Last updated: 2020-05-05

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.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/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File Version Author Date Message
Rmd 6850c9d brimittleman 2020-05-05 fix enrichemnt
html 8f72fb7 brimittleman 2020-04-10 Build site.
Rmd db347d0 brimittleman 2020-04-10 remove 184999
html b45003d brimittleman 2020-04-07 Build site.
Rmd fbcbbd2 brimittleman 2020-04-07 add non con analysis
html 2242e4a brimittleman 2020-04-06 Build site.
Rmd 92f06e4 brimittleman 2020-04-06 update anno
html 332cbca brimittleman 2020-03-19 Build site.
Rmd 4e2a869 brimittleman 2020-03-19 add SS in opp
html ac2b058 brimittleman 2020-01-26 Build site.
Rmd 6658151 brimittleman 2020-01-26 add density plots
html 838da9b brimittleman 2020-01-23 Build site.
Rmd a018c33 brimittleman 2020-01-23 add diff usgage and ss

In this analysis, I will ask if presence or absense of a signal site in either species can explain the diffentially used PAS. I can do overlaps and look at correlations.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(RColorBrewer)
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(tidyverse)
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✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ─────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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topSS=c("AATAAA", "ATTAAA")
MetaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSS.txt", header = T, stringsAsFactors = F) %>% mutate(ChimpTopSS=ifelse(ChimpPAS %in% topSS, "Yes", "No"),HumanTopSS=ifelse(HumanPAS %in% topSS, "Yes", "No") )

Pull in the differentiall used PAS:

DiffUsed=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F) %>% rename("Human_NormUse"=Human, "Chimp_NormUse"=Chimp)

Join this by chr, start,end, gene

DiffUsed_anno=DiffUsed %>% inner_join(MetaPAS,by=c("chr","start", "end","gene"))

Ask how many of the effect size either way are those with SS in one species

  • upreg in human
  • upred in chimp
DiffUsed_anno_humanup=DiffUsed_anno %>% filter(deltaPAU>0)
nrow(DiffUsed_anno_humanup)
[1] 1030
nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
[1] 32
DiffUsed_anno_chimpup=DiffUsed_anno %>% filter(deltaPAU<0)
nrow(DiffUsed_anno_chimpup)
[1] 1312
nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
[1] 32

Significance:

humanOnlyPattern=MetaPAS %>%  filter(HumanTopSS=="Yes", ChimpTopSS=="No")

chimpOnlyPattern=MetaPAS %>%  filter(HumanTopSS=="No", ChimpTopSS=="Yes")

nrow(humanOnlyPattern)+nrow(chimpOnlyPattern)
[1] 720

Of the 44432 we see that 720 have the pattern of interest. We choose 1312 of them and 32 come out. Look to see if it is more than expected by change

Human up reg: phyper(success in sample, sucesss in possible, failure possible, sample size)

up with pattern up reg general no up used all with pattern

#DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No")
x= nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
m= nrow(DiffUsed_anno_humanup)
n=nrow(MetaPAS)-m
k=nrow(humanOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 1.366468e-10
(x/k)/(m/N)
[1] 3.823855
x
[1] 32

This means that of those that have a top signal site only in human there is an enrichment for dAPA PAS.

Try oppostie dir.

x= nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
m= nrow(DiffUsed_anno_humanup)
n=nrow(MetaPAS)-m
k=nrow(chimpOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.0420955
(x/k)/(m/N)
[1] 1.682257
x
[1] 14

So this is 32 vs 14.

Do this for chimp.

x= nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
m= nrow(DiffUsed_anno_chimpup)
n=nrow(MetaPAS)-m
k=nrow(chimpOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 3.906968e-08
(x/k)/(m/N)
[1] 3.018683

Strong enrichment here too:

x= nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
m= nrow(DiffUsed_anno_chimpup)
n=nrow(MetaPAS)-m
k=nrow(humanOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.9943171
(x/k)/(m/N)
[1] 0.3752449
x
[1] 4

No enrichment for this pattern.

This direction has 32 vs 4.

This can be something I use. This suggests that if there are differences in signal site they are likely to lead to differential usage in the expected direction.

Next question: is the pattern enriched in PAS called as dAPA: Are dAPA more likely than expected to have this pattern?

I can use a chisq test of independence.

DiffUsed_annotest= DiffUsed_anno %>% mutate(UpReg=ifelse(deltaPAU< 0, "Chimp", "Human"), Pattern=ifelse(ChimpTopSS=="Yes", ifelse(HumanTopSS=="Yes", "none", "Chimp"), ifelse(HumanTopSS=="Yes", "Human", "none")))  %>% select(PAS, UpReg,Pattern ) %>% filter(Pattern!="none")

DiffUsed_annotest %>% group_by(UpReg, Pattern) %>% summarise(nType=n())
# A tibble: 4 x 3
# Groups:   UpReg [2]
  UpReg Pattern nType
  <chr> <chr>   <int>
1 Chimp Chimp      32
2 Chimp Human       4
3 Human Chimp      14
4 Human Human      32
toTest=DiffUsed_annotest %>% group_by(UpReg, Pattern) %>% summarise(nType=n()) %>% spread(Pattern, nType) %>% column_to_rownames("UpReg")

chisq.test(toTest)

    Pearson's Chi-squared test with Yates' continuity correction

data:  toTest
X-squared = 25.695, df = 1, p-value = 3.998e-07

This is significant

DiffUsed_annotest %>% group_by(UpReg, Pattern) %>% summarise(nType=n()) %>% spread(Pattern, nType)
# A tibble: 2 x 3
# Groups:   UpReg [2]
  UpReg Chimp Human
  <chr> <int> <int>
1 Chimp    32     4
2 Human    14    32

Are these the strongest differences?

DiffUsed_annoPatternAssign= DiffUsed_anno %>% mutate(UpReg=ifelse(deltaPAU< 0, "Chimp", "Human"), Pattern=ifelse(ChimpTopSS=="Yes", ifelse(HumanTopSS=="Yes", "none", "Chimp"), ifelse(HumanTopSS=="Yes", "Human", "none")), ExpectedPattern=ifelse(Pattern!="none", "Yes", "No"))

DiffUsed_annoPatternAssign$ExpectedPattern= as.factor(DiffUsed_annoPatternAssign$ExpectedPattern)
ggplot(DiffUsed_annoPatternAssign, aes(x=ExpectedPattern, y=abs(deltaPAU), fill=ExpectedPattern))+ geom_boxplot()+ geom_jitter(alpha=.1)+stat_compare_means() + scale_fill_brewer(palette = "Set1") + labs(title="delta PAU by signal site in expected direction") + theme(legend.position = "none")

Plot dPAU by presence and absense:

ggplot(DiffUsed_anno_humanup,aes(y=abs(deltaPAU), x=HumanTopSS))+ geom_boxplot()+ stat_compare_means() + labs(title="Human upregualted PAS by presence of Signal")

ggplot(DiffUsed_anno_humanup,aes(x=abs(deltaPAU), by=HumanTopSS, fill=HumanTopSS))+ geom_density(alpha=.5)+  labs(title="Human upregualted PAS by presence of Signal") + scale_fill_discrete(name="Human Signal Site Detected")

ggplot(DiffUsed_anno_chimpup,aes(x=ChimpTopSS, y=abs(deltaPAU))) + geom_boxplot() + stat_compare_means() + labs(title="Chimp upregualted PAS by presence of Signal")

ggplot(DiffUsed_anno_chimpup,aes(x=abs(deltaPAU), by=ChimpTopSS, fill=ChimpTopSS))+ geom_density(alpha=.5)+  labs(title="Chimp upregualted PAS by presence of Signal") + scale_fill_discrete(name="Chimp Signal Site Detected") + annotate("text",label="Wilcoxon, p=0.035",x=.8,y=7.5)

It does not look like presence of a signal within the upregulated matters.

I choose these is the original SS analysis. I used the chooseSignalSite.py it was a hierarchical model

MetaAllSS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSS.txt",stringsAsFactors = F,header = T)
MetaAllSSsmall=MetaAllSS %>% select(chr, start,end,PAS)

This has every PAS with a signal site. I will add information about if the PAS is differentially used.

DiffUsed_small= DiffUsed %>% select(chr,start,end, gene, deltaPAU, p.adjust) %>% inner_join(MetaAllSSsmall,by=c("chr",'start','end'))

DiffUsed_small_human= DiffUsed_small %>% filter(deltaPAU>0)
DiffUsed_small_chimp= DiffUsed_small %>% filter(deltaPAU<0)

MetaAllSS_diffUsage=MetaAllSS %>% mutate(DiffUsed=ifelse(PAS %in% DiffUsed_small$PAS, "yes","no"), HumanUp=ifelse(PAS %in% DiffUsed_small_human$PAS, "yes","no"), ChimpUp=ifelse(PAS %in% DiffUsed_small_chimp$PAS, "yes","no"))

Look at any vs none.

x= nrow(MetaAllSS_diffUsage %>% filter(HumanUp=="yes",HumanPAS!="None", ChimpPAS=="None"))
m=nrow(MetaAllSS_diffUsage %>% filter(HumanUp=="yes"))
n=nrow(MetaAllSS_diffUsage %>% filter(HumanUp!="yes"))
k= nrow(MetaAllSS_diffUsage %>% filter(HumanPAS!="None", ChimpPAS=="None"))

N=nrow(MetaAllSS_diffUsage)

x
[1] 8
#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.4952338
(x/k)/(m/N)
[1] 1.048945

Chimp:

x= nrow(MetaAllSS_diffUsage %>% filter(ChimpUp=="yes",HumanPAS=="None", ChimpPAS!="None"))
m=nrow(MetaAllSS_diffUsage %>% filter(ChimpUp=="yes"))
n=nrow(MetaAllSS_diffUsage %>% filter(ChimpUp!="yes"))
k= nrow(MetaAllSS_diffUsage %>% filter(HumanPAS=="None", ChimpPAS!="None"))

N=nrow(MetaAllSS_diffUsage)


#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.00935056
(x/k)/(m/N)
[1] 1.858492
x
[1] 18

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] forcats_0.3.0      stringr_1.3.1      dplyr_0.8.0.1     
 [4] purrr_0.3.2        readr_1.3.1        tidyr_0.8.3       
 [7] tibble_2.1.1       tidyverse_1.2.1    ggpubr_0.2        
[10] magrittr_1.5       ggplot2_3.1.1      RColorBrewer_1.1-2
[13] workflowr_1.6.0   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [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] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.4.6     promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        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      lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  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