Last updated: 2020-04-07

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.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/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File Version Author Date Message
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)
── Attaching packages ───────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()
MetaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSSTop2.txt", header = T, stringsAsFactors = F)

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 chimp
  • upred in human
DiffUsed_anno_humanup=DiffUsed_anno %>% filter(deltaPAU<0)
nrow(DiffUsed_anno_humanup)
[1] 1609
nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
[1] 6
DiffUsed_anno_chimpup=DiffUsed_anno %>% filter(deltaPAU>0)
nrow(DiffUsed_anno_chimpup)
[1] 1523
nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
[1] 12

This means that of the 3132 only 18 are in PAS where signal site could account for it.

Significance:

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

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

humanOnly+chimpOnly
[1] 768

Of the 46494 we see that 768 have the pattern of interest. We choose 3076 of them and 19 come out. Look to see if it is more than expected by change

phyper(success in sample, sucesss in possible, failure possible, sample size)

x= 18
m= 768
n=46494
k=3132


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 18
phyper(18,768, 46494 , 3132,lower.tail=F)
[1] 1

No significant enrichment for the pattern.

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")

Version Author Date
2242e4a brimittleman 2020-04-06
838da9b brimittleman 2020-01-23
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")

Version Author Date
2242e4a brimittleman 2020-04-06
ac2b058 brimittleman 2020-01-26

Test with wilcoxan test:

There is a shift.

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

Version Author Date
2242e4a brimittleman 2020-04-06
838da9b brimittleman 2020-01-23
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)

Version Author Date
2242e4a brimittleman 2020-04-06
ac2b058 brimittleman 2020-01-26

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

Overall it does not look like presense of a signal site or not can explain the differences.

Opposite direction:

nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
[1] 33
nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
[1] 30

Look at the regions that have a non cononical signal site.

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"))

First question: Are there cononical in one and non connonical in the other species

con=c("AATAAA", "ATTAAA")
non=c('AAAAAG', 'AAAAAA', 'TATAAA', 'AATATA', 'AGTAAA', 'AATACA', 'GATAAA', 'AATAGA', 'CATAAA', 'ACTAAA')
ChimpConHumanNon=MetaAllSS_diffUsage %>% filter(ChimpPAS %in% con, HumanPAS %in% non) 
nrow(ChimpConHumanNon)
[1] 315
#number up in chim
ChimpConHumanNon %>% filter(ChimpUp=="yes") %>% nrow()
[1] 11
#number up in human  

ChimpConHumanNon %>% filter(HumanUp=="yes") %>% nrow()
[1] 23

Opposite direction:

ChimpNonHumanCon=MetaAllSS_diffUsage %>% filter(ChimpPAS %in% non, HumanPAS %in% con) 
nrow(ChimpNonHumanCon)
[1] 311
#number up in chim
ChimpNonHumanCon %>% filter(ChimpUp=="yes") %>% nrow()
[1] 25
#number up in human  

ChimpNonHumanCon %>% filter(HumanUp=="yes") %>% nrow()
[1] 5

This doesnt make a lot of sense.

Question 2: any signal site vs no signal site?

MetaAllSS_diffUsage_add=MetaAllSS_diffUsage %>% mutate(HumanNone=ifelse(ChimpPAS!="None" & HumanPAS=="None", "yes", "no"),ChimpNone=ifelse(ChimpPAS=="None" & HumanPAS!="None", "yes", "no"), EitherNonePatter=ifelse(ChimpNone=="yes" | HumanNone =="yes", "yes","no"))

Enrichment for either:

x=nrow(MetaAllSS_diffUsage_add %>% filter(EitherNonePatter=="yes", DiffUsed=="yes"))
m=nrow(MetaAllSS_diffUsage_add %>% filter(DiffUsed=="yes"))
n=nrow(MetaAllSS_diffUsage_add %>% filter(DiffUsed=="no"))
k=nrow(MetaAllSS_diffUsage_add %>% filter(EitherNonePatter=="no"))
N=nrow(MetaAllSS_diffUsage_add)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.01524209

Human dir

x=nrow(MetaAllSS_diffUsage_add %>% filter(ChimpNone=="yes", HumanUp=="yes"))
m=nrow(MetaAllSS_diffUsage_add %>% filter(HumanUp=="yes"))
n=nrow(MetaAllSS_diffUsage_add %>% filter(HumanUp=="no"))
k=nrow(MetaAllSS_diffUsage_add %>% filter(ChimpNone=="no"))
N=nrow(MetaAllSS_diffUsage_add)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.001879062

Chimp direction

x=nrow(MetaAllSS_diffUsage_add %>% filter(HumanNone=="yes", ChimpUp=="yes"))
m=nrow(MetaAllSS_diffUsage_add %>% filter(ChimpUp=="yes"))
n=nrow(MetaAllSS_diffUsage_add %>% filter(ChimpUp=="no"))
k=nrow(MetaAllSS_diffUsage_add %>% filter(HumanNone=="no"))
N=nrow(MetaAllSS_diffUsage_add)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.008602206

None of these are enriched

MetaPAS_diff= MetaPAS %>% mutate(DiffUsed=ifelse(PAS %in% DiffUsed_small$PAS, "yes","no"), HumanNoChimpYes=ifelse(HumanTopSS=="No" & ChimpTopSS=="Yes", "Yes","No"), HumanYesChimpNo=ifelse(HumanTopSS=="Yes" & ChimpTopSS=="No", "Yes","No"), EitherPattern=ifelse(HumanYesChimpNo=="Yes" | HumanNoChimpYes =="Yes", "Yes","No"))

Either pattern

x=nrow(MetaPAS_diff %>% filter(EitherPattern=="Yes", DiffUsed=="yes"))
m=nrow(MetaPAS_diff %>% filter(DiffUsed=="yes"))
n=nrow(MetaPAS_diff %>% filter(DiffUsed=="no"))
k=nrow(MetaPAS_diff %>% filter(EitherPattern=="No"))
N=nrow(MetaPAS_diff)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.02629644

No enrichment for any of the patterns


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       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.1.0       
[37] tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.26.1     compiler_3.5.1