New Dominance Definition

Last updated: 2020-04-14

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
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    Modified:   analysis/correlationPhenos.Rmd
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    Modified:   analysis/investigatePantro5.Rmd
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    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File	Version	Author	Date	Message
Rmd	2d0668e	brimittleman	2020-04-14	add new way to call dom

library(workflowr)

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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

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")

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)
ggplot(BothDom9, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .9 difference",y="genes")

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.plot <- 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)

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)
ggplot(BothDom8, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .8 difference",y="genes")

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.plot <- 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

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")

###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)
ggplot(BothDom7, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .7 difference",y="genes")

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.plot <- 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

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")

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)
ggplot(BothDom6, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .6 difference",y="genes")

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.plot <- 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

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")

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)
ggplot(BothDom5, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .5 difference",y="genes")

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.plot <- 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

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")

###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)
ggplot(BothDom4, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .4 difference",y="genes")

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.plot <- 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

22 is enough to start to plot:

both4sm_diff_g= both4sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
ggplot(both4sm_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 40% difference")

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")

###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)
ggplot(BothDom3, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .3 difference",y="genes")

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.plot <- 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

both3sm_diff_g= both3sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
ggplot(both3sm_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 30% difference")

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")

###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)
ggplot(BothDom2, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .2 difference",y="genes")

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.plot <- 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

both2sm_diff_g= both2sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
ggplot(both2sm_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 20% difference")

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")

10% difference

HumanDom1=read.table("../data/DomDefGreaterX/Human_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human2") %>% 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="Chimp2") %>% inner_join(PASnumChimp, by="gene")
nrow(ChimpDom1)

[1] 5254

BothDom1=HumanDom1 %>% bind_rows(ChimpDom1)
ggplot(BothDom1, aes(x=loc, fill=set)) + geom_bar(stat="count", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of PAS dominant with .1 difference",y="genes")

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.plot <- 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)

both1sm_same= both1sm %>% filter(ChimpPAS==HumanPAS)
nrow(both1sm_same)

[1] 3542

both1sm_diff= both1sm %>% filter(ChimpPAS!=HumanPAS)
nrow(both1sm_diff)

[1] 272

both1sm_diff_g= both1sm_diff %>% select(gene, HumanLoc, ChimpLoc) %>% gather('Species', 'Loc', -gene)
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")

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")

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.

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] forcats_0.3.0       stringr_1.3.1       dplyr_0.8.0.1      
 [4] purrr_0.3.2         readr_1.3.1         tidyr_0.8.3        
 [7] tibble_2.1.1        ggplot2_3.1.1       tidyverse_1.2.1    
[10] VennDiagram_1.6.20  futile.logger_1.4.3 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           munsell_0.5.0       
[19] gtable_0.2.0         cellranger_1.1.0     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      assertthat_0.2.0    
[49] rmarkdown_1.10       httr_1.3.1           rstudioapi_0.10     
[52] R6_2.3.0             nlme_3.1-137         git2r_0.26.1        
[55] compiler_3.5.1