Last updated: 2020-05-21

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd fd04a01 brimittleman 2020-05-21 add U rich analysis

there are downstream and upstream U rich elements that direct APA. I will test if PAS differentially used have different U content.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(tidyverse)
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✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
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library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggpubr':

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mkdir ../data/UrichElements

I will use bedtools nuc to get the T (U) content.

bedtools nuc -s -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed > ../data/UrichElements/HumanPAS_Nuc.txt


bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa -bed ../data/PAS_doubleFilter/PAS_doublefilter_either_ChimpCoordChimpUsage.sort.bed > ../data/UrichElements/ChimpPAS_Nuc.txt
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T,stringsAsFactors =F)

names=c("chr", "start",'end', 'PAS','score','strand','pct_at',  'pct_gc',   'num_A',    'num_C',    'num_G',    'num_T',    'num_N',    'num_oth',  'seq_len')
HumanPASSeq=read.table("../data/UrichElements/HumanPAS_Nuc.txt", col.names = names, header = F, stringsAsFactors = F) %>% mutate(HumanPropT=num_T/seq_len) %>% select(PAS, HumanPropT)

ChimpPASSeq=read.table("../data/UrichElements/ChimpPAS_Nuc.txt", col.names = names, header = F, stringsAsFactors = F)%>% mutate(ChimpPropT=num_T/seq_len)%>% select(PAS, ChimpPropT)


PASMetaWithT=PASMeta %>% inner_join(ChimpPASSeq, by="PAS") %>% inner_join(HumanPASSeq, by="PAS") %>% mutate(DiffHminT=HumanPropT-ChimpPropT)
ggplot(PASMetaWithT, aes(y=ChimpPropT,x=HumanPropT)) + geom_point() + geom_density_2d(col="blue") + stat_cor()

ggplot(PASMetaWithT, aes(x=DiffHminT)) + geom_density() 

summary(lm(PASMetaWithT$ChimpPropT~PASMetaWithT$HumanPropT ))

Call:
lm(formula = PASMetaWithT$ChimpPropT ~ PASMetaWithT$HumanPropT)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.037455 -0.002197 -0.000013  0.002008  0.040090 

Coefficients:
                         Estimate Std. Error  t value Pr(>|t|)    
(Intercept)             0.0005890  0.0001016    5.795 6.88e-09 ***
PASMetaWithT$HumanPropT 0.9979439  0.0003491 2858.919  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.005109 on 44430 degrees of freedom
Multiple R-squared:  0.9946,    Adjusted R-squared:  0.9946 
F-statistic: 8.173e+06 on 1 and 44430 DF,  p-value: < 2.2e-16

Color by differentially used:

Meta_genes= PASMeta %>% select(gene) %>% unique()

Meta_PAS=PASMeta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(PASMeta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No"))
dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)
dICdata_sig= dICdata %>% filter(sIC=="Yes")
dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))


OnlyAPAGenes= dAPAandDic %>% filter(OnlyAPA=="Yes") %>% select(gene) %>% mutate(set="Site")
IsoformGenes=  dAPAandDic %>% filter(OnlyIC=="Yes") %>% select(gene) %>% mutate(set="Isoform")
BothGenes=  dAPAandDic %>% filter(Both=="Yes") %>% select(gene) %>% mutate(set="Both")
NoneGenes=dAPAandDic %>% filter(dAPA=="No" & sIC=="No" ) %>% select(gene) %>% mutate(set="Conserved")


CharacterizeAllGenes= OnlyAPAGenes %>% bind_rows(IsoformGenes) %>% bind_rows(BothGenes)%>% bind_rows(BothGenes) %>% bind_rows(NoneGenes) %>% mutate(OverAllCons=ifelse(set=="Conserved", "Yes","No"))



CharacterizeAllGenes_T= PASMetaWithT %>% inner_join(CharacterizeAllGenes,by="gene")

First seperate by these genes:

ggplot(CharacterizeAllGenes_T, aes(y=ChimpPropT,x=HumanPropT)) + geom_point() + geom_density_2d(col="blue") + stat_cor()+ facet_grid(~set)

ggplot(CharacterizeAllGenes_T, aes(x=DiffHminT, col=set)) + geom_density(alpha=.5)

ggplot(CharacterizeAllGenes_T, aes(y=ChimpPropT,x=HumanPropT)) + geom_point() + geom_density_2d(col="blue") + stat_cor()+ facet_grid(~OverAllCons)

ggplot(CharacterizeAllGenes_T, aes(x=DiffHminT, fill=OverAllCons)) + geom_density(alpha=.5)

Look at site level PAS difference:

PASMetaWithT_sigPAS= PASMetaWithT %>% inner_join(dAPAPAS, by=c("PAS", "gene"))


ggplot(PASMetaWithT_sigPAS, aes(y=ChimpPropT,x=HumanPropT, col=SigPAU2)) + geom_point() + geom_density_2d() + stat_cor()

ggplot(PASMetaWithT_sigPAS, aes(y=ChimpPropT,x=HumanPropT, col=SigPAU2))  + geom_density_2d() + stat_cor()

ggplot(PASMetaWithT_sigPAS, aes(x=DiffHminT, fill=SigPAU2)) + geom_density(alpha=.5)

ggplot(PASMetaWithT_sigPAS, aes(x=DiffHminT, col=SigPAU2)) + stat_ecdf()

difference in the distribution?

PASMetaWithT_sigPAS_sig= PASMetaWithT_sigPAS %>% filter(SigPAU2=="Yes")

PASMetaWithT_sigPAS_Nosig= PASMetaWithT_sigPAS %>% filter(SigPAU2=="No")


wilcox.test(PASMetaWithT_sigPAS_sig$DiffHminT, PASMetaWithT_sigPAS_Nosig$DiffHminT)

    Wilcoxon rank sum test with continuity correction

data:  PASMetaWithT_sigPAS_sig$DiffHminT and PASMetaWithT_sigPAS_Nosig$DiffHminT
W = 47081000, p-value = 0.3036
alternative hypothesis: true location shift is not equal to 0

Plot T vs deltaPAU:

dAPAPASPAU=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(PASMeta, by=c("chr","start", "end","gene")) %>% select(PAS,deltaPAU) 

PASMetaWithT_sigPAS_pau=PASMetaWithT_sigPAS %>% inner_join(dAPAPASPAU,by="PAS")
ggplot(PASMetaWithT_sigPAS_pau, aes(x=deltaPAU, y=DiffHminT)) +geom_point() +stat_cor() + facet_grid(~SigPAU2)

Do this the way i did the signal site analysis. Are significant PAS more likely to have non zero differences?

PASMetaWithT_sigPAS_pau_cat= PASMetaWithT_sigPAS_pau %>% mutate(DiffTeither= ifelse(DiffHminT != 0, "Yes", "No"))
x= nrow(PASMetaWithT_sigPAS_pau_cat %>% filter(SigPAU2=="Yes",DiffTeither=="Yes"))
m=nrow(PASMetaWithT_sigPAS_pau_cat %>% filter(DiffTeither=="Yes"))
n=nrow(PASMetaWithT_sigPAS_pau_cat %>% filter(DiffTeither!="Yes"))
k= nrow(PASMetaWithT_sigPAS_pau_cat %>% filter(SigPAU2=="Yes"))

N=nrow(PASMetaWithT_sigPAS_pau_cat)

x
[1] 1426
phyper(x-1,m,n,k,lower.tail=F)
[1] 3.458623e-08
(x/k)/(m/N)
[1] 1.09697

Check direction.

I exprect lower T content to mean lower usage. or opposite

  • PAU upreg in chimp

  • T diff is chimp higher value

direction:

we expect chimp upregulation, when the chimps have higher T/U content

DO this among sig values:

PASMetaWithT_sigPAS_pau_cat_sig=PASMetaWithT_sigPAS_pau_cat %>% filter(SigPAU2=="Yes",DiffHminT!=0)

x= nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(deltaPAU<0,DiffHminT<0))
m=nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(DiffHminT<0))
n=nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(DiffHminT>=0))
k= nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(deltaPAU<0))

N=nrow(PASMetaWithT_sigPAS_pau_cat_sig)

x
[1] 415
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.03298297
(x/k)/(m/N)
[1] 1.044766

Human direction

x= nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(deltaPAU>0,DiffHminT>0))
m=nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(DiffHminT>0))
n=nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(DiffHminT<=0))
k= nrow(PASMetaWithT_sigPAS_pau_cat_sig %>% filter(deltaPAU>0))

N=nrow(PASMetaWithT_sigPAS_pau_cat_sig)

x
[1] 335
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.03298297
(x/k)/(m/N)
[1] 1.056056

This is like the signal site analysis. If there is a difference in T/U content then site is more likley to be diff used in the correct direction.

Difference in U content does not explain difference is usage.


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] cowplot_0.9.4   forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] tidyverse_1.2.1 ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  
[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      lattice_0.20-38 
 [5] colorspace_1.3-2 generics_0.0.2   htmltools_0.3.6  yaml_2.2.0      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.4.6     promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1