Last updated: 2020-04-20

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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Rmd b00be28 brimittleman 2020-04-20 add new dom to anaylses

I will look at conservation with my new definition of dominant PAS.I will start with the PAS dominant at 0.4 then test robustnes.

library(tidyverse)
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library(workflowr)
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library(ggpubr)
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    extract
Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) 

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_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)

#all dom set is the list that are >.4 in either species 
ChimpRes_40=ChimpRes %>% filter(Chimp_DiffDom>=.4)
HumanRes_40=HumanRes %>% filter(Human_DiffDom>=.4)
AllDom=c(ChimpRes_40$Chimp_PAS, HumanRes_40$Human_PAS) %>% unique()


phylores=read.table("../data/PhyloP/PAS_phyloP.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na() %>% inner_join(Meta, by=c("chr", "start", "end")) %>% mutate(EitherDom=ifelse(PAS %in% AllDom, "Yes", "No"))

First test if the dominant PAS are more conserved than the non dominant PAS.

ggplot(phylores, aes(y=phyloP, x=EitherDom, fill=EitherDom)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method.args = list(alternative = "greater"))+labs(title="Dominant PAS are more conserved than non dominant PAS", x="PAS dominance structure")+ theme(legend.position = "none")

Is this location driven?

ggplot(phylores, aes(y=phyloP, x=EitherDom, fill=EitherDom)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + stat_compare_means(method.args = list(alternative = "greater"))+labs(title="Dominant PAS are more conserved than non dominant PAS", x="PAS dominance structure")+ theme(legend.position = "none") + facet_grid(~loc)

This is interesting, it looks like the extended isoforms show the difference.

Filter for these genes and ask about same different PAS

BothRes_40g= BothRes_40 %>% gather(species, PAS, -gene, -cut, -Set,-Human_DiffDom, -Chimp_DiffDom ) %>% select(PAS, Set) %>% unique()

phylores_dom= phylores %>% inner_join(BothRes_40g, by="PAS")

ggplot(phylores_dom, aes(x=Set, y=phyloP, fill=Set)) + geom_boxplot()+ scale_fill_brewer(palette = "Dark2") + stat_compare_means(method.args = list(alternative = "less")) + labs(title="Matching Domionant PAS are more conserved than non matching dominant PAS", x="PAS dominance structure")+ theme(legend.position = "none")

By location:

ggplot(phylores_dom, aes(x=Set, y=phyloP, fill=Set)) + geom_boxplot()+ scale_fill_brewer(palette = "Dark2") + stat_compare_means(method.args = list(alternative = "less")) + labs(title="Matching Domionant PAS are more conserved than non matching dominant PAS", x="PAS dominance structure")+ theme(legend.position = "none") + facet_grid(~loc)

This suggest sequence conservation does play a role in the conservation of dominant PAS. This is not driven by 1 location.

DNDS

At the gene level I want to look at conservation for the full gene when the dominant PAS is the same or different. I can use DNDS for this.

DN (non synonymous) /DS (synonymous): from ensamble site - ratio of substitution rate (quick and dirty way to look at evo), ratio >1 usually evidence for positive selection. values are in ../data/DNDS/HumanChimp_DNDS.csv

Remove NA values

We can ask if genes with different dominant show evidence for positive selection:

DNDS= read.csv("../data/DNDS/HumanChimp_DNDS.csv", header = T,stringsAsFactors = F) %>% drop_na() %>% group_by(Gene.name) %>% slice(1) %>% ungroup() %>% mutate(DNDSratio= dN.with.Chimpanzee/dS.with.Chimpanzee) %>% dplyr::select(Gene.name, dN.with.Chimpanzee,dS.with.Chimpanzee,DNDSratio) %>% rename("gene"=Gene.name)

BothRes_40_dnds= BothRes_40 %>% inner_join(DNDS,by="gene")

#filter the infinity (syn ==0)
BothRes_40_dnds_filt= BothRes_40_dnds %>% filter(dS.with.Chimpanzee >0)

I will first plot them then ask for enrichment of DN/DS >1 in each set.

ggplot(BothRes_40_dnds_filt,aes(x=Set, y=DNDSratio, fill=Set)) + geom_boxplot()+  scale_fill_brewer(palette = "Dark2") + stat_compare_means(method.args = list(alternative = "greater")) + labs(title="Genes with different domiannt dominant have higher DN/DS scores",x="Dominance structure")+ theme(legend.position = "none") 

x=nrow(BothRes_40_dnds_filt %>% filter(Set=="Different", DNDSratio>=1))
m= nrow(BothRes_40_dnds_filt %>% filter(DNDSratio>=1))
n=nrow(BothRes_40_dnds_filt %>% filter(DNDSratio<1))
k=nrow(BothRes_40_dnds_filt %>% filter(Set=="Different"))

phyper(x,m,n,k,lower.tail=F)
[1] 0.1830808

No enrichment for genes with evidence of positive selection.

O,1 means constraint:

x=nrow(BothRes_40_dnds_filt %>% filter(Set=="Different", DNDSratio<1))
m= nrow(BothRes_40_dnds_filt %>% filter(DNDSratio<1))
n=nrow(BothRes_40_dnds_filt %>% filter(DNDSratio>=1))
k=nrow(BothRes_40_dnds_filt %>% filter(Set=="Different"))

phyper(x,m,n,k,lower.tail=F)
[1] 0.6951448

No enrichment here either.


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    workflowr_1.6.0 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   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         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       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     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1