Conservation Questions
Briana Mittleman
2/28/2020
Last updated: 2020-05-04
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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In my initial exploration of dAPA PAS I saw they are enriched for negative phylop scores. I will explore this trend further here. I will see if intron location explain the differences.
library(tidyverse)── Attaching packages ──────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.1.1 ✔ purrr 0.3.2
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✖ dplyr::filter() masks stats::filter()
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Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extractlibrary(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithsDiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene, loc)
DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))phylores=read.table("../data/PhyloP/PAS_phyloP.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na()
NucReswPhy=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr","start","end"))ggplot(NucReswPhy,aes(y=phyloP, x=SigPAU2,fill=SigPAU2)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2", name="Signficant")
ggplot(NucReswPhy,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2", name="Signficant PAS") + labs(title="Mean PhyloP scores for tested PAS") + annotate("text",label="Wilcoxan, p=1.4e -5",x=6,y=.75)
The significant PAS have on average lower phyloP scores.
Positive scores — Measure conservation, which is slower evolution than expected, at sites that are predicted to be conserved. Negative scores — Measure acceleration, which is faster evolution than expected, at sites that are predicted to be fast-evolving.
I can look at those with negative values:
x=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
m= nrow(NucReswPhy %>% filter(phyloP<0))
n=nrow(NucReswPhy %>% filter(phyloP>=0))
k=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))[1] 559#actual:
x[1] 604#pval
phyper(x,m,n,k,lower.tail=F)[1] 0.01326693b=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
n=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))
B=nrow(NucReswPhy %>% filter(phyloP<0))
N=nrow(NucReswPhy)
(b/n)/(B/N)[1] 1.079162This means these regions are more likely to be fast evolving.
Look at this by location: (is it driven by region)
NucReswPhy_meta= NucReswPhy %>% inner_join(PASMeta, by=c("chr", "start", "end", "gene"))
ggplot(NucReswPhy_meta,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2") + facet_grid(~loc)
NucReswPhy_meta_group=NucReswPhy_meta %>% group_by(loc,SigPAU2) %>% summarise(n=n(),meanPhylo=mean(phyloP))
NucReswPhy_meta_group# A tibble: 10 x 4
# Groups: loc [5]
loc SigPAU2 n meanPhylo
<chr> <chr> <int> <dbl>
1 cds No 7048 2.15
2 cds Yes 262 2.14
3 end No 3574 0.372
4 end Yes 172 0.324
5 intron No 13484 0.0622
6 intron Yes 544 0.0833
7 utr3 No 15408 1.04
8 utr3 Yes 1280 0.904
9 utr5 No 1158 0.300
10 utr5 Yes 81 0.261 Control sequence:upstream
(upstream 200)
Look at the 200 basepairs upstream of each PAS as a control.
metaStrand=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% select(chr, start,end, strandFix, PAS)
NucReswPhy_upstream=NucReswPhy %>% inner_join(metaStrand,by=c("chr", "start", "end")) %>% mutate(newStart=ifelse(strandFix=="+", start - 200, end), newEnd=ifelse(strandFix=="+", start, end +200))
NucReswPhy_upstreambed=NucReswPhy_upstream %>% select(chr, newStart, newEnd, PAS, Human, strandFix)
write.table(NucReswPhy_upstreambed,"../data/PhyloP/PAS_200upregions.bed",col.names = F,row.names = F,quote = F,sep="\t")python extractPhylopReg200up.py
Phylo200UpContron=read.table("../data/PhyloP/PAS_phyloP_200upstream.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","UpstreamControl_Phylop"))
NucReswPhyandC=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr", "start","end")) %>% inner_join(metaStrand,by=c("chr", "start", "end"))%>% inner_join(Phylo200UpContron, by="PAS") %>% drop_na()
NucReswPhyandCsmall=NucReswPhyandC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop ) %>% gather("set", "Phylop", -PAS, -SigPAU2)
wilcox.test(NucReswPhyandC$phyloP, NucReswPhyandC$UpstreamControl_Phylop, alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: NucReswPhyandC$phyloP and NucReswPhyandC$UpstreamControl_Phylop
W = 1088300000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0Actual are greater than region upstream
ggplot(NucReswPhyandCsmall, aes(x=SigPAU2, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c('PAS', 'Control') ) + stat_compare_means()
NucReswPhyandCsmall_noc= NucReswPhyandCsmall %>% filter(set!="UpstreamControl_Phylop")
ggplot(NucReswPhyandCsmall_noc, aes(x=SigPAU2, fill=SigPAU2, y=Phylop )) + geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "OrRd") + labs(title="Significant PAS",x="")+ scale_x_discrete(labels=c("Not Significant", "Signficant"))+ theme(legend.position = "none",text= element_text(size=16))
Significant are lower than not significant:
NucReswPhyandCsmall_nocYES= NucReswPhyandCsmall_noc %>% filter(SigPAU2=="Yes")
NucReswPhyandCsmall_nocNO= NucReswPhyandCsmall_noc %>% filter(SigPAU2=="No")
wilcox.test(NucReswPhyandCsmall_nocYES$Phylop, NucReswPhyandCsmall_nocNO$Phylop, alternative ="less")
Wilcoxon rank sum test with continuity correction
data: NucReswPhyandCsmall_nocYES$Phylop and NucReswPhyandCsmall_nocNO$Phylop
W = 46695000, p-value = 0.0733
alternative hypothesis: true location shift is less than 0Significant have lower scores.
Negative enrichment
Number of negative in each set?
neg=NucReswPhyandCsmall %>% filter(Phylop <0) %>% group_by(set, SigPAU2) %>% summarise(nNeg=n())
pos=NucReswPhyandCsmall %>% filter(Phylop >0) %>% group_by(set, SigPAU2) %>% summarise(nPos=n())
both=neg %>% inner_join(pos,by= c('set', 'SigPAU2')) %>% mutate(PropNeg=nNeg/(nNeg+nPos))
both# A tibble: 4 x 5
# Groups: set [2]
set SigPAU2 nNeg nPos PropNeg
<chr> <chr> <int> <int> <dbl>
1 phyloP No 9685 30967 0.238
2 phyloP Yes 604 1735 0.258
3 UpstreamControl_Phylop No 13229 27423 0.325
4 UpstreamControl_Phylop Yes 817 1522 0.349More negative overall in actual. Is there an enrichment for negative in the control set?
x=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes", UpstreamControl_Phylop<0))
m= nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop<0))
n=nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop>=0))
k=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes"))
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))[1] 764#actual:
x[1] 817#pval
phyper(x,m,n,k,lower.tail=F)[1] 0.00806816b=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes", UpstreamControl_Phylop<0))
n=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes"))
B=nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop<0))
N=nrow(NucReswPhyandC)
(b/n)/(B/N)[1] 1.069096Stronger enrichement in the for negative in the real results compared to contol. 1.07x in control 1.11x in actual.
Maybe I need to move the control further up.
Is this a better control? Dont want to go into an exon? What about downstream?
Control sequence (downstream 200):
NucReswPhy_downstream=NucReswPhy %>% inner_join(metaStrand,by=c("chr", "start", "end")) %>% mutate(newStart=ifelse(strandFix=="+", end, start-200), newEnd=ifelse(strandFix=="+", end+200, start))
NucReswPhy_downstreambed=NucReswPhy_downstream %>% select(chr, newStart, newEnd, PAS, Human, strandFix)
write.table(NucReswPhy_downstreambed,"../data/PhyloP/PAS_200downpregions.bed",col.names = F,row.names = F,quote = F,sep="\t")python extractPhylopReg200down.py
Phylo200downCont=read.table("../data/PhyloP/PAS_phyloP_200downstream.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","DownstreamControl_Phylop"))
NucReswPhyandbothC=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr", "start","end")) %>% inner_join(metaStrand,by=c("chr", "start", "end"))%>% inner_join(Phylo200UpContron, by="PAS") %>% drop_na() %>% inner_join(Phylo200downCont, by="PAS") %>% drop_na()
NucReswPhyandCbothsmall=NucReswPhyandbothC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop,DownstreamControl_Phylop ) %>% gather("set", "Phylop", -PAS, -SigPAU2) %>% drop_na()
#difference in controls?
wilcox.test(NucReswPhyandbothC$DownstreamControl_Phylop, NucReswPhyandbothC$UpstreamControl_Phylop,alternative = "greater")
Wilcoxon rank sum test with continuity correction
data: NucReswPhyandbothC$DownstreamControl_Phylop and NucReswPhyandbothC$UpstreamControl_Phylop
W = 23487000, p-value = 1
alternative hypothesis: true location shift is greater than 0levels=NucReswPhyandCbothsmall$set %>% unique()
NucReswPhyandCbothsmall$set= factor(NucReswPhyandCbothsmall$set, levels = c("UpstreamControl_Phylop", "phyloP", "DownstreamControl_Phylop"))
my_comparisons <- list( c("DownsreamControl_Pylop", "phylopP"), c("DownsreamControl_Pylop", "UpstreamControl_Phylop"), c("phylopP", "UpstreamControl_Phylop") )
ggplot(NucReswPhyandCbothsmall, aes(x=set, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c("Upstream Control", "PAS", "Downstream Control") ) + stat_compare_means(ref.group = "phyloP",paired = FALSE,label = "p.signif") + labs(x="", title="PAS conserved compared to surrounding regions" ) + scale_x_discrete( labels=c("Upstream Control", "PAS", "Downstream Control"))+ theme(legend.position = "none",text= element_text(size=16))
Same here. The actual region looks more conserved.
ggplot(NucReswPhyandCbothsmall, aes(x=SigPAU2, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c("Downstream Control", "PAS", "Upstream Control") )
Extend
2 more blocks up and downstream to add to plot.
Extend downstream:
NucReswPhy_downstream2=NucReswPhy_downstream %>% mutate(newStart2=ifelse(strandFix=="+", newEnd, newStart-200), newEnd2=ifelse(strandFix=="+", newEnd+200, newStart))
NucReswPhy_downstream2bed=NucReswPhy_downstream2 %>% select(chr, newStart2, newEnd2, PAS, Human, strandFix)
write.table(NucReswPhy_downstream2bed,"../data/PhyloP/PAS_200downpregions2.bed",col.names = F,row.names = F,quote = F,sep="\t")
NucReswPhy_downstream3=NucReswPhy_downstream2 %>% mutate(newStart3=ifelse(strandFix=="+", newEnd2, newStart2-200), newEnd3=ifelse(strandFix=="+", newEnd2+200, newStart2))
NucReswPhy_downstream3bed=NucReswPhy_downstream3 %>% select(chr, newStart3, newEnd3, PAS, Human, strandFix)
write.table(NucReswPhy_downstream3bed,"../data/PhyloP/PAS_200downpregions3.bed",col.names = F,row.names = F,quote = F,sep="\t")Extend upstream:
NucReswPhy_upstream2=NucReswPhy_upstream %>% mutate(newStart2=ifelse(strandFix=="+", newStart - 200, newEnd), newEnd2=ifelse(strandFix=="+", newStart, newEnd +200))
NucReswPhy_upstreambed2=NucReswPhy_upstream2 %>% select(chr, newStart2, newEnd2, PAS, Human, strandFix)
write.table(NucReswPhy_upstreambed2,"../data/PhyloP/PAS_200upregions2.bed",col.names = F,row.names = F,quote = F,sep="\t")
NucReswPhy_upstream3=NucReswPhy_upstream2 %>% mutate(newStart3=ifelse(strandFix=="+", newStart2 - 200, newEnd2), newEnd3=ifelse(strandFix=="+", newStart2, newEnd2 +200))
NucReswPhy_upstreambed3=NucReswPhy_upstream3 %>% select(chr, newStart3, newEnd3, PAS, Human, strandFix)
write.table(NucReswPhy_upstreambed3,"../data/PhyloP/PAS_200upregions3.bed",col.names = F,row.names = F,quote = F,sep="\t")Run phylop for each of these:
python extractPhylopGeneral.py ../data/PhyloP/PAS_200downpregions2.bed ../data/PhyloP/PAS_phyloP_200downstream2.txt
python extractPhylopGeneral.py ../data/PhyloP/PAS_200downpregions3.bed ../data/PhyloP/PAS_phyloP_200downstream3.txt
python extractPhylopGeneral.py ../data/PhyloP/PAS_200upregions2.bed ../data/PhyloP/PAS_phyloP_200upstream2.txt
python extractPhylopGeneral.py ../data/PhyloP/PAS_200upregions3.bed ../data/PhyloP/PAS_phyloP_200upstream3.txt
ResUpdown=NucReswPhyandbothC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop,DownstreamControl_Phylop )
Down2=read.table("../data/PhyloP/PAS_phyloP_200downstream2.txt",col.names = c("chr", "start", "end", "PAS", "Down2"),stringsAsFactors = F) %>% select(PAS, Down2)%>% drop_na()
Down3=read.table("../data/PhyloP/PAS_phyloP_200downstream3.txt",col.names = c("chr", "start", "end", "PAS", "Down3"),stringsAsFactors = F) %>% select(PAS, Down3)%>% drop_na()
Up2=read.table("../data/PhyloP/PAS_phyloP_200upstream2.txt",col.names = c("chr", "start", "end", "PAS", "Up2"),stringsAsFactors = F) %>% select(PAS, Up2)%>% drop_na()
Up3=read.table("../data/PhyloP/PAS_phyloP_200upstream3.txt",col.names = c("chr", "start", "end", "PAS", "Up3"),stringsAsFactors = F) %>% select(PAS, Up3)%>% drop_na()
ResUpdownAll= ResUpdown %>% inner_join(Down2, by="PAS")%>% inner_join(Down3, by="PAS") %>% inner_join(Up2, by="PAS") %>% inner_join(Up3, by="PAS")
ResUpdownAll_gather= ResUpdownAll %>% gather("Set", "PhyloP", -PAS, -SigPAU2)
ResUpdownAll_gather$Set=factor(ResUpdownAll_gather$Set, levels=c("Up3", "Up2","UpstreamControl_Phylop", "phyloP","DownstreamControl_Phylop", "Down2", "Down3" ))ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")
ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + facet_grid(~SigPAU2)+ scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")
Change colors:
ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")+ theme_classic()+ guides(fill = FALSE)
Color just PAS and surrounding:
ResUpdownAll_gather2= ResUpdownAll_gather %>% mutate(region=ifelse(Set=="phyloP", "Yes", "No"))
ggplot(ResUpdownAll_gather2, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + guides(fill = FALSE) + theme_classic()
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] reshape2_1.4.3 ggpubr_0.2 magrittr_1.5 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
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 utf8_1.1.4 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 workflowr_1.6.0 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