Last updated: 2020-03-04

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Rmd ad95271 brimittleman 2020-03-04 add upstream control

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 ──
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✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
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library(ggpubr)
Loading required package: magrittr

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library(reshape2)

Attaching package: 'reshape2'
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    smiths
DiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)

PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_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"))

40756 have results of the 40776

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] 707
#actual:
x
[1] 788
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.0001570509
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] 1.114898

This 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       7141    2.16  
 2 cds    Yes       333    2.16  
 3 end    No       3564    0.450 
 4 end    Yes       247    0.403 
 5 intron No      10478    0.0630
 6 intron Yes       737    0.0702
 7 utr3   No      15351    1.04  
 8 utr3   Yes      1659    0.933 
 9 utr5   No       1151    0.300 
10 utr5   Yes        95    0.230

Control sequence (upstream 200):

Look at the 200 basepairs upstream of each PAS as a control.

metaStrand=read.table("../data/PAS_doubleFilter/PAS_10perc_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 = 990510000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0

Actual 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 = "Dark2")

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 = 55118000, p-value = 6.843e-06
alternative hypothesis: true location shift is less than 0

Significant have lower scores.

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       8592 29079   0.228
2 phyloP                 Yes       788  2283   0.257
3 UpstreamControl_Phylop No      12085 25586   0.321
4 UpstreamControl_Phylop Yes      1062  2009   0.346

More 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] 991
#actual:
x
[1] 1062
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.002137693
b=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] 1.071668

Stronger 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):


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_1.0.2         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