Last updated: 2020-01-25

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
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Rmd 0f7b377 brimittleman 2020-01-25 add graphs for CM
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Rmd 908f02d brimittleman 2020-01-23 add compare filter and write out for top SS
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Rmd c3a9af5 brimittleman 2020-01-23 add all ss and choose 1
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Rmd 2e66af9 brimittleman 2020-01-21 add ss and PAS num DF

Top 2 SS

In this analysis I will look at the signal site distributions for the human and chimp PAS I have called.

library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(workflowr)
This is workflowr version 1.5.0
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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I am looking at 200 base pair regions for each pas. I will look for the sequence in these for now and then refine the search.

I can use bedtools nuc on both to get the sequences for the bed files in ../data/PAS.

mkdir ../data/SignalSites_doublefilter
sbatch PASsequences_DF.sh

The way I did this it flipped the - strand and assayed the correct strand sequence. I will still have to make everything upper case.

Before I use python to find the occurances. I will look at the results because I gave the AATAAA pattern to the nuc program to assay.

First i have to remove the # in each file

humanRawout=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_nuc.txt", stringsAsFactors = F, header = T) %>% mutate(SS=ifelse(X17_user_patt_count>=1, "yes", "no"))
ChimpRawout=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_nuc.txt", stringsAsFactors = F, header = T)%>% mutate(SS=ifelse(X17_user_patt_count>=1, "yes", "no"))

Histogram for the results:

ggplot(humanRawout,aes(x=X17_user_patt_count)) + geom_bar(aes(y=..prop..)) +labs(title="Distribution of AATAAA pattern Human")

Version Author Date
5525b39 brimittleman 2020-01-21 
ggplot(ChimpRawout,aes(x=X17_user_patt_count)) + geom_bar(aes(y=..prop..))+labs(title="Distribution of AATAAA pattern Chimps")

Version Author Date
5525b39 brimittleman 2020-01-21

See if yes no segragates with usage:

ggplot(humanRawout,aes(x=SS,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test")

Version Author Date
5525b39 brimittleman 2020-01-21 
ggplot(ChimpRawout,aes(x=SS,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test")

Version Author Date
5525b39 brimittleman 2020-01-21

Look at location data and bring this in.

Loc=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% rename("X4_usercol"=PAS) %>% dplyr::select(X4_usercol,loc)

ChimpRawout_withloc=ChimpRawout %>% inner_join(Loc, by="X4_usercol") %>% filter(loc!="008559")
humanRawout_withloc=humanRawout%>% inner_join(Loc, by="X4_usercol") %>% filter(loc!="008559")
ggplot(humanRawout_withloc,aes(x=loc,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

Version Author Date
5525b39 brimittleman 2020-01-21 
ggplot(ChimpRawout_withloc,aes(x=loc,y=X5_usercol,by=SS, fill=SS)) + geom_boxplot() + labs(x="Presence of AATAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

Version Author Date
5525b39 brimittleman 2020-01-21

I can run the nuc command again for the other doninant signal site I found in the apaQTL analysis (ATTAAA), I can join the results.

sbatch PAS_ATTAAA_df.sh

remove #

human_ATTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_ATTAAA.txt",stringsAsFactors = F,header = T) %>% mutate(SS2=ifelse(X17_user_patt_count>=1, "yes", "no"))

chimp_ATTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_ATTAAA.txt",stringsAsFactors = F,header = T) %>% mutate(SS2=ifelse(X17_user_patt_count>=1, "yes", "no"))


human_both=human_ATTAAA %>% inner_join(humanRawout_withloc, by=c("X1_usercol", "X2_usercol", "X3_usercol", "X4_usercol", "X5_usercol", "X6_usercol", "X7_pct_at", "X8_pct_gc", "X9_num_A", "X10_num_C", "X11_num_G", "X12_num_T", "X13_num_N", "X14_num_oth", "X15_seq_len", "X16_seq")) %>% mutate(anySS=ifelse(SS == "yes" | SS2 =="yes", "yes", "no"))

chimp_both=chimp_ATTAAA %>% inner_join(ChimpRawout_withloc, by=c("X1_usercol", "X2_usercol", "X3_usercol", "X4_usercol", "X5_usercol", "X6_usercol", "X7_pct_at", "X8_pct_gc", "X9_num_A", "X10_num_C", "X11_num_G", "X12_num_T", "X13_num_N", "X14_num_oth", "X15_seq_len", "X16_seq")) %>% mutate(anySS=ifelse(SS == "yes" | SS2 =="yes", "yes", "no"))
ggplot(human_both,aes(x=loc,y=X5_usercol,by=SS2, fill=SS2)) + geom_boxplot() + labs(x="Presence of  ATTAAA", y="Human mean usage",title="Human usage by presense of at least 1  ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

Version Author Date
5525b39 brimittleman 2020-01-21 
ggplot(chimp_both,aes(x=loc,y=X5_usercol,by=SS2, fill=SS2)) + geom_boxplot() + labs(x="Presence of ATTAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1  ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

Version Author Date
5525b39 brimittleman 2020-01-21 
ggplot(human_both,aes(x=loc,y=X5_usercol,by=anySS, fill=anySS)) + geom_boxplot() + labs(x="Presence of AATAAA or ATTAAA", y="Human mean usage",title="Human usage by presense of at least 1 AATAAA or ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",label.y.npc = "bottom")

Version Author Date
5525b39 brimittleman 2020-01-21 
ggplot(chimp_both,aes(x=loc,y=X5_usercol,by=anySS, fill=anySS)) + geom_boxplot() + labs(x="Presence of AATAAA or ATTAAA", y="Chimp mean usage",title="Chimp usage by presense of at least 1 AATAAA or ATTAAA") + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")

Version Author Date
5525b39 brimittleman 2020-01-21

Plot percentage either by loc:

human_both_loc= human_both %>% group_by(loc, anySS) %>% summarise(count=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(count),Human=count/nLoc) %>%ungroup() %>%  dplyr::select(loc, anySS,Human)

chimp_both_loc= chimp_both %>% group_by(loc, anySS) %>% summarise(count=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(count),Chimp=count/nLoc)%>% ungroup() %>% dplyr::select(loc, anySS,Chimp)

bothSpeciesLoc=chimp_both_loc %>% inner_join(human_both_loc,by=c("loc", "anySS")) %>% gather(key="species", value="propSS", -loc, -anySS) %>% filter(anySS=="yes")


ggplot(bothSpeciesLoc, aes(x=loc, fill=species,y=propSS)) + geom_bar(stat="identity",position = "dodge") +  scale_fill_brewer(palette = "Dark2") + labs(title="Presence of top 2 signal sites by location", x="Proportion with signal site", x="location")

Version Author Date
5525b39 brimittleman 2020-01-21

Write out information about SS so i can use it for other anaylsis.

human_write=human_both %>% dplyr::select(X4_usercol,SS,SS2,anySS) %>% rename("PAS"=X4_usercol)

write.table(human_write, "../data/SignalSites_doublefilter/HumanPresenceofSS_DF.txt", col.names = T, row.names = F, quote = F)

chimp_write=chimp_both %>% dplyr::select(X4_usercol,SS,SS2,anySS) %>% rename("PAS"=X4_usercol)

write.table(chimp_write,"../data/SignalSites_doublefilter/ChimpPresenceofSS_DF.txt", col.names = T, row.names = F, quote = F)

Expand

I previously just looked at the top 2 signal sites. Now I will write a loop to run this on the remaining 10.

AAAAAG AATACA AATAGA AATATA ACTAAA AGTAAA CATAAA GATAAA TATAAA AAAAAA

Human_AATAAA= humanRawout %>% rename("Human_AATAAA"=X17_user_patt_count, "PAS"=X4_usercol) %>% dplyr::select(PAS, Human_AATAAA)

Human_ATTAAA= human_ATTAAA %>% rename("Human_ATTAAA"=X17_user_patt_count, "PAS"=X4_usercol) %>% dplyr::select(PAS, Human_ATTAAA)

Human_AAAAAG=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_AAAAAG.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_AAAAAG")) %>% dplyr::select(PAS, Human_AAAAAG)

Human_AATACA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_AATACA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_AATACA")) %>% dplyr::select(PAS, Human_AATACA)
 
Human_AATAGA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_AATAGA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_AATAGA")) %>% dplyr::select(PAS, Human_AATAGA)
 

Human_AATATA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_AATATA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_AATATA")) %>% dplyr::select(PAS, Human_AATATA)  


Human_ACTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_ACTAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_ACTAAA")) %>% dplyr::select(PAS, Human_ACTAAA)  

Human_AGTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_AGTAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_AGTAAA")) %>% dplyr::select(PAS, Human_AGTAAA)  


Human_CATAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_CATAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_CATAAA")) %>% dplyr::select(PAS, Human_CATAAA)  

Human_GATAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_GATAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_GATAAA")) %>% dplyr::select(PAS, Human_GATAAA)  

Human_TATAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_TATAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_TATAAA")) %>% dplyr::select(PAS, Human_TATAAA)  


Human_AAAAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_AAAAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Human_AAAAAA")) %>% dplyr::select(PAS, Human_AAAAAA)  
Chimp_AATAAA= ChimpRawout %>% rename("Chimp_AATAAA"=X17_user_patt_count, "PAS"=X4_usercol) %>% dplyr::select(PAS, Chimp_AATAAA)

Chimp_ATTAAA= chimp_ATTAAA %>% rename("Chimp_ATTAAA"=X17_user_patt_count, "PAS"=X4_usercol) %>% dplyr::select(PAS, Chimp_ATTAAA)


Chimp_AAAAAG=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_AAAAAG.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_AAAAAG")) %>% dplyr::select(PAS, Chimp_AAAAAG)

Chimp_AATACA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_AATACA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_AATACA")) %>% dplyr::select(PAS, Chimp_AATACA)
 
Chimp_AATAGA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_AATAGA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_AATAGA")) %>% dplyr::select(PAS, Chimp_AATAGA)
 

Chimp_AATATA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_AATATA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_AATATA")) %>% dplyr::select(PAS, Chimp_AATATA)  


Chimp_ACTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_ACTAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_ACTAAA")) %>% dplyr::select(PAS, Chimp_ACTAAA)  

Chimp_AGTAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_AGTAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_AGTAAA")) %>% dplyr::select(PAS, Chimp_AGTAAA)  


Chimp_CATAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_CATAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_CATAAA")) %>% dplyr::select(PAS, Chimp_CATAAA)  

Chimp_GATAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_GATAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_GATAAA")) %>% dplyr::select(PAS, Chimp_GATAAA)  

Chimp_TATAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_TATAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_TATAAA")) %>% dplyr::select(PAS, Chimp_TATAAA)  


Chimp_AAAAAA=read.table("../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_AAAAAA.txt",stringsAsFactors = F,col.names=c("chr","start", "end", "PAS", "Human", "strand", "pcAT", "pcGC", "A", "C", "G", "T","N","oth", "leng", "Chimp_AAAAAA")) %>% dplyr::select(PAS, Chimp_AAAAAA)

Join all of these by PAS

Human_allPAS=Human_AATAAA %>% inner_join(Human_ATTAAA, by="PAS")  %>% inner_join(Human_AAAAAG, by="PAS")  %>% inner_join(Human_AATACA, by="PAS") %>% inner_join(Human_AATAGA, by="PAS") %>% inner_join(Human_AATATA, by="PAS") %>% inner_join(Human_ACTAAA, by="PAS") %>% inner_join(Human_AGTAAA, by="PAS") %>% inner_join(Human_CATAAA, by="PAS") %>% inner_join(Human_GATAAA, by="PAS") %>% inner_join(Human_TATAAA, by="PAS") %>% inner_join(Human_AAAAAA, by="PAS")


Chimp_allPAS=Chimp_AATAAA %>% inner_join(Chimp_ATTAAA, by="PAS")  %>% inner_join(Chimp_AAAAAG, by="PAS")  %>% inner_join(Chimp_AATACA, by="PAS") %>% inner_join(Chimp_AATAGA, by="PAS") %>% inner_join(Chimp_AATATA, by="PAS") %>% inner_join(Chimp_ACTAAA, by="PAS") %>% inner_join(Chimp_AGTAAA, by="PAS") %>% inner_join(Chimp_CATAAA, by="PAS") %>% inner_join(Chimp_GATAAA, by="PAS") %>% inner_join(Chimp_TATAAA, by="PAS") %>% inner_join(Chimp_AAAAAA, by="PAS")

Gather these

Human_allPAS_gather=Human_allPAS %>% gather("Site", "Count",-PAS) %>% mutate(Identified=ifelse(Count>=1, "Y", "N")) %>% separate(Site, into=c("Species", "Signal"), by="_")

Chimp_allPAS_gather=Chimp_allPAS %>% gather("Site", "Count",-PAS) %>% mutate(Identified=ifelse(Count>=1, "Y", "N"))%>% separate(Site, into=c("Species", "Signal"), by="_")

Both_AllPAS_ident= Chimp_allPAS_gather %>% bind_rows(Human_allPAS_gather) %>% filter(Identified=="Y")


Both_AllPAS_group= Both_AllPAS_ident %>% group_by(Species, Signal) %>% summarise(n=n()) %>% mutate(NPAS=42318, propW=n/NPAS)

Plot:

ggplot(Both_AllPAS_ident, aes(x=Signal, by=Species, fill=Species)) + geom_bar(stat="count",position = "dodge")+ theme(axis.text.x = element_text(angle = 90)) + scale_fill_brewer(palette = "Dark2")

Version Author Date
f3aa6b1 brimittleman 2020-01-22 
ggplot(Both_AllPAS_group, aes(x=Signal, by=Species, fill=Species,y=propW)) + geom_bar(stat="identity",position = "dodge")+ theme(axis.text.x = element_text(angle = 90)) + scale_fill_brewer(palette = "Dark2")

Version Author Date
f3aa6b1 brimittleman 2020-01-22

This is not accounting for more than 1. I need to chose in a hierarchical way. I think I will use these proportions.

I want to see how many signals are identified per PAS

Chimp_allPAS_gather_site= Chimp_allPAS_gather %>% filter(Identified=="Y") %>% group_by(PAS) %>% summarise(nPerPAS_Chimp=n())

Human_allPAS_gather_site= Human_allPAS_gather %>% filter(Identified=="Y") %>% group_by(PAS) %>% summarise(nPerPAS_Human=n())


BothwithninSite=Chimp_allPAS_gather_site %>% inner_join(Human_allPAS_gather_site, by="PAS")

Plot:

ggplot(BothwithninSite, aes(x=nPerPAS_Chimp, y=nPerPAS_Human)) + geom_point() + geom_smooth(method="lm")

Version Author Date
f3aa6b1 brimittleman 2020-01-22 
ggplot(BothwithninSite, aes(x=nPerPAS_Chimp)) + geom_bar()

Version Author Date
f3aa6b1 brimittleman 2020-01-22 
ggplot(BothwithninSite, aes(x=nPerPAS_Human)) + geom_bar()

Version Author Date
f3aa6b1 brimittleman 2020-01-22

Ok similar distributions. I can hierarchically chose in both with the same parameter.

AATAAA, ATTAAA, AAAAAG, AAAAAA, TATAAA, AATATA, AGTAAA, AATACA, GATAAA, AATAGA, CATAAA, ACTAAA

I will do this seperately for human and chimp per PAS.

I can make a script in python that makes a dictionary for each PAS with the signals that are identified for it. After that I can use the heiarchical model to choose the signal.

I can do the signal with a dictionary so each PAS is given a number. I will chose the minimun number

Write out the files for this:

write.table(Human_allPAS_gather, "../data/SignalSites_doublefilter/HumanAllSignalSiteInfo.txt", col.names = F, row.names = F, quote = F)

write.table(Chimp_allPAS_gather, "../data/SignalSites_doublefilter/ChimpAllSignalSiteInfo.txt", col.names = F, row.names = F, quote = F)
python chooseSignalSite.py ../data/SignalSites_doublefilter/HumanAllSignalSiteInfo.txt ../data/SignalSites_doublefilter/HumanSignalSiteperPAS.txt

python chooseSignalSite.py ../data/SignalSites_doublefilter/ChimpAllSignalSiteInfo.txt ../data/SignalSites_doublefilter/ChimpSignalSiteperPAS.txt
SS=c('AATAAA', 'ATTAAA', 'AAAAAG', 'AAAAAA', 'TATAAA', 'AATATA', 'AGTAAA', 'AATACA', 'GATAAA', 'AATAGA', 'CATAAA', 'ACTAAA')
SS_numer=seq(1,12)

SS_DF=as.data.frame(cbind(SS, SS_numer))

SS_DF$SS_numer=as.numeric(as.character(SS_DF$SS_numer))
Human1Per=read.table("../data/SignalSites_doublefilter/HumanSignalSiteperPAS.txt",col.names = c("PAS", "SS_numer"), stringsAsFactors = F) %>% full_join(SS_DF, by="SS_numer") %>% mutate(Species="Human")

Chimp1Per=read.table("../data/SignalSites_doublefilter/ChimpSignalSiteperPAS.txt",col.names = c("PAS", "SS_numer"), stringsAsFactors = F) %>% full_join(SS_DF, by="SS_numer") %>% mutate(Species="Chimp") 


Both1Per=Human1Per %>% bind_rows(Chimp1Per)

Plot

ggplot(Both1Per,aes(x=SS, by=Species, fill=Species)) + geom_bar(stat="count",position = "dodge")+ theme(axis.text.x = element_text(angle = 90)) + scale_fill_brewer(palette = "Dark2")

Version Author Date
86dc150 brimittleman 2020-01-23

Look and see if the sites are the same

Chimp1Petojoin=Chimp1Per %>% rename("Chimp"=SS) %>% dplyr::select(PAS, Chimp )
Human1Petojoin=Human1Per %>% rename("Human"=SS) %>% dplyr::select(PAS, Human )


Both1perJoin=Chimp1Petojoin %>% full_join(Human1Petojoin,by="PAS")
Both1perJoin$Chimp=as.character(Both1perJoin$Chimp)
Both1perJoin$Human=as.character(Both1perJoin$Human)
Both1perJoin= Both1perJoin %>%  mutate(Chimp = replace_na(Chimp, "None"),Human = replace_na(Human, "None"))

ChimpNone=Both1perJoin %>% filter(Chimp=="None")

HumanNone=Both1perJoin %>% filter(Human=="None")

Plot when the other has none, what is the SS

ggplot(ChimpNone,aes(x=Human))+ geom_bar(stat="count")+ theme(axis.text.x = element_text(angle = 90))

Version Author Date
86dc150 brimittleman 2020-01-23 
ggplot(HumanNone,aes(x=Chimp))+ geom_bar(stat="count")+ theme(axis.text.x = element_text(angle = 90))

Version Author Date
86dc150 brimittleman 2020-01-23

Now I want to add usage:

PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T)

MetaPASwSS=Both1perJoin %>% rename("ChimpPAS"=Chimp, "HumanPAS"=Human) %>%  full_join(PASMeta,by="PAS") %>% mutate(ChimpPAS = replace_na(ChimpPAS, "None"),HumanPAS = replace_na(HumanPAS, "None"))

Plot usage average by SS

#human
ggplot(MetaPASwSS, aes(x=HumanPAS,y=Human)) + geom_boxplot()+ theme(axis.text.x = element_text(angle = 90))

Version Author Date
86dc150 brimittleman 2020-01-23 
#chimp  
ggplot(MetaPASwSS, aes(x=ChimpPAS,y=Chimp)) + geom_boxplot()+ theme(axis.text.x = element_text(angle = 90))

Version Author Date
86dc150 brimittleman 2020-01-23

Make the above plots wiht both speicies:

I need to seperate and bind the rows.

MetaPASwSS_groupC=MetaPASwSS %>% dplyr::select(PAS,ChimpPAS,Chimp) %>% mutate(Species="Chimp") %>% rename("Signal"=ChimpPAS, "Usage"=Chimp)

MetaPASwSS_groupH=MetaPASwSS %>% dplyr::select(PAS,HumanPAS,Human) %>% mutate(Species="Human") %>% rename("Signal"=HumanPAS, "Usage"=Human)

MetaPASwSSBoth=MetaPASwSS_groupC %>% bind_rows(MetaPASwSS_groupH)

ggplot(MetaPASwSSBoth, aes(x=Signal, y=Usage, by=Species, fill=Species)) + geom_boxplot()+ theme(axis.text.x = element_text(angle = 90)) + scale_fill_brewer(palette = "Dark2") + labs(title="Usage distribution by Signal and species")

I want to see if the usage is different when the PAS is same vs different

#filter out when same is none 
MetaPASwSS_match= MetaPASwSS %>% mutate(SameSS=ifelse(ChimpPAS==HumanPAS , "Yes", "No"), bothNone=ifelse(ChimpPAS=="None" & HumanPAS=="None", "yes", "no")) %>% filter(bothNone=="no") 


MetaPASwSS_matchG= MetaPASwSS_match%>% dplyr::select(PAS, SameSS, Chimp, Human) %>% gather(Species, Usage, -SameSS, -PAS)
ggplot(MetaPASwSS_matchG,aes(x=Species, y=Usage, by=SameSS,fill=SameSS)) + geom_boxplot() + stat_compare_means(method = "t.test",label.y=0) + scale_fill_brewer(palette = "Dark2",name="Both Species \nhave Same Signal Site") +labs(title="Usage of PAS by same signal in both species")

Version Author Date
86dc150 brimittleman 2020-01-23

This means usage is higher when they have matching signal sites.

Get proportion plot.

MetaPASwSS_Sm = MetaPASwSS %>%  dplyr::select(PAS,ChimpPAS, HumanPAS) %>% gather("Species", "SS", -PAS) %>% group_by(Species,SS) %>% summarise(nSS=n()) %>% mutate(propSS=nSS/nrow(MetaPASwSS))

ggplot(MetaPASwSS_Sm, aes(x=SS,y=propSS,by=Species,fill=Species)) + geom_bar(stat="identity", position = "dodge") + theme(axis.text.x = element_text(angle = 90)) + scale_fill_brewer(palette = "Dark2")

Version Author Date
86dc150 brimittleman 2020-01-23

By location SS:

MetaPASwSS_loc=MetaPASwSS %>% mutate(ChimpWSS=ifelse(ChimpPAS =="None", "No", "Yes"),HumanWSS=ifelse(HumanPAS =="None", "No", "Yes")) %>% dplyr::select(loc, PAS, ChimpWSS, HumanWSS) %>% gather("Species", "SS", -PAS, -loc) %>% group_by(loc, Species, SS) %>% summarise(n=n()) %>% ungroup() %>% group_by(loc, Species) %>% mutate(nLoc=sum(n),PropWSS=n/nLoc) %>% filter(SS=="Yes")

ggplot(MetaPASwSS_loc,aes(x=loc, by=Species, fill=Species, y=PropWSS)) +geom_bar(stat="identity", position = "dodge")+ labs(x="",y="Proportion of PAS",title="PAS with signal site \nby species and location") + scale_fill_brewer( labels = c("Chimp","Human"), palette = "Dark2")

I will write out the metadata with signal site info for downstream analysis.

write.table(MetaPASwSS, "../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSS.txt", col.names = T, quote = F, row.names = F)

In another analysis I can ask if the differentially used PAS are those that have a SS in one and not another or different SS.

I also want to include only the signal sites that correlate with differences in average usage: These are the Top 2. AATAAA and ATTAAA

I will add a column with those with PAS if they are either of those:

MetaPASwSS_top = MetaPASwSS %>% mutate(HumanTopSS=ifelse(HumanPAS=="AATAAA" | HumanPAS== "ATTAAA", "Yes", "No"),ChimpTopSS=ifelse(ChimpPAS=="AATAAA" | ChimpPAS== "ATTAAA", "Yes", "No"))

This will be important for looking at the differentially used PAS.

ggplot(MetaPASwSS_top,aes(x=HumanTopSS, y=Human))+ geom_boxplot()

Version Author Date
2d84fb1 brimittleman 2020-01-23 
ggplot(MetaPASwSS_top,aes(x=ChimpTopSS, y=Chimp))+ geom_boxplot()

Now I can subset to those with SS in chimp only:

MetaPASwSS_top_chimponly=MetaPASwSS_top %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes")

nrow(MetaPASwSS_top_chimponly)
[1] 339
MetaPASwSS_top_chimponly_G= MetaPASwSS_top_chimponly %>% dplyr::select(PAS, Chimp,Human) %>% gather("Species", "Usage", -PAS)

ggplot(MetaPASwSS_top_chimponly_G,aes(x=Species, y=Usage))+ geom_boxplot() + stat_compare_means(method="t.test") + labs(title="Usage for PAS with a signal site in chimps only")

Version Author Date
2d84fb1 brimittleman 2020-01-23 
MetaPASwSS_top_humanonly=MetaPASwSS_top %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No")

nrow(MetaPASwSS_top_humanonly)
[1] 343
MetaPASwSS_top_humanonly_G= MetaPASwSS_top_humanonly %>% dplyr::select(PAS, Chimp,Human) %>% gather("Species", "Usage", -PAS)

ggplot(MetaPASwSS_top_humanonly_G,aes(x=Species, y=Usage))+ geom_boxplot() + stat_compare_means(method="t.test") + labs(title="Usage for PAS with a signal site in human only")

MetaPASwSS_topLocH= MetaPASwSS_top %>% dplyr::select(HumanTopSS,loc) %>% mutate(Species='Human') %>% rename("Signal"=HumanTopSS)
MetaPASwSS_topLocC= MetaPASwSS_top %>% dplyr::select(ChimpTopSS,loc) %>% mutate(Species='Chimp')%>% rename("Signal"=ChimpTopSS)

MetaPASwSS_topLocBoth= MetaPASwSS_topLocH %>% bind_rows(MetaPASwSS_topLocC) %>% group_by(Species, loc,Signal) %>% summarise(WithSS=n()) %>% ungroup() %>% group_by(Species, loc) %>% mutate(nLoc=sum(WithSS), Prop=WithSS/nLoc) %>% filter(Signal=="Yes")

ggplot(MetaPASwSS_topLocBoth, aes(x=loc, y=Prop, by=Species, fill=Species))+geom_bar(stat="identity", position = "dodge")+ labs(title="Proportion of PAS with a Signal Site", y="Proportion of PAS", x="") + scale_fill_brewer(palette = "Dark2")

Write out this extra info:

write.table(MetaPASwSS_top, "../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSSTop2.txt", col.names = T, quote = F, row.names = F)

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] workflowr_1.5.0 ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  

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        lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[49] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[52] compiler_3.5.1