Last updated: 2020-01-09

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Knit directory: Comparative_APA/analysis/

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Rmd 928a30d brimittleman 2020-01-09 add same and diff dom ss analysis

library(workflowr)
This is workflowr version 1.5.0
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library(tidyverse)
── Attaching packages ──────────────────────────────────────── tidyverse 1.2.1 ──
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I want to test if a difference in signal site distribution may explain the different dominant PAS. I am working with nuclear fraction results.

HumanIntronChimpUTR=read.table("../data/DominantPAS/Nuclear_HumanIntronicChimpUTR.txt",stringsAsFactors = F, header = T ) %>% rename("PAS"=HumanPAS)
SameDom=read.table("../data/DominantPAS/Nuclear_SameDom.txt", stringsAsFactors = F, header = T)
HumanUTRChimpIntron= read.table("../data/DominantPAS/Nuclear_HumanUTRChimpIntronic.txt", stringsAsFactors = F, header = T) %>% rename("PAS"=ChimpPAS)

Upload results for the PAS signal sites:

HumanSS=read.table("../data/SignalSites/HumanPresenceofSS.txt", col.names = c("PAS", "SS_human", "SS2_human", "anySS_human"), stringsAsFactors = F)
ChimpSS=read.table("../data/SignalSites/ChimpPresenceofSS.txt", stringsAsFactors = F,col.names = c("PAS", "SS_chimp", "SS2_chimp", "anySS_chimp"))

Join these:

I will also ask if human intronic PAS have a SS in human but not in chimp or vice versa.

HumanIntronChimpUTR_SS=HumanIntronChimpUTR %>% inner_join(HumanSS, by=c("PAS")) %>% inner_join(ChimpSS,by=c("PAS"))

SSinHumanOnly=HumanIntronChimpUTR_SS %>% filter(anySS_human=="yes", anySS_chimp=="no") 
nrow(SSinHumanOnly)
[1] 12
SSchimpOnly=HumanIntronChimpUTR_SS %>% filter(anySS_human=="no", anySS_chimp=="yes")
nrow(SSchimpOnly)
[1] 10
prop.test(x=c(12,10), n=c(926,926), alternative="greater")

    2-sample test for equality of proportions with continuity
    correction

data:  c(12, 10) out of c(926, 926)
X-squared = 0.046001, df = 1, p-value = 0.4151
alternative hypothesis: greater
95 percent confidence interval:
 -0.007201626  1.000000000
sample estimates:
    prop 1     prop 2 
0.01295896 0.01079914 

It does not look like this is the mechanism of action.

Opposite direction. I will ask if chimp intronic PAS have a SS in chimp but not in human or vice versa.

HumanUTRChimpIntron_SS=HumanUTRChimpIntron %>% inner_join(HumanSS, by=c("PAS")) %>% inner_join(ChimpSS,by=c("PAS"))

SSinHumanOnly_opp=HumanUTRChimpIntron_SS %>% filter(anySS_human=="yes", anySS_chimp=="no") 
nrow(SSinHumanOnly_opp)
[1] 2
SSchimpOnly_opp=HumanUTRChimpIntron_SS %>% filter(anySS_human=="no", anySS_chimp=="yes")
nrow(SSinHumanOnly_opp)
[1] 2

This does not look like the mechanism this way either.

** It may be interesting to look at the less frequently used signal sites. **

I want to ask if the same dominant PAS have signal sites. It may be interesting if these intronic PAS are more likely to have a signal site than all of the intronic PAS.

HumanSS_2=read.table("../data/SignalSites/HumanPresenceofSS.txt", col.names = c("HumanPAS", "SS_human", "SS2_human", "anySS_human"), stringsAsFactors = F)
ChimpSS_2=read.table("../data/SignalSites/ChimpPresenceofSS.txt", stringsAsFactors = F,col.names = c("ChimpPAS", "SS_chimp", "SS2_chimp", "anySS_chimp"))

SameDom_SS=SameDom %>% inner_join(HumanSS_2, by="HumanPAS") %>% inner_join(ChimpSS_2, by="ChimpPAS") %>% mutate(bothSS=ifelse(anySS_human=="yes" & anySS_chimp =="yes", "yes", "no"), onehasSS=ifelse(anySS_human=="yes" | anySS_chimp =="yes", "yes", "no"))

#most have in both  

Plot

SameDom_SS_byloc=SameDom_SS %>% group_by(HumanLoc,bothSS) %>% summarise(count=n()) %>% ungroup() %>% group_by(HumanLoc) %>% mutate(nGroup=sum(count), propSS=count/nGroup)

ggplot(SameDom_SS_byloc, aes(x=HumanLoc, y=count, by=bothSS, fill=bothSS)) +geom_bar(stat="identity",position="dodge")

ggplot(SameDom_SS_byloc, aes(x=HumanLoc, y=propSS, by=bothSS, fill=bothSS)) +geom_bar(stat="identity",position="dodge") 

Make the same plot with all of them. I need to pull in the metadata to do this because it has the locations.


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:
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[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   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.5.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4