Can signal site explain differential usage

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

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	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

Can signal site explain differential usage

Briana Mittleman

1/23/2020