Signal Site Distribution Double filter
Briana Mittleman
1/21/2020
Last updated: 2020-07-03
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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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: ggplot2Loading required package: magrittrlibrary(workflowr)This is workflowr version 1.6.0
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✖ purrr::set_names() masks magrittr::set_names()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.shThe 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")
ggplot(ChimpRawout,aes(x=X17_user_patt_count)) + geom_bar(aes(y=..prop..))+labs(title="Distribution of AATAAA pattern Chimps")
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 = "Set1") + stat_compare_means(method = "t.test")
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 = "Set1") + stat_compare_means(method = "t.test")
Look at location data and bring this in.
Loc=read.table("../data/PAS_doubleFilter/PAS_5perc_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 = "Set1") + stat_compare_means(method = "t.test",label.y.npc = "bottom")
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 = "Set1") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")
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.shremove #
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 = "Set1") + stat_compare_means(method = "t.test",label.y.npc = "bottom")
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 = "Set1") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")
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 = "Set1") + stat_compare_means(method = "t.test",label.y.npc = "bottom")
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 = "Set1") + stat_compare_means(method = "t.test",
label.y.npc = "bottom")
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")
topplot=ggplot(bothSpeciesLoc, aes(x=loc, fill=species,y=propSS)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Set1", labels=c("Chimpanzee", "Human"), name="") + labs(title="Presence of top 2 signal sites by location", y="Proportion with signal site", x="")+ theme_classic() + scale_x_discrete(labels=c("Coding", "5kb downstream", "Intronic", "3' UTR", "5' UTR"))+ theme(legend.position = "bottom", axis.text.x = element_text(size=10,angle=90),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
topplot
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
REDO THESE I previously just looked at the top 2 signal sites. Now I will write a loop to run this on the remaining 10.
#../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_AAAAAG.txt
for i in AAAAAG AATACA AATAGA AATATA ACTAAA AGTAAA CATAAA GATAAA TATAAA AAAAAA
do
echo $i
bedtools nuc -s -pattern $i -C -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed > ../data/SignalSites_doublefilter/PAS_doublefilter_either_HumanCoordHummanUsage_${i}.txt
done
for i in AAAAAG AATACA AATAGA AATATA ACTAAA AGTAAA CATAAA GATAAA TATAAA AAAAAA
do
echo $i
bedtools nuc -s -pattern $i -C -fi /project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa -bed ../data/PAS_doubleFilter/PAS_doublefilter_either_ChimpCoordChimpUsage.sort.bed > ../data/SignalSites_doublefilter/PAS_doublefilter_either_ChimpCoordChimpUsage_${i}.txt
done
sbatch allPASSeq_df.shAAAAAG 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=44432, 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 = "Set1")
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 = "Set1")
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")
ggplot(BothwithninSite, aes(x=nPerPAS_Chimp)) + geom_bar()
ggplot(BothwithninSite, aes(x=nPerPAS_Human)) + geom_bar()
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.txtSS=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 = "Set1")
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))
ggplot(HumanNone,aes(x=Chimp))+ geom_bar(stat="count")+ theme(axis.text.x = element_text(angle = 90))
Now I want to add usage:
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_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))
#chimp
ggplot(MetaPASwSS, aes(x=ChimpPAS,y=Chimp)) + geom_boxplot()+ theme(axis.text.x = element_text(angle = 90))
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 = "Set1") + labs(title="Usage distribution by Signal and species")
for supplement:
pdf("../output/supplement/Fig1_figSup8.pdf", height=4, width=6)
ggplot(MetaPASwSSBoth, aes(x=Signal, y=Usage, by=Species, fill=Species)) + geom_boxplot()+ theme_classic() + scale_fill_brewer(palette = "Set1", labels=c("Chimpanzee", "Human")) + labs(title="Usage distribution by Signal and species", y="PAS Usage")+theme(axis.text.x = element_text(size=10, angle=90),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10), legend.position = "bottom",plot.margin = unit(c(0,0,0,0), "cm"))
dev.off()png
2 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 = "Set1",name="Both Species \nhave Same Signal Site") +labs(title="Usage of PAS by same signal in both species")
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", label=c("Chimp", "Human")) + labs(y="Proportion of PAS",title="Proportion of PAS with a signal site", x="")+ theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),panel.background = element_blank())
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 = "Set1")
I will write out the metadata with signal site info for downstream analysis.
write.table(MetaPASwSS, "../data/PAS_doubleFilter/PAS_5perc_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()
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] 359MetaPASwSS_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")
MetaPASwSS_top_humanonly=MetaPASwSS_top %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No")
nrow(MetaPASwSS_top_humanonly)[1] 361MetaPASwSS_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")
| Version | Author | Date |
|---|---|---|
| e30affd | brimittleman | 2020-06-29 |
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 = "Set1") + theme_classic()
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)MetaPASwSS_SmnoNone= MetaPASwSS_Sm %>% filter(SS!="None")write.table(MetaPASwSS_SmnoNone,"../output/FigureDF/SignalSizeFigure.txt", col.names = T,row.names = F, quote = 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] cowplot_0.9.4 workflowr_1.6.0 ggpubr_0.2 magrittr_1.5
[13] 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_1.0.4.6 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