Last updated: 2020-12-11

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
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    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File Version Author Date Message
Rmd a43b269 brimittleman 2020-12-11 add phylop20 analysis 
library(tidyverse)
── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.2.1     ✓ purrr   0.3.4
✓ tibble  2.1.3     ✓ dplyr   0.8.3
✓ tidyr   1.1.0     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.4.0
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(ggpubr)
library(workflowr)
This is workflowr version 1.6.2
Run ?workflowr for help getting started

Copy and edit extractPhylopGeneral.py for phylop 20 way.

#extractPhylopGeneral_20way.py

python extractPhylopGeneral_20way.py  ../data/PhyloP/PAS_200downpregions2.bed ../data/PhyloP/PAS_phyloP20_200downstream2.txt 

python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200downpregions3.bed ../data/PhyloP/PAS_phyloP20_200downstream3.txt 
python extractPhylopGeneral_20way.py  ../data/PhyloP/PAS_200downpregions.bed ../data/PhyloP/PAS_phyloP20_200downstream.txt 

python extractPhylopGeneral_20way.py  ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed ../data/PhyloP/PAS_Phylop20.txt

python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200upregions.bed ../data/PhyloP/PAS_phyloP20_200upstream.txt
python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200upregions2.bed ../data/PhyloP/PAS_phyloP20_200upstream2.txt
python extractPhylopGeneral_20way.py ../data/PhyloP/PAS_200upregions3.bed ../data/PhyloP/PAS_phyloP20_200upstream3.txt
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)

phylores=read.table("../data/PhyloP/PAS_Phylop20.txt", col.names = c("chr","start","end","PAS", "phyloP"), stringsAsFactors = F) %>% drop_na()


Down=read.table("../data/PhyloP/PAS_phyloP20_200downstream.txt",col.names = c("chr", "start", "end", "PAS", "Down"),stringsAsFactors = F) %>% select(PAS, Down)%>% drop_na()
Down2=read.table("../data/PhyloP/PAS_phyloP20_200downstream2.txt",col.names = c("chr", "start", "end", "PAS", "Down2"),stringsAsFactors = F) %>% select(PAS, Down2)%>% drop_na()
Down3=read.table("../data/PhyloP/PAS_phyloP20_200downstream3.txt",col.names = c("chr", "start", "end", "PAS", "Down3"),stringsAsFactors = F) %>% select(PAS, Down3)%>% drop_na()


Up=read.table("../data/PhyloP/PAS_phyloP_200upstream.txt",col.names = c("chr", "start", "end", "PAS", "Up"),stringsAsFactors = F) %>% select(PAS, Up)%>% 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= phylores %>% inner_join(Down, by="PAS")%>% inner_join(Down2, by="PAS") %>% inner_join(Down3, by="PAS") %>% inner_join(Up, by="PAS") %>% inner_join(Up3, by="PAS")%>% inner_join(Up2, by="PAS") %>% select(-chr, -start,-end)


ResUpdownAll_gather= ResUpdownAll %>% gather("Set", "PhyloP", -PAS)

ResUpdownAll_gather$Set=factor(ResUpdownAll_gather$Set, levels=c("Up3", "Up2","Up", "phyloP","Down", "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_classic()+ theme(legend.position = "none")

PASMetaSm=PASMeta %>% select(PAS,loc)
ResUpdownAll_loc=ResUpdownAll_gather %>% inner_join(PASMetaSm,by="PAS") %>% mutate(region=ifelse(Set=="phyloP", "Yes", "No"))
ggplot(ResUpdownAll_loc, 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 from PAS", title="PAS are more conserved than surrounding regions") + guides(fill = FALSE) + theme_classic() + facet_grid(~loc)

Compare significant and non significant PAS:

DiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)
DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))



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=0.0016",x=0,y=.75)

more <0 than expected:

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] 239
#actual:
x
[1] 326
#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 4.083027e-09
b=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] 1.360191

This is even more enriched than the 100 way phyloP.


sessionInfo()
R version 3.6.1 (2019-07-05)
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] workflowr_1.6.2 ggpubr_0.3.0    forcats_0.4.0   stringr_1.4.0  
 [5] dplyr_0.8.3     purrr_0.3.4     readr_1.3.1     tidyr_1.1.0    
 [9] tibble_2.1.3    ggplot2_3.2.1   tidyverse_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         lubridate_1.7.4    lattice_0.20-38   
 [4] assertthat_0.2.1   rprojroot_1.3-2    digest_0.6.20     
 [7] plyr_1.8.4         R6_2.4.0           cellranger_1.1.0  
[10] backports_1.1.4    reprex_0.3.0       evaluate_0.14     
[13] httr_1.4.1         pillar_1.4.2       rlang_0.4.6       
[16] lazyeval_0.2.2     curl_3.3           readxl_1.3.1      
[19] rstudioapi_0.10    data.table_1.13.2  whisker_0.3-2     
[22] car_3.0-5          rmarkdown_1.13     labeling_0.3      
[25] foreign_0.8-71     munsell_0.5.0      broom_0.5.2       
[28] compiler_3.6.1     httpuv_1.5.1       modelr_0.1.8      
[31] xfun_0.8           pkgconfig_2.0.2    htmltools_0.3.6   
[34] tidyselect_1.1.0   rio_0.5.16         fansi_0.4.0       
[37] crayon_1.3.4       dbplyr_1.4.2       withr_2.1.2       
[40] later_0.8.0        grid_3.6.1         nlme_3.1-140      
[43] jsonlite_1.6       gtable_0.3.0       lifecycle_0.1.0   
[46] DBI_1.1.0          git2r_0.26.1       magrittr_1.5      
[49] scales_1.1.0       zip_2.0.3          carData_3.0-2     
[52] cli_2.2.0          stringi_1.4.3      reshape2_1.4.3    
[55] farver_2.0.1       ggsignif_0.5.0     fs_1.3.1          
[58] promises_1.0.1     xml2_1.3.2         ellipsis_0.2.0.1  
[61] generics_0.0.2     vctrs_0.3.0        openxlsx_4.1.0.1  
[64] RColorBrewer_1.1-2 tools_3.6.1        glue_1.3.1        
[67] hms_0.5.3          abind_1.4-5        yaml_2.2.0        
[70] colorspace_1.4-1   rstatix_0.5.0      rvest_0.3.5       
[73] knitr_1.23         haven_2.3.1