Last updated: 2020-05-14
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
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Rmd | 3c19233 | brimittleman | 2020-05-14 | add which PAS and seq between |
Previously I mapped PAS to ortho exons. I want genes where the only PAS are those in the ortho UTRs:
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
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library(cowplot)
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library(ggpubr)
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OverlapOrtho=read.table("../data/orthoUTR/FilteredPASOverlapOrthoUTR.text", header = T,stringsAsFactors = F)
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T,stringsAsFactors = F)
OverlapOrtho_nper= OverlapOrtho %>% group_by(gene) %>% summarise(nPASOrtho=n())
PASperGene= PASMeta %>% group_by(gene) %>% summarise(nPAS=n())
First inner join genes. Then those with the same nunmber of PAS:
PASperGene_inortho=PASperGene %>% inner_join(OverlapOrtho_nper,by="gene") %>% filter(nPAS==nPASOrtho)
Now i will grab these genes from the meta data:
use if else to get the first, middle, last..
Meta_allOrtho=PASMeta %>% filter(gene %in% PASperGene_inortho$gene) %>% group_by(gene) %>% arrange(start) %>% mutate(NumPAS=ifelse(strandFix=="+", 1:n(), n():1),number=n()) %>% mutate(WhichSite=ifelse(number==NumPAS, ifelse(NumPAS==1, "single", "last"), ifelse(NumPAS==1, "first", "middle")))
max(Meta_allOrtho$NumPAS)
[1] 8
Meta_allOrtho %>% ungroup() %>% group_by(WhichSite) %>% summarise(n())
# A tibble: 4 x 2
WhichSite `n()`
<chr> <int>
1 first 480
2 last 480
3 middle 294
4 single 836
this means 836 have 1, 480 have more than one
Start to ask about which ones are dominant?, dAPA ect. filter diff used to ony those in the ortho exon only gene set.
PASGene=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% select(PAS, chr, start, end,loc)
DiffUsed=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T,stringsAsFactors = F) %>% inner_join(PASGene, by=c("chr",'start','end')) %>% filter(gene %in% PASperGene_inortho$gene) %>% select(PAS, SigPAU2)
useCOl <- c("#d73027", "#4575b4","#fee090")
Meta_allOrtho_SIG= Meta_allOrtho %>% inner_join(DiffUsed, by="PAS")
Meta_allOrtho_SIG$WhichSite=factor(Meta_allOrtho_SIG$WhichSite, levels=c("last", "middle", "first"))
ggplot(Meta_allOrtho_SIG, aes(x=SigPAU2, by=WhichSite, fill=WhichSite)) + geom_bar(stat="count", position = "fill") + labs(x="dAPA PAS",y="Proportion", title="Orthologous UTR PAS by dAPA") + scale_fill_manual(values=useCOl)
anavo test:
PropdAPA=Meta_allOrtho_SIG %>% group_by(SigPAU2, WhichSite) %>% summarise(n=n()) %>% ungroup() %>% group_by(SigPAU2) %>% mutate(nDapa=sum(n), prop=n/nDapa,dAPA=ifelse(SigPAU2=="Yes",1,0))
PropdAPA_no=as.vector(PropdAPA[1:3,5])
PropdAPA_yes=unlist(as.vector(PropdAPA[4:6,5]))
wilcox.test(PropdAPA$prop, PropdAPA$dAPA)
Warning in wilcox.test.default(PropdAPA$prop, PropdAPA$dAPA): cannot
compute exact p-value with ties
Wilcoxon rank sum test with continuity correction
data: PropdAPA$prop and PropdAPA$dAPA
W = 18, p-value = 1
alternative hypothesis: true location shift is not equal to 0
prop.test(x=c(47,442), n=c(84,1168))
2-sample test for equality of proportions with continuity
correction
data: c(47, 442) out of c(84, 1168)
X-squared = 10.05, df = 1, p-value = 0.001523
alternative hypothesis: two.sided
95 percent confidence interval:
0.06497125 0.29722705
sample estimates:
prop 1 prop 2
0.5595238 0.3784247
Test dominant:
HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)
ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)
BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")
BothRes_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10)
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)
BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)
Filter the genes in the utr only set:
BothRes_40_filt= BothRes_40 %>% filter(gene %in% PASperGene_inortho$gene )
nrow(BothRes_40_filt)
[1] 238
BothRes_40_filt %>% group_by(Set) %>% summarise(n())
# A tibble: 2 x 2
Set `n()`
<chr> <int>
1 Different 10
2 Same 228
Same:
Meta_allOrthoSm= Meta_allOrtho %>% ungroup() %>% select(PAS, WhichSite)
BothRes_40_filt_same= BothRes_40_filt %>% filter(Set=="Same") %>% dplyr::rename("PAS" = Human_PAS) %>% inner_join(Meta_allOrthoSm, by="PAS")
BothRes_40_filt_same$WhichSite= factor(BothRes_40_filt_same$WhichSite, levels=c("last", "middle","first"))
ggplot(BothRes_40_filt_same, aes(x=WhichSite, fill=WhichSite)) + geom_bar(stat="count" ) + scale_fill_manual(values=useCOl)
Diff dominant:
BothRes_40_filt_diff= BothRes_40_filt%>% filter(Set=="Different") %>% select(gene, Human_PAS, Chimp_PAS) %>% gather("Species","PAS", -gene) %>% inner_join(Meta_allOrthoSm, by="PAS")
ggplot(BothRes_40_filt_diff, aes(x=Species,by=Species, fill=WhichSite)) + geom_bar(stat="count",position = "dodge" ) + scale_fill_manual(values=useCOl)
This may be due to the low numbers: try different cutoffs:
number=seq(1,9,1)
#BothResAll
plotlist=list()
for (i in number){
val=i *10
df=BothResAll %>% filter(Set=="Same", cut==val) %>% dplyr::rename("PAS" = Human_PAS) %>% inner_join(Meta_allOrthoSm, by="PAS")
df$WhichSite= factor(df$WhichSite, levels=c("first", "middle","last"))
plotlist[[i]]=ggplot(df, aes(x=WhichSite, fill=WhichSite)) + geom_bar(stat="count" ) + scale_fill_manual(values=useCOl) + labs(title=paste(val, "Same Dominant" , nrow(df), sep="_")) +theme(legend.position = "bottom")
}
plot_grid(plotlist[[1]], plotlist[[2]], plotlist[[3]], plotlist[[4]], plotlist[[5]], plotlist[[6]], plotlist[[7]], plotlist[[8]], plotlist[[9]])
#BothResAll
plotlistdiff=list()
for (i in number){
val=i *10
df=BothResAll %>% filter(Set=="Different", cut==val) %>% select(gene, Human_PAS, Chimp_PAS) %>% gather("Species","PAS", -gene) %>% inner_join(Meta_allOrthoSm, by="PAS")
df$WhichSite= factor(df$WhichSite, levels=c("first", "middle","last"))
plotlistdiff[[i]]=ggplot(df, aes(x=Species,by=Species, fill=WhichSite)) + geom_bar(stat="count",position = "dodge" ) + scale_fill_manual(values=useCOl) + labs(title=paste(val, "Different Dominant" , nrow(df), sep="_")) +theme(legend.position = "bottom")
}
plot_grid(plotlistdiff[[1]], plotlistdiff[[2]], plotlistdiff[[3]], plotlistdiff[[4]], plotlistdiff[[5]], plotlistdiff[[6]], plotlistdiff[[7]], plotlistdiff[[8]], plotlistdiff[[9]])
Seperate by DE:
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% dplyr::rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)
DE_yes= DE %>% filter(DE=="Yes")
numbersmall=seq(1,4)
#BothResAll
plotlistdiffDE=list()
for (i in numbersmall){
val=i *10
df=BothResAll %>% filter(Set=="Different", cut==val) %>% select(gene, Human_PAS, Chimp_PAS) %>% gather("Species","PAS", -gene) %>% inner_join(Meta_allOrthoSm, by="PAS") %>% mutate(DEgenes=ifelse(gene %in%DE_yes$gene, "DE", "Not"))
df$WhichSite= factor(df$WhichSite, levels=c("first", "middle","last"))
plotlistdiffDE[[i]]=ggplot(df, aes(x=Species,by=Species, fill=WhichSite)) + geom_bar(stat="count",position = "dodge" ) + scale_fill_manual(values=useCOl) + labs(title=paste(val, "Different Dominant" , nrow(df), sep="_")) +theme(legend.position = "bottom") + facet_grid(~DEgenes)
}
plot_grid(plotlistdiffDE[[1]], plotlistdiffDE[[2]], plotlistdiffDE[[3]], plotlistdiffDE[[4]])
metaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% mutate(midpoint=start+100)
metaPAS_sm= metaPAS %>% select(PAS, gene, midpoint)
BothResallDiff= BothResAll %>%
filter(Set=="Different") %>%
select(gene, Chimp_PAS, Human_PAS, cut) %>%
gather("species", "PAS", -gene, -cut) %>%
inner_join(metaPAS_sm, by=c("gene", "PAS")) %>%
mutate(extra="PAS") %>%
spread(extra,midpoint) %>%
group_by(cut, gene) %>%
summarise(minPAS=min(PAS), maxPAS=max(PAS)) %>%
mutate(length= maxPAS-minPAS) %>%
filter(gene %in% PASperGene_inortho$gene)
BothResallDiff$cut=factor(BothResallDiff$cut)
ggplot(BothResallDiff, aes(x=cut, y=length,fill=cut)) +geom_boxplot() + theme(legend.position = "none")+ scale_fill_brewer(palette = "Set1") + geom_jitter() + labs(title="Distance between center of different Dominant PAS",y="basepairs",x="cutoff")
BothResallDiff %>% group_by(cut) %>% summarise(n())
# A tibble: 8 x 2
cut `n()`
<fct> <int>
1 10 37
2 20 24
3 30 17
4 40 10
5 50 8
6 60 5
7 70 2
8 80 2
Run nuc for these:
metaPAS_bed= metaPAS %>% select(chr, gene, strandFix) %>% unique()
BothResallDiff_Bed= BothResallDiff%>% inner_join(metaPAS_bed, by="gene") %>% select(chr, minPAS, maxPAS, gene, cut, strandFix ) %>% arrange(chr, minPAS)
write.table(BothResallDiff_Bed, "../data/DistTwoDom/SeqBetweenDom_Allcutt.bed", quote = F, col.names = F, row.names = F, sep="\t")
bedtools nuc -s -seq -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/DistTwoDom/SeqBetweenDom_Allcutt.bed > ../data/DistTwoDom/SeqBetweenDom_Allcutt_nuc.bed
SeqBetween=read.table("../data/DistTwoDom/SeqBetweenDom_Allcutt_nuc.bed", col.names = c(colnames(BothResallDiff_Bed),"AT", "GC", "A", "C", "G", "T","N", "other", "len", "seq" )) %>% mutate(DEgenes=ifelse(gene %in%DE_yes$gene, "DE", "Not"))
SeqBetween %>% group_by(cut) %>% summarise(n())
# A tibble: 8 x 2
cut `n()`
<int> <int>
1 10 37
2 20 24
3 30 17
4 40 10
5 50 8
6 60 5
7 70 2
8 80 2
SeqBetween$cut=as.factor(SeqBetween$cut)
ggplot(SeqBetween, aes(y=len, by=DEgenes,fill=DEgenes, x=cut))+geom_boxplot() + geom_jitter()+scale_fill_brewer(palette = "Set1")+ labs(y="Nucleotides", title="Distance between Dominant PAS")+ theme(legend.position = "bottom")
atplot=ggplot(SeqBetween, aes(by=DEgenes, y=AT, x=cut,fill=DEgenes)) +geom_boxplot()+geom_jitter() +scale_fill_brewer(palette = "Set1")+ labs(y="AT proportion", title="AT proportion for Seq between dominat")+ theme(legend.position = "bottom")
gcplot=ggplot(SeqBetween, aes(by=DEgenes, y=GC, x=cut,fill=DEgenes)) +geom_boxplot()+geom_jitter() +scale_fill_brewer(palette = "Set1")+ theme(legend.position = "bottom") +labs(y="GC proportion", title="GC proportion for Seq between dominat")
plot_grid(atplot,gcplot)
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.2 magrittr_1.5 cowplot_0.9.4 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[9] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1
[13] workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 reshape2_1.4.3 haven_1.1.2
[4] lattice_0.20-38 colorspace_1.3-2 generics_0.0.2
[7] htmltools_0.3.6 yaml_2.2.0 utf8_1.1.4
[10] rlang_0.4.0 later_0.7.5 pillar_1.3.1
[13] glue_1.3.0 withr_2.1.2 RColorBrewer_1.1-2
[16] modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[19] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0
[22] rvest_0.3.2 evaluate_0.12 labeling_0.3
[25] knitr_1.20 httpuv_1.4.5 fansi_0.4.0
[28] broom_0.5.1 Rcpp_1.0.4.6 promises_1.0.1
[31] scales_1.0.0 backports_1.1.2 jsonlite_1.6
[34] fs_1.3.1 hms_0.4.2 digest_0.6.18
[37] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[40] cli_1.1.0 tools_3.5.1 lazyeval_0.2.1
[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