Last updated: 2020-01-14

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/OppositeMap.Rmd
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    Modified:   analysis/multiMap.Rmd

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Rmd 203eae5 brimittleman 2020-01-08 first steps for intron loc analysis 
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

The goal of this analysis is to look at the distribution of intronic location for the shared and not shared dominant PAS. This will help me know if the pattern is due to annotation or not. In this analysis I will use the nuclear results.

The first step is assigning each PAS to the intron it comes from.

HumanIntronicChimpUTR=read.table("../data/DominantPAS/Nuclear_HumanIntronicChimpUTR.txt",header = T, stringsAsFactors = F)%>% dplyr::select(gene, HumanPAS, HumanMean)
SameDomIntron=read.table("../data/DominantPAS/SameDominantIntronic.txt",header = T, stringsAsFactors = F)%>% dplyr::select(gene, HumanPAS,HumanMean)
HumanPAS= read.table("../data/Pheno_5perc/Human_Pheno_5perc.txt", header = T,stringsAsFactors = F) %>% dplyr::select(chr, start, end, gene, strand, PAS) %>% rename("HumanPAS"=PAS)

Subset this file by those in the set and select it as a bed file for overlap with intron file. I will use the human mean as the score for now.

HumanPAS_samedom=HumanPAS %>% inner_join(SameDomIntron, by="HumanPAS") %>% mutate(GenePAS=paste(gene.x, HumanPAS, sep="_")) %>% dplyr::select(chr, start, end, GenePAS, HumanMean,strand)

HumanPAS_diffDom=HumanPAS %>% inner_join(HumanIntronicChimpUTR, by="HumanPAS") %>% mutate(GenePAS=paste(gene.x, HumanPAS, sep="_")) %>% dplyr::select(chr, start, end, GenePAS, HumanMean,strand)

I can write these out as bed files.

write.table(HumanPAS_samedom, "../data/DominantPAS/SameDominantPAS_intronic.bed", quote = F, row.names = F, col.names = F,sep = "\t")
write.table(HumanPAS_diffDom, "../data/DominantPAS/DifferentDominantPAS_intronic.bed", quote = F, row.names = F, col.names = F,sep = "\t")

I can use bedtools intersect to find the intron these are in.

bedtools intersect -s -sorted -loj -a (PAS file) -b intron file > output

sbatch FindIntronForDomPAS.sh

There are places where multiple transcripts with the intron included. this means the same PAS shows up multiple times ( i can look for uniq intron locations to take care of this)

SameDomIntron=read.table("../data/DominantPAS/SameDominantPAS_intronic_mapped2Intron.txt", stringsAsFactors = F, col.names = c("PASchr", "PASstart", "PASend", "PASname", "PASusage", "PASstrand", "Intronchr", "IntronStart", "IntronEnd", "IntronName", "IntronScore", "IntronStrand"))
#group by intronname and keep top intron

SameDomIntronOne=SameDomIntron %>% group_by(PASname) %>% slice(1) %>% ungroup()



DiffDomIntron=read.table("../data/DominantPAS/DifferentDominantPAS_intronic_mapped2Intron.txt", stringsAsFactors = F, col.names = c("PASchr", "PASstart", "PASend", "PASname", "PASusage", "PASstrand", "Intronchr", "IntronStart", "IntronEnd", "IntronName", "IntronScore", "IntronStrand"))
DiffDomIntronOne=DiffDomIntron %>% group_by(PASname) %>% slice(1) %>% ungroup()

Now I need to find the midpoint for the PAS and get the percent distance to the start of the intron. I will use the intron strand because this is the genomic strand.

SameDomIntronOne_dist=SameDomIntronOne %>% mutate(centerPAS=PASstart +100, intronLength=IntronEnd-IntronStart, distance2PAS=ifelse(IntronStrand=="+", centerPAS -IntronStart, IntronEnd-centerPAS),propIntron=distance2PAS/intronLength)

DiffDomIntronOne_dist=DiffDomIntronOne %>% mutate(centerPAS=PASstart +100, intronLength=IntronEnd-IntronStart, distance2PAS=ifelse(IntronStrand=="+", centerPAS -IntronStart, IntronEnd-centerPAS),propIntron=distance2PAS/intronLength)

Plot both distributions, do percentage and absolute distance

ggplot(SameDomIntronOne_dist, aes(x=distance2PAS)) + geom_histogram(bins=100)  + labs(x="Distance from intron start to PAS", title="Same Dominant Intron, absolute distance")

Version Author Date
f34d597 brimittleman 2020-01-09 
ggplot(SameDomIntronOne_dist, aes(x=propIntron)) + geom_histogram(bins=100) +labs(x="Proportion of intron", title="Same Dominant Intron, proportion of intron")

Version Author Date
f34d597 brimittleman 2020-01-09 
SameDomIntronOne_dist_filt= SameDomIntronOne_dist %>% filter(distance2PAS<=100000)

ggplot(SameDomIntronOne_dist_filt, aes(x=distance2PAS)) + geom_histogram(bins=100)   +labs(x="Distance from intron start to PAS", title="Same Dominant Intron, absolute distance (less than 100kb)")

Version Author Date
f34d597 brimittleman 2020-01-09 
ggplot(DiffDomIntronOne_dist, aes(x=distance2PAS)) + geom_histogram(bins=100)+ labs(x="Distance from intron start to PAS", title="Human Dominant Intronic, Chimp Dominant 3' UTR, absolute distance")

Version Author Date
f34d597 brimittleman 2020-01-09 
ggplot(DiffDomIntronOne_dist, aes(x=propIntron)) + geom_histogram(bins=100) +labs(x="Proportion of intron", title="Human Dominant Intronic, Chimp Dominant 3' UTR, Proportion of intron")

Version Author Date
f34d597 brimittleman 2020-01-09 
DiffDomIntronOne_dist_filt= DiffDomIntronOne_dist %>% filter(distance2PAS<=100000)

ggplot(DiffDomIntronOne_dist_filt, aes(x=distance2PAS)) + geom_histogram(bins=100) +  labs(x="Distance from intron start to PAS", title="Human Dominant Intronic, Chimp Dominant 3' UTR \nabsolute distance (less than 100kb)")

Version Author Date
f34d597 brimittleman 2020-01-09

Distributions look pretty similar.

ggplot(DiffDomIntronOne_dist, aes(x=propIntron)) +stat_ecdf(geom = "step", col="red") +stat_ecdf(data=SameDomIntronOne_dist, geom = "step", col="blue") + scale_colour_manual(name = 'Intron Set', values =c('red'='red','blue'='blue'), labels = c('c2','c1'),guide = 'legend')+ labs(x="PAS location by proprortion of Intron", title = "ecdf for Intronic location, red=Different Dom, blue=Same dom")

Version Author Date
4c056b5 brimittleman 2020-01-09 
wilcox.test(DiffDomIntronOne_dist$propIntron, SameDomIntronOne_dist$propIntron)

    Wilcoxon rank sum test with continuity correction

data:  DiffDomIntronOne_dist$propIntron and SameDomIntronOne_dist$propIntron
W = 949110, p-value = 0.04153
alternative hypothesis: true location shift is not equal to 0
wilcox.test(DiffDomIntronOne_dist$distance2PAS, SameDomIntronOne_dist$distance2PAS)

    Wilcoxon rank sum test with continuity correction

data:  DiffDomIntronOne_dist$distance2PAS and SameDomIntronOne_dist$distance2PAS
W = 887280, p-value = 0.3558
alternative hypothesis: true location shift is not equal to 0

Not a significant difference in these distributions.


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] 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