Can signal site explain differential usage

Last updated: 2020-05-05

Checks: 7 0

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

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

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)

── 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()

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

[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

[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

[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

[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

Can signal site explain differential usage

Briana Mittleman

1/23/2020