Last updated: 2020-04-17

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd afa2694 brimittleman 2020-04-17 deal with ties
html 385fe04 brimittleman 2020-04-16 Build site.
Rmd 4cc2f72 brimittleman 2020-04-16 add prop plots and 1v1 lines
html 71e0ab3 brimittleman 2020-04-15 Build site.
Rmd 8b2f73d brimittleman 2020-04-15 add aggregate res
html 38c6dc9 brimittleman 2020-04-14 Build site.
Rmd 2d0668e brimittleman 2020-04-14 add new way to call dom

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library(cowplot)

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    ggsave

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")

Version Author Date
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14

Seperate results

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:

90% difference

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

Version Author Date
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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")

Version Author Date
385fe04 brimittleman 2020-04-16
38c6dc9 brimittleman 2020-04-14
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

80% difference

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15

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")

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

70% difference

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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")

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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")

60% difference

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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")

50% difference

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14
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")

40% difference

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
38c6dc9 brimittleman 2020-04-14

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

30% difference

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

20% difference

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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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) 

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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

Version Author Date
385fe04 brimittleman 2020-04-16
71e0ab3 brimittleman 2020-04-15
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")

10% difference

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")

Aggregate results:

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