filter(cut==i, Set=="Same", DE=="Yes"))
m=nrow(All4 %>% filter(cut==i, DE=="Yes"))
n=nrow(All4 %>% filter(cut==i, DE=="No"))
k=nrow(All4 %>% filter(cut==i, Set=="Same"))
N=nrow(All4 %>% filter(cut==i))
val=phyper(x,m,n,k,lower.tail=F)
PvalSame= c(PvalSame, val)
en=(x/k)/(m/N)
EnrichSame=c(EnrichSame, en)
}
ResDFSame=as.data.frame(cbind(set,PvalSame,EnrichSame))
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 DE 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 DE and same dominant \n condition on tested in both")
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 rlang_0.4.0
[9] later_0.7.5 pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] modelr_0.1.2 readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[17] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2 evaluate_0.12
[21] labeling_0.3 knitr_1.20 httpuv_1.4.5 broom_0.5.1
[25] Rcpp_1.0.2 promises_1.0.1 scales_1.0.0 backports_1.1.2
[29] jsonlite_1.6 fs_1.3.1 hms_0.4.2 digest_0.6.18
[33] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2 cli_1.1.0
[37] tools_3.5.1 magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[41] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4
[45] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10
[49] R6_2.3.0 nlme_3.1-137 git2r_0.26.1 compiler_3.5.1