Last updated: 2019-10-02

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/annotationInfo.Rmd

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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 1c6c5e2 brimittleman 2019-10-02 add results for full set
html 8dd5eec brimittleman 2019-10-02 Build site.
Rmd 558afe6 brimittleman 2019-10-02 add liftover res
html b5edd8e brimittleman 2019-10-01 Build site.
Rmd b5a2151 brimittleman 2019-10-01 add analysis for liftover 
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()

Next step will be to prepare these for liftover. I will get the PAS as the furthest downstream base. I will liftover the regions 100bp upstream and 100bp downstream of the PAS.

As of now the PAS are still on the opposite strand.

Pos strand: pas is the end - start_new= end-100 - end_new=end + 100

neg strand: pas is the start - start_new= start-100 - end_new= start + 100

human: /project2/gilad/briana/Comparative_APA/Human/data/cleanPeaks/human_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed chimp: /project2/gilad/briana/Comparative_APA/Chimp/data/cleanPeaks/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed

Output:

mkdir ../data/cleanPeaks_byspecies/

python preparePAS4lift.py ../Human/data/cleanPeaks/human_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed ../data/cleanPeaks_byspecies/human_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed

python preparePAS4lift.py ../Chimp/data/cleanPeaks/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed ../data/cleanPeaks_byspecies/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed

Chain files from: http://hgdownload.soe.ucsc.edu/downloads.html#chimp dowload to ../data/chainFiles/

Liftover pipeline:

start with human- lift to chimp and back start with chimp- lift to human and back

mkdir ../data/primaryLift
mkdir ../data/reverseLift

sbatch primaryLift.sh

Results from primary lift:

unliftedH=read.table("../data/primaryLift/human_APApeaks_primarylift2Chimp_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()
unliftedC=read.table("../data/primaryLift/chimp_APApeaks_primarylift2Human_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()

liftedH=read.table("../data/primaryLift/human_APApeaks_primarylift2Chimp.bed",stringsAsFactors = F) %>% nrow()
liftedC=read.table("../data/primaryLift/chimp_APApeaks_primarylift2Human.bed",stringsAsFactors = F) %>% nrow()

primaryUnC=c("Chimp","Unlifted", unliftedC)
primaryUnH=c("Human","Unlifted", unliftedH)

primaryLH=c("Human","Lifted", liftedH)
primaryLC=c("Chimp","Lifted", liftedC)

header=c("species", "liftStat", "PAS")
primaryDF= as.data.frame(rbind(primaryLH,primaryLC, primaryUnH,primaryUnC)) 
colnames(primaryDF)=header


primaryDF$PAS=as.numeric(as.character(primaryDF$PAS)) 



primaryDF= primaryDF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)
ggplot(primaryDF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results")

Version Author Date
8dd5eec brimittleman 2019-10-02 
ggplot(primaryDF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results")

Version Author Date
8dd5eec brimittleman 2019-10-02

Reverse lift:

re_unliftedH=read.table("../data/reverseLift/human_APApeaks_primarylift2Human_rev2Human_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()
re_unliftedC=read.table("../data/reverseLift/chimp_APApeaks_primarylift2Human_rev2Chimp_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()

re_liftedH=read.table("../data/reverseLift/human_APApeaks_primarylift2Chimp_rev2Human.bed",stringsAsFactors = F) %>% nrow()
re_liftedC=read.table("../data/reverseLift/chimp_APApeaks_primarylift2Human_rev2Chimp.bed",stringsAsFactors = F) %>% nrow()

re_UnC=c("Chimp","Unlifted", re_unliftedC)
re_UnH=c("Human","Unlifted", re_unliftedH)

re_LH=c("Human","Lifted", re_liftedH)
re_LC=c("Chimp","Lifted", re_liftedC)

header=c("species", "liftStat", "PAS")
re_DF= as.data.frame(rbind(re_LH,re_LC, re_UnH,re_UnC)) 
colnames(re_DF)=header


re_DF$PAS=as.numeric(as.character(re_DF$PAS)) 



re_DF= re_DF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)
ggplot(re_DF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Reverse Liftover Results")

Version Author Date
8dd5eec brimittleman 2019-10-02 
ggplot(re_DF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2")+ labs(title="Reverse Liftover Results")

Version Author Date
8dd5eec brimittleman 2019-10-02

I need to now make sure they lifted to the same location. To do this I will overlap the reciprocal lifted PAS with the original files.

I subset the original files by the pas that have an exact match in the reverse map. I can do this by pas name- start:end

mkdir ../data/cleanPeaks_lifted
python filterPostLift.py ../data/cleanPeaks_byspecies/human_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed ../data/reverseLift/human_APApeaks_primarylift2Chimp_rev2Human.bed ../data/cleanPeaks_lifted/Human_PASregions.bed

python filterPostLift.py ../data/cleanPeaks_byspecies/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed  ../data/reverseLift/chimp_APApeaks_primarylift2Human_rev2Chimp.bed ../data/cleanPeaks_lifted/Chimp_PASregions.bed

Results:

Human_recLift=read.table("../data/cleanPeaks_lifted/Human_PASregions.bed",stringsAsFactors = F, col.names = c("chr", "start","end", "name", "score", "strand")) 
Chimp_recLift=read.table("../data/cleanPeaks_lifted/Chimp_PASregions.bed",stringsAsFactors = F,col.names = c("chr", "start","end", "name", "score", "strand"))

originalH=unliftedH + liftedH
originalC=unliftedC + liftedC

#human
nrow(Human_recLift)/originalH
[1] 0.9640866
#chimp 
nrow(Chimp_recLift)/originalC
[1] 0.9655327

96% reciprocally lifted over.

lift the chimp ones back to human

sbatch recLiftchim2human.sh

Join the results: If they are discovered in both say so.

Chimp_recLift_hcoord=read.table("../data/cleanPeaks_lifted/Chimp_PASregions_humanCoord.bed",stringsAsFactors = F,col.names = c("chr", "start","end", "name", "score", "strand"))

Inboth=Human_recLift %>% inner_join(Chimp_recLift_hcoord, by=c("chr", "start", "end")) %>% mutate(discover="Both", newName=paste(name.x,discover,sep=":"),meanscore=((score.x+score.y)/2)) %>% select(chr, start,end, newName, meanscore, strand.x) %>% dplyr::rename("score"=meanscore, "strand"=strand.x)

HumanOnly= Human_recLift %>% anti_join(Chimp_recLift_hcoord, by=c("chr", "start", "end"))  %>% mutate(discover="Human",newName=paste(name,discover,sep=":")) %>% select(chr, start,end, newName, score, strand)
ChimpOnly=Chimp_recLift_hcoord %>% anti_join(Human_recLift, by=c("chr", "start", "end")) %>% mutate(discover="Chimp",newName=paste(name,discover,sep=":")) %>% select(chr, start,end, newName, score, strand)

Graph this:

set=c("Both", "Human", "Chimp")
Number=c(nrow(Inboth), nrow(HumanOnly),nrow(ChimpOnly))
pasdiscnum=as.data.frame(cbind(set, Number))
pasdiscnum$Number=as.numeric(as.character(pasdiscnum$Number))

pasdiscnum= pasdiscnum %>% mutate(prop=Number/sum(Number))
ggplot(pasdiscnum, aes(x=set, y=Number, fill=set)) + geom_bar(stat="identity")+ scale_fill_brewer(palette = "Dark2")+ labs(y="Number of PAS", x="Discovery Set")

ggplot(pasdiscnum, aes(x=set, y=prop, fill=set)) + geom_bar(stat="identity")+ scale_fill_brewer(palette = "Dark2") + labs(y="Proportion of PAS", x="Discovery Set")

Are higher scores discovered in both?

AllPAS=rbind(Inboth,HumanOnly ,ChimpOnly) %>% separate(newName,into=c("name", "discover"))
ggplot(AllPAS, aes(x=discover, y=log10(score), fill=discover)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + labs(y="log10(Mean Read count)", title="PAS mean reads by Species of discovery")


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

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] pillar_1.3.1       compiler_3.5.1     git2r_0.25.2      
 [7] plyr_1.8.4         workflowr_1.4.0    tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       magrittr_1.5       whisker_0.3-2     
[40] backports_1.1.2    scales_1.0.0       htmltools_0.3.6   
[43] rvest_0.3.2        assertthat_0.2.0   colorspace_1.3-2  
[46] labeling_0.3       stringi_1.2.4      lazyeval_0.2.1    
[49] munsell_0.5.0      broom_0.5.1        crayon_1.3.4