Last updated: 2020-04-17
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Knit directory: Comparative_APA/analysis/
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Rmd | afa2694 | brimittleman | 2020-04-17 | deal with ties |
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Rmd | 4cc2f72 | brimittleman | 2020-04-16 | add prop plots and 1v1 lines |
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Rmd | 2d0668e | brimittleman | 2020-04-14 | add new way to call dom |
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The way I am calling dominance is a bit naive. I am looking for the most used PAS. It may be better to look for genes with actual dominance on one PAS, meaning a PAS is used X percent above the others in the gene. I will have a set of genes with 1 PAS, 1 domiant PAS, no dominant PAS. I can then look to see if these sets are te same or different across species.
I wrote a python script. I will test it with a small file.
mkdir ../data/DomDefGreaterX
head -n 1 ../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt > ../data/DomDefGreaterX/TestFile_ZSWIM7.txt
grep ZSWIM7 ../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt >> ../data/DomDefGreaterX/TestFile_ZSWIM7.txt
python FindDomXCutoff.py ../data/DomDefGreaterX/TestFile_ZSWIM7.txt ../data/DomDefGreaterX/TestDom_ZSWIM7.txt .3 Human
python FindDomXCutoff.py ../data/DomDefGreaterX/TestFile_ZSWIM7.txt ../data/DomDefGreaterX/TestDom_ZSWIM7.txt .3 Chimp
sbatch runNewDom.sh
Looks like this code works for both species. Not the most efficient but fine.
First I need the set of genes with 1 pas. These will not be included in the analysis. I added to the code to not include PAS that have 0 usage in that species.
MetaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
MetaCol=colnames(MetaPAS)
Human1PASGene= MetaPAS %>% filter(Human>0) %>% group_by(gene) %>% summarise(nPAS=n()) %>% filter(nPAS==1)
Chimp1PASGene= MetaPAS %>% filter(Chimp>0) %>% group_by(gene) %>% summarise(nPAS=n()) %>% filter(nPAS==1)
MetaPAS_human1= MetaPAS %>% filter(gene %in% Human1PASGene$gene, Human >0) %>% mutate(Set="Human1")
MetaPAS_chimp1= MetaPAS %>% filter(gene %in% Chimp1PASGene$gene, Chimp >0) %>% mutate(Set="Chimp1")
Look at where these are.
OnePASboth=MetaPAS_human1 %>% bind_rows(MetaPAS_chimp1)
ggplot(OnePASboth, aes(x=loc, by=Set, fill=Set)) + geom_bar(position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="PAS location for genes with 1 PAS in each species")
I will run the code at different cutoffs from 10% different to 90% different. I can do this with a bash script.
I need to think about the best way to visualize this. I will start with 1 usage cutoff then move toward more. Start with the most stringent. For each one I will add the number of PAS in the gene origianlly:
PASnum= MetaPAS %>% select(PAS, gene, Chimp, Human) %>% gather(Species, usage, -PAS, -gene) %>% filter(usage >0) %>% group_by(gene,Species) %>% summarise(nPAS=n())
PASnumHuman=PASnum %>% filter(Species=="Human") %>% select(-Species)
PASnumChimp=PASnum %>% filter(Species=="Chimp") %>% select(-Species)
HumanDom9=read.table("../data/DomDefGreaterX/Human_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human9") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom9)
[1] 65
ChimpDom9=read.table("../data/DomDefGreaterX/Chimp_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp9") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom9)
[1] 173
Explore these:
BothDom9=HumanDom9 %>% bind_rows(ChimpDom9)
genloc9=ggplot(BothDom9, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.9",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc9
BothDom9_prop= BothDom9 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc9=ggplot(BothDom9_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.9",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
There are way more in chimp at this cutoff.
Look at how many genes have the same and different dominant at .9 difference:
HumanDom9_sm=HumanDom9 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom9_sm=ChimpDom9 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both9sm=ChimpDom9_sm %>% inner_join(HumanDom9_sm, by="gene")
nrow(both9sm)
[1] 0
Look at the number of PAS in these genes:
None of the same genes are represented here.
ggplot(BothDom9, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .9 difference",y="genes")
grid.newpage()
venn.plot9 <- draw.pairwise.venn(area1 = length(HumanDom9_sm$gene),
area2 = length(ChimpDom9_sm$gene),
cross.area = nrow(both9sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
Look at the location of the PAS not in gene.
HumanDom9_smLab= HumanDom9 %>% mutate(Set="Human")
ChimpDom9_smLab= ChimpDom9 %>% mutate(Set="Chimp")
BothDom9_smLab=HumanDom9_smLab %>% bind_rows(ChimpDom9_smLab)
diffgenes9=ggplot(BothDom9_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2")+ labs(title="Non Matched: 0.9", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes9
BothDom9_smLab_prop=BothDom9_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff9=ggplot(BothDom9_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.9: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom8=read.table("../data/DomDefGreaterX/Human_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human8") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom8)
[1] 318
ChimpDom8=read.table("../data/DomDefGreaterX/Chimp_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp8") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom8)
[1] 571
BothDom8=HumanDom8 %>% bind_rows(ChimpDom8)
genloc8=ggplot(BothDom8, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.8",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc8
HumanDom8_sm=HumanDom8 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom8_sm=ChimpDom8 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both8sm=ChimpDom8_sm %>% inner_join(HumanDom8_sm, by="gene")
nrow(both8sm)
[1] 192
There are 192 genes that are the same.
grid.newpage()
venn.plot8 <- draw.pairwise.venn(area1 = length(HumanDom8_sm$gene),
area2 = length(ChimpDom8_sm$gene),
cross.area = nrow(both8sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
For these I can look to see if the PAS are the same or different:
both8sm_same= both8sm %>% filter(ChimpPAS==HumanPAS)
nrow(both8sm_same)
[1] 189
both8sm_diff= both8sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both8sm_diff)
[1] 3
location for same:
same8=ggplot(both8sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .8", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same8
Only 3 are in different PAS
both8sm_diff
ChimpPAS gene ChimpLoc HumanPAS HumanLoc
1 human21024 CKS1B utr3 human21027 end
2 chimp163919 IFNGR2 utr3 human215583 intron
3 human324647 NSUN5P1 utr5 human324646 utr5
ggplot(BothDom8, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .8 difference",y="genes")
HumanDom8_smLab= HumanDom8 %>% anti_join(ChimpDom8,by = "gene") %>% mutate(Set="Human")
ChimpDom8_smLab= ChimpDom8 %>% anti_join(HumanDom8,by = "gene") %>% mutate(Set="Chimp")
BothDom8_smLab=HumanDom8_smLab %>% bind_rows(ChimpDom8_smLab)
diffgenes8=ggplot(BothDom8_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Non Matched: 0.8", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes8
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
BothDom8_prop= BothDom8 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc8=ggplot(BothDom8_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.8",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom8_smLab_prop=BothDom8_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff8=ggplot(BothDom8_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.8: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom7=read.table("../data/DomDefGreaterX/Human_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human7") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom7)
[1] 637
ChimpDom7=read.table("../data/DomDefGreaterX/Chimp_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp7") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom7)
[1] 988
BothDom7=HumanDom7 %>% bind_rows(ChimpDom7)
genloc7=ggplot(BothDom7, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.7",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc7
HumanDom7_sm=HumanDom7 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom7_sm=ChimpDom7 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both7sm=ChimpDom7_sm %>% inner_join(HumanDom7_sm, by="gene")
nrow(both7sm)
[1] 473
grid.newpage()
venn.plot7 <- draw.pairwise.venn(area1 = length(HumanDom7_sm$gene),
area2 = length(ChimpDom7_sm$gene),
cross.area = nrow(both7sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
both7sm_same= both7sm %>% filter(ChimpPAS==HumanPAS)
nrow(both7sm_same)
[1] 467
both7sm_diff= both7sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both7sm_diff)
[1] 6
location for same:
same7=ggplot(both7sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .7", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same7
both7sm_diff
ChimpPAS gene ChimpLoc HumanPAS HumanLoc
1 human21024 CKS1B utr3 human21027 end
2 chimp137870 TMC8 utr3 human151981 utr3
3 human162402 MFSD12 utr3 human162401 utr3
4 chimp182091 MAT2A utr3 human184286 cds
5 chimp163919 IFNGR2 utr3 human215583 intron
6 human324647 NSUN5P1 utr5 human324646 utr5
ggplot(BothDom7, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .7 difference",y="genes")
HumanDom7_smLab= HumanDom7 %>% anti_join(ChimpDom7,by = "gene") %>% mutate(Set="Human")
ChimpDom7_smLab= ChimpDom7 %>% anti_join(HumanDom7,by = "gene") %>% mutate(Set="Chimp")
BothDom7_smLab=HumanDom7_smLab %>% bind_rows(ChimpDom7_smLab)
diffgenes7=ggplot(BothDom7_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of dominant PAS in genes \nnot matched between species: 0.7", y="genes")+ labs(title="Non Matched: 0.7", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes7
BothDom7_prop= BothDom7 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc7=ggplot(BothDom7_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.7",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom7_smLab_prop=BothDom7_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff7=ggplot(BothDom7_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.7: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom6=read.table("../data/DomDefGreaterX/Human_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human6") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom6)
[1] 1014
ChimpDom6=read.table("../data/DomDefGreaterX/Chimp_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp6") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom6)
[1] 1404
BothDom6=HumanDom6 %>% bind_rows(ChimpDom6)
genloc6=ggplot(BothDom6, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.6",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc6
HumanDom6_sm=HumanDom6 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom6_sm=ChimpDom6 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both6sm=ChimpDom6_sm %>% inner_join(HumanDom6_sm, by="gene")
nrow(both6sm)
[1] 803
grid.newpage()
venn.plot6 <- draw.pairwise.venn(area1 = length(HumanDom6_sm$gene),
area2 = length(ChimpDom6_sm$gene),
cross.area = nrow(both6sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
both6sm_same= both6sm %>% filter(ChimpPAS==HumanPAS)
nrow(both6sm_same)
[1] 796
both6sm_diff= both6sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both6sm_diff)
[1] 7
location for same:
same6=ggplot(both6sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .6", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same6
both6sm_diff
ChimpPAS gene ChimpLoc HumanPAS HumanLoc
1 human21024 CKS1B utr3 human21027 end
2 human60067 FTH1 utr3 human60068 cds
3 chimp137870 TMC8 utr3 human151981 utr3
4 human162402 MFSD12 utr3 human162401 utr3
5 chimp182091 MAT2A utr3 human184286 cds
6 chimp163919 IFNGR2 utr3 human215583 intron
7 human324647 NSUN5P1 utr5 human324646 utr5
ggplot(BothDom6, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .6 difference",y="genes")
HumanDom6_smLab= HumanDom6 %>% anti_join(ChimpDom6,by = "gene") %>% mutate(Set="Human")
ChimpDom6_smLab= ChimpDom6 %>% anti_join(HumanDom6,by = "gene") %>% mutate(Set="Chimp")
BothDom6_smLab=HumanDom6_smLab %>% bind_rows(ChimpDom6_smLab)
diffgenes6=ggplot(BothDom6_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Non Matched: 0.6", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes6
BothDom6_prop= BothDom6 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc6=ggplot(BothDom6_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.6",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom6_smLab_prop=BothDom6_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff6=ggplot(BothDom6_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.6: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom5=read.table("../data/DomDefGreaterX/Human_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human5") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom5)
[1] 1404
ChimpDom5=read.table("../data/DomDefGreaterX/Chimp_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp5") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom5)
[1] 1908
BothDom5=HumanDom5 %>% bind_rows(ChimpDom5)
genloc5=ggplot(BothDom5, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.5",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc5
HumanDom5_sm=HumanDom5 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom5_sm=ChimpDom5 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both5sm=ChimpDom5_sm %>% inner_join(HumanDom5_sm, by="gene")
nrow(both5sm)
[1] 1160
grid.newpage()
venn.plot5 <- draw.pairwise.venn(area1 = length(HumanDom5_sm$gene),
area2 = length(ChimpDom5_sm$gene),
cross.area = nrow(both5sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
both5sm_same= both5sm %>% filter(ChimpPAS==HumanPAS)
nrow(both5sm_same)
[1] 1145
both5sm_diff= both5sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both5sm_diff)
[1] 15
location for same:
same5=ggplot(both5sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .5", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same5
both5sm_diff
ChimpPAS gene ChimpLoc HumanPAS HumanLoc
1 human21024 CKS1B utr3 human21027 end
2 human48359 PGAM1 utr3 human48357 intron
3 human60067 FTH1 utr3 human60068 cds
4 human68171 DDX6 utr3 human68160 utr3
5 chimp137870 TMC8 utr3 human151981 utr3
6 human162402 MFSD12 utr3 human162401 utr3
7 chimp182091 MAT2A utr3 human184286 cds
8 human205442 RBCK1 utr3 human205434 utr3
9 chimp163919 IFNGR2 utr3 human215583 intron
10 chimp165747 LSS utr3 human217562 intron
11 human225614 RPL32 utr3 human225615 utr3
12 human250766 NCBP2 utr3 chimp224588 cds
13 human324647 NSUN5P1 utr5 human324646 utr5
14 chimp308202 FABP5 utr3 human345717 cds
15 human366498 SLC27A4 intron human366500 utr3
ggplot(BothDom5, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .5 difference",y="genes")
HumanDom5_smLab= HumanDom5 %>% anti_join(ChimpDom5,by = "gene") %>% mutate(Set="Human")
ChimpDom5_smLab= ChimpDom5 %>% anti_join(HumanDom5,by = "gene") %>% mutate(Set="Chimp")
BothDom5_smLab=HumanDom5_smLab %>% bind_rows(ChimpDom5_smLab)
diffgenes5=ggplot(BothDom5_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Non Matched: 0.5", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes5
BothDom5_prop= BothDom5 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc5=ggplot(BothDom5_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.5",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom5_smLab_prop=BothDom5_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff5=ggplot(BothDom5_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.5: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom4=read.table("../data/DomDefGreaterX/Human_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human4") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom4)
[1] 1833
ChimpDom4=read.table("../data/DomDefGreaterX/Chimp_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp4") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom4)
[1] 2478
BothDom4=HumanDom4 %>% bind_rows(ChimpDom4)
genloc4=ggplot(BothDom4, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.4",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc4
HumanDom4_sm=HumanDom4 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom4_sm=ChimpDom4 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both4sm=ChimpDom4_sm %>% inner_join(HumanDom4_sm, by="gene")
nrow(both4sm)
[1] 1565
grid.newpage()
venn.plot4 <- draw.pairwise.venn(area1 = length(HumanDom4_sm$gene),
area2 = length(ChimpDom4_sm$gene),
cross.area = nrow(both4sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
both4sm_same= both4sm %>% filter(ChimpPAS==HumanPAS)
nrow(both4sm_same)
[1] 1543
both4sm_diff= both4sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both4sm_diff)
[1] 22
location for same:
same4=ggplot(both4sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .4", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same4
22 is enough to start to plot:
both4sm_diff_g= both4sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
diffDom4=ggplot(both4sm_diff_g, aes(x=Loc, fill=Species))+ geom_bar(stat="count",position = "dodge") +scale_fill_brewer(palette = "Dark2")+ labs(y='Genes', title="Different Dominant PAS: 0.4",x="") +theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffDom4
ggplot(BothDom4, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .4 difference",y="genes")
HumanDom4_smLab= HumanDom4 %>% anti_join(ChimpDom4,by = "gene") %>% mutate(Set="Human")
ChimpDom4_smLab= ChimpDom4 %>% anti_join(HumanDom4,by = "gene") %>% mutate(Set="Chimp")
BothDom4_smLab=HumanDom4_smLab %>% bind_rows(ChimpDom4_smLab)
diffgenes4=ggplot(BothDom4_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Non Matched: 0.4", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes4
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
BothDom4_prop= BothDom4 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc4=ggplot(BothDom4_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.4",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom4_smLab_prop=BothDom4_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff4=ggplot(BothDom4_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.4: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom3=read.table("../data/DomDefGreaterX/Human_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human3") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom3)
[1] 2403
ChimpDom3=read.table("../data/DomDefGreaterX/Chimp_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp3") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom3)
[1] 3127
BothDom3=HumanDom3 %>% bind_rows(ChimpDom3)
genloc3=ggplot(BothDom3, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.3",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc3
HumanDom3_sm=HumanDom3 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom3_sm=ChimpDom3 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both3sm=ChimpDom3_sm %>% inner_join(HumanDom3_sm, by="gene")
nrow(both3sm)
[1] 2069
grid.newpage()
venn.plot3 <- draw.pairwise.venn(area1 = length(HumanDom3_sm$gene),
area2 = length(ChimpDom3_sm$gene),
cross.area = nrow(both3sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
both3sm_same= both3sm %>% filter(ChimpPAS==HumanPAS)
nrow(both3sm_same)
[1] 2031
both3sm_diff= both3sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both3sm_diff)
[1] 38
location for same:
same3=ggplot(both3sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .3", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same3
both3sm_diff_g= both3sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
diffDom3=ggplot(both3sm_diff_g, aes(x=Loc, fill=Species))+ geom_bar(stat="count",position = "dodge") +scale_fill_brewer(palette = "Dark2")+ labs(y='Genes', title="Different Dominant PAS: 0.3",x="") +theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffDom3
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
ggplot(BothDom3, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .3 difference",y="genes")
HumanDom3_smLab= HumanDom3 %>% anti_join(ChimpDom3,by = "gene") %>% mutate(Set="Human")
ChimpDom3_smLab= ChimpDom3 %>% anti_join(HumanDom3,by = "gene") %>% mutate(Set="Chimp")
BothDom3_smLab=HumanDom3_smLab %>% bind_rows(ChimpDom3_smLab)
diffgenes3=ggplot(BothDom3_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Non Matched: 0.3", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes3
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
BothDom3_prop= BothDom3 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc3=ggplot(BothDom3_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.3",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom3_prop= BothDom3 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc3=ggplot(BothDom3_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.3",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom3_smLab_prop=BothDom3_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff3=ggplot(BothDom3_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.3: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom2=read.table("../data/DomDefGreaterX/Human_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human2") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom2)
[1] 3183
ChimpDom2=read.table("../data/DomDefGreaterX/Chimp_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp2") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom2)
[1] 3969
BothDom2=HumanDom2 %>% bind_rows(ChimpDom2)
genloc2=ggplot(BothDom2, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.2",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc2
HumanDom2_sm=HumanDom2 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom2_sm=ChimpDom2 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both2sm=ChimpDom2_sm %>% inner_join(HumanDom2_sm, by="gene")
nrow(both2sm)
[1] 2711
grid.newpage()
venn.plot2 <- draw.pairwise.venn(area1 = length(HumanDom2_sm$gene),
area2 = length(ChimpDom2_sm$gene),
cross.area = nrow(both2sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
both2sm_same= both2sm %>% filter(ChimpPAS==HumanPAS)
nrow(both2sm_same)
[1] 2634
both2sm_diff= both2sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both2sm_diff)
[1] 77
location for same:
same2=ggplot(both2sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .2", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same2
both2sm_diff_g= both2sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
diffDom2=ggplot(both2sm_diff_g, aes(x=Loc, fill=Species))+ geom_bar(stat="count",position = "dodge") +scale_fill_brewer(palette = "Dark2")+ labs(y='Genes', title="Different Dominant PAS: 0.2",x="") +theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffDom2
ggplot(BothDom2, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .2 difference",y="genes")
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
HumanDom2_smLab= HumanDom2 %>% anti_join(ChimpDom2,by = "gene") %>% mutate(Set="Human")
ChimpDom2_smLab= ChimpDom2 %>% anti_join(HumanDom2,by = "gene") %>% mutate(Set="Chimp")
BothDom2_smLab=HumanDom2_smLab %>% bind_rows(ChimpDom2_smLab)
diffgenes2=ggplot(BothDom2_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Non Matched: 0.2", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes2
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
BothDom2_prop= BothDom2 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc2=ggplot(BothDom2_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.2",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom2_smLab_prop=BothDom2_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff2=ggplot(BothDom2_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.2: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
HumanDom1=read.table("../data/DomDefGreaterX/Human_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human1") %>% inner_join(PASnumHuman, by="gene")
nrow(HumanDom1)
[1] 4417
ChimpDom1=read.table("../data/DomDefGreaterX/Chimp_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp1") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom1)
[1] 5254
BothDom1=HumanDom1 %>% bind_rows(ChimpDom1)
genloc1=ggplot(BothDom1, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.1",y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
genloc1
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
HumanDom1_sm=HumanDom1 %>% select(PAS, gene, loc) %>% rename(HumanPAS=PAS, HumanLoc=loc)
ChimpDom1_sm=ChimpDom1 %>% select(PAS, gene, loc)%>% rename(ChimpPAS=PAS, ChimpLoc=loc)
both1sm=ChimpDom1_sm %>% inner_join(HumanDom1_sm, by="gene")
nrow(both1sm)
[1] 3814
grid.newpage()
venn.plot1 <- draw.pairwise.venn(area1 = length(HumanDom1_sm$gene),
area2 = length(ChimpDom1_sm$gene),
cross.area = nrow(both1sm),
c("Human", "Chimp"), scaled = TRUE,
fill = c("orange", "green"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
both1sm_same= both1sm %>% filter(ChimpPAS==HumanPAS)
nrow(both1sm_same)
[1] 3542
both1sm_diff= both1sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both1sm_diff)
[1] 272
location for same:
same1=ggplot(both1sm_same,aes(x=ChimpLoc,fill=ChimpLoc)) +geom_bar(stat = "count") +scale_fill_brewer(palette = "Dark2")+ labs(title="Matched Gene Matched PAS .1", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
same1
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
both1sm_diff_g= both1sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
diffDom1=ggplot(both1sm_diff_g, aes(x=Loc, fill=Species))+ geom_bar(stat="count",position = "dodge") +scale_fill_brewer(palette = "Dark2")+ labs(y='PAS', title="Location for Different Dominant PAS at 10% difference") +labs(y='Genes', title="Different Dominant PAS: 0.1",x="") +theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffDom1
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
ggplot(BothDom1, aes(x=nPAS, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of PAS per gene with .1 difference",y="genes")
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
HumanDom1_smLab= HumanDom1 %>% anti_join(ChimpDom1,by = "gene") %>% mutate(Set="Human")
ChimpDom1_smLab= ChimpDom1 %>% anti_join(HumanDom1,by = "gene") %>% mutate(Set="Chimp")
BothDom1_smLab=HumanDom1_smLab %>% bind_rows(ChimpDom1_smLab)
diffgenes1=ggplot(BothDom1_smLab,aes(x=loc, fill=Set))+ geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Non Matched: 0.1", y="genes",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
diffgenes1
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
BothDom1_prop= BothDom1 %>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
proploc1=ggplot(BothDom1_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.1",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
BothDom1_smLab_prop=BothDom1_smLab%>% group_by(set, loc) %>% summarise(nLoc=n()) %>% ungroup() %>% group_by(set) %>% mutate(nSet=sum(nLoc), prop=nLoc/nSet)
propDiff1=ggplot(BothDom1_smLab_prop, aes(x=loc,by=set, fill=set,y=prop)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="0.1: Different Genes",y="Proportion",x="")+theme(axis.text.x = element_text(angle = 90),legend.position = "none")
Next step will be to aggregate these plots in a way that is both easier to visualize but also easier to draw conclusions from.
First plot the number of genes with a dominant PAS at each cutoff.
Cutoffs=seq(0.1,.9, .1)
Human=c(nrow(HumanDom1),nrow(HumanDom2),nrow(HumanDom3),nrow(HumanDom4),nrow(HumanDom5),nrow(HumanDom6),nrow(HumanDom7),nrow(HumanDom8),nrow(HumanDom9))
Chimp=c(nrow(ChimpDom1),nrow(ChimpDom2),nrow(ChimpDom3),nrow(ChimpDom4),nrow(ChimpDom5),nrow(ChimpDom6),nrow(ChimpDom7),nrow(ChimpDom8),nrow(ChimpDom9))
DomNumsDF=as.data.frame(cbind(Cutoffs, Human, Chimp)) %>% gather("Species", "DominantGenes", -Cutoffs)
DomNumsDF$Cutoffs= as.factor(DomNumsDF$Cutoffs)
ggplot(DomNumsDF, aes(x=Cutoffs, fill=Species, y=DominantGenes)) + geom_bar(stat="identity", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Number of Genes with a Dominant PAS",x="Difference in usage to be called dominant")
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
General location:
genloctiles <- ggdraw() +
draw_label(
"Location of Dominant PAS",
fontface = 'bold',
x = 0,
hjust = 0
)
genloc=plot_grid(genloc1,genloc2,genloc3,genloc4,genloc5,genloc6,genloc7,genloc8,genloc9)
plot_grid(
genloctiles, genloc,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1)
)
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
This shows this pattern is robust to the cutoff.
Proportion:
proploctiles <- ggdraw() +
draw_label(
"Location of Dominant PAS by proportion",
fontface = 'bold',
x = 0,
hjust = 0
)
proploc=plot_grid(proploc9,proploc8,proploc7,proploc6,proploc5,proploc4,proploc3,proploc2,proploc1)
plot_grid(
proploctiles, proploc,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1)
)
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
Plot the joint genes.
diffdomtiles <- ggdraw() +
draw_label(
"Location of Dominant PAS in genes with different Dominant PAS",
fontface = 'bold',
x = 0,
hjust = 0
)
diffdom=plot_grid(diffDom1,diffDom2,diffDom3,diffDom4)
plot_grid(
diffdomtiles, diffdom,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1)
)
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
This is also robust to cutoff.
Location when they do not share the same gene.
diffgenes9
diffgenestiles <- ggdraw() +
draw_label(
"Location of Dominant PAS in genes with a dominant PAS only in 1 species",
fontface = 'bold',
x = 0,
hjust = 0
)
diffgenes=plot_grid(diffgenes1,diffgenes2,diffgenes3,diffgenes4,diffgenes5,diffgenes6,diffgenes7,diffgenes8,diffgenes9)
plot_grid(
diffgenestiles, diffgenes,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1)
)
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
#propDiff1
diffgenesProptiles <- ggdraw() +
draw_label(
"Location of Dominant PAS in genes with a dominant PAS only in 1 species- Proportion",
fontface = 'bold',
x = 0,
hjust = 0
)
diffPropgenes=plot_grid(propDiff1,propDiff2,propDiff3,propDiff4,propDiff5,propDiff6,propDiff7,propDiff8,propDiff9)
plot_grid(
diffgenesProptiles, diffPropgenes,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1)
)
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
This is robust as well.
Look at the overlapping genes with the same PAS
#same1
SAMESAMEtiles <- ggdraw() +
draw_label(
"Location of Dominant PAS in genes with the same dominant PAS",
fontface = 'bold',
x = 0,
hjust = 0
)
SAMESAME=plot_grid(same1,same2,same3,same4,same5,same6,same7,same8)
plot_grid(
SAMESAMEtiles, SAMESAME,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1)
)
Version | Author | Date |
---|---|---|
385fe04 | brimittleman | 2020-04-16 |
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_0.9.4 forcats_0.3.0 stringr_1.3.1
[4] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[7] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[10] tidyverse_1.2.1 VennDiagram_1.6.20 futile.logger_1.4.3
[13] gridExtra_2.3 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 withr_2.1.2 glue_1.3.0
[13] RColorBrewer_1.1-2 lambda.r_1.2.3 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 cellranger_1.1.0
[19] munsell_0.5.0 gtable_0.2.0 rvest_0.3.2
[22] evaluate_0.12 labeling_0.3 knitr_1.20
[25] httpuv_1.4.5 broom_0.5.1 Rcpp_1.0.2
[28] promises_1.0.1 backports_1.1.2 scales_1.0.0
[31] formatR_1.5 jsonlite_1.6 fs_1.3.1
[34] hms_0.4.2 digest_0.6.18 stringi_1.2.4
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1
[40] magrittr_1.5 lazyeval_0.2.1 futile.options_1.0.1
[43] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[46] xml2_1.2.0 lubridate_1.7.4 rstudioapi_0.10
[49] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[52] R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[55] compiler_3.5.1