Last updated: 2020-01-08

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/Nuclear_HvC.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd

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File Version Author Date Message
Rmd 203eae5 brimittleman 2020-01-08 first steps for intron loc analysis
html dec23c1 brimittleman 2020-01-06 Build site.
Rmd 54015c8 brimittleman 2020-01-06 add overlap dom with dAPA
html 0176b49 brimittleman 2019-12-30 Build site.
Rmd 5b25363 brimittleman 2019-12-30 add total and nuclear dominant PAS analysis

In this analysis I want to find the most dominant PAS for each gene in each species. I am interested in genes where the dominant PAS in human and chimp are intronic vs utr respectively. I will do this first in the nuclear fraction.

I will compare these genes with those identified in the differential APA analysis. This will be helpful to narrow down the genes I want to visualize.

library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

These are the PAS

ChimpPAS= read.table("../data/Pheno_5perc/Chimp_Pheno_5perc.txt", header = T) %>% dplyr::select(-contains("_T"))


HumanPAS= read.table("../data/Pheno_5perc/Human_Pheno_5perc.txt", header = T) %>% dplyr::select(-contains("_T"))

Prepare the mean vector:

ChimpMean=rowMeans(ChimpPAS[,9:ncol(ChimpPAS)])

ChimpPASwMean=cbind(ChimpPAS[,1:8],ChimpMean)


HumanMean=rowMeans(HumanPAS[,9:ncol(HumanPAS)])

HumanPASwMean=cbind(HumanPAS[,1:8],HumanMean)

Find the dominant PAS per gene:

I will remove genes with ties for now

Chimp_Dom= ChimpPASwMean %>%
  group_by(gene) %>%
  top_n(1,ChimpMean) %>% 
  mutate(nPer=n()) %>% 
  filter(nPer==1) %>% 
  dplyr::select(gene,loc,PAS,ChimpMean) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS)

Human_Dom= HumanPASwMean %>% 
  group_by(gene) %>% 
  top_n(1, HumanMean) %>% 
  mutate(nPer=n()) %>% 
  filter(nPer==1) %>% 
  dplyr::select(gene,loc,PAS,HumanMean) %>% 
  rename(HumanLoc=loc, HumanPAS=PAS)


#merge

BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")

Look at how many have the same dominat and where these are:

SameDom=BothDom %>% filter(ChimpPAS==HumanPAS,HumanLoc!="008559") 

ggplot(SameDom, aes(x=HumanLoc))+ geom_histogram(stat="count") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
0176b49 brimittleman 2019-12-30

Plot this as boxplots as well.

SameDom_gather=SameDom %>% dplyr::select(gene, HumanLoc, ChimpMean,HumanMean) %>% gather(species, value, -c(gene,HumanLoc))

ggplot(SameDom_gather,aes(x=HumanLoc, y=value,fill=species)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + labs(x="PAS Location", y="Mean Usage accross individuals", title="Mean usage for Genes with matching Dominant PAS")

Version Author Date
0176b49 brimittleman 2019-12-30

Write out the intronic for location analysis.

mkdir ../data/DominantPAS
SameDom_intron=SameDom %>% filter(ChimpLoc=="intron")
write.table(SameDom_intron, "../data/DominantPAS/SameDominantIntronic.txt", col.names = T, row.names = F, quote = F)

Different PAS but in the same location:

DiffDom_sameLoc=BothDom %>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc==HumanLoc) 

ggplot(DiffDom_sameLoc,aes(x=HumanLoc)) +  geom_histogram(stat="count") + labs(x="PAS Location", y= "Number of Genes", title="Dominat PAS in same genic location but different PAS")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Now I can look at those that are in different locations.

DiffDom_diffLoc=BothDom %>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc) 


ggplot(DiffDom_diffLoc,aes(x=ChimpLoc))+ geom_histogram(stat="count")+ labs(x="Chimp Dominant Location", y="Number of Genes", title="Location of dominant PAS when they are differnet between species")
Warning: Ignoring unknown parameters: binwidth, bins, pad

ggplot(DiffDom_diffLoc,aes(x=HumanLoc))+ geom_histogram(stat="count")+labs(x="Human Dominant Location", y="Number of Genes", title="Location of dominant PAS when they are differnet between species")
Warning: Ignoring unknown parameters: binwidth, bins, pad

This is interesting but could be based on the annotations. I will look at the genes with human in intron and chimp in 3’ UTR.

DiffDom_diffLoc_humanIntronChimpUTR= DiffDom_diffLoc %>% filter(ChimpLoc=="utr3", HumanLoc=="intron")

nrow(DiffDom_diffLoc_humanIntronChimpUTR)
[1] 926

Opposite Direction

DiffDom_diffLoc_humanUTRChimpInton= DiffDom_diffLoc %>% filter(ChimpLoc=="intron", HumanLoc=="utr3")
nrow(DiffDom_diffLoc_humanUTRChimpInton)
[1] 251
prop.test(x=c(nrow(DiffDom_diffLoc_humanIntronChimpUTR),nrow(DiffDom_diffLoc_humanUTRChimpInton)), n=c(nrow(DiffDom_diffLoc), nrow(DiffDom_diffLoc)))

    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(DiffDom_diffLoc_humanIntronChimpUTR), nrow(DiffDom_diffLoc_humanUTRChimpInton)) out of c(nrow(DiffDom_diffLoc), nrow(DiffDom_diffLoc))
X-squared = 485.91, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.2155473 0.2563156
sample estimates:
    prop 1     prop 2 
0.32366305 0.08773156

I will look to see if these genes are those I see with differential APA.

write.table(DiffDom_diffLoc_humanIntronChimpUTR, "../data/DominantPAS/Nuclear_HumanIntronicChimpUTR.txt", col.names = T, row.names = F, quote = F)
write.table(DiffDom_diffLoc_humanUTRChimpInton, "../data/DominantPAS/Nuclear_HumanUTRChimpIntronic.txt", col.names = T, row.names = F, quote = F)

write.table(SameDom, "../data/DominantPAS/Nuclear_SameDom.txt", col.names = T, row.names = F, quote = F)

How do i test if this number of genes is enriched??


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   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] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1