Last updated: 2020-01-25

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/speciesSpecific_DF.Rmd

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File Version Author Date Message
Rmd e26cfd1 brimittleman 2020-01-26 fix plot for CM
html aee923e brimittleman 2019-12-17 Build site.
Rmd 83fb66e brimittleman 2019-12-17 update liftover PAS
html 3da520f brimittleman 2019-10-04 Build site.
Rmd 00c308a brimittleman 2019-10-04 fix overlap
html e0ac227 brimittleman 2019-10-03 Build site.
Rmd e3f0cdf brimittleman 2019-10-03 add annotation analysis
html 5fbf02b brimittleman 2019-10-02 Build site.
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 2019-10-02 Build site.
Rmd b5a2151 brimittleman 2019-10-02 add analysis for liftover 
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── 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
sbatch reverseLift.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
aee923e brimittleman 2019-12-17
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
aee923e brimittleman 2019-12-17
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
aee923e brimittleman 2019-12-17
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
aee923e brimittleman 2019-12-17
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] 0.963198
#chimp 
nrow(Chimp_recLift)/originalC
[1] 0.9675151

Reciprocal lift:

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.

I need to intersect these with bedtools to know when a PAS is

sort -k1,1 -k2,2n ../data/cleanPeaks_lifted/Chimp_PASregions_humanCoord.bed > ../data/cleanPeaks_lifted/Chimp_PASregions_humanCoord.sort.bed


sort -k1,1 -k2,2n ../data/cleanPeaks_lifted/Human_PASregions.bed > ../data/cleanPeaks_lifted/Human_PASregions.sort.bed

I used 75% overlap for the observed in both. I had it report number of basepair overlap so i can evaluate this. If it is bimodal i will change this. (i chose 75% because its 50bp or within 1 read.)

 sbatch intersectLiftedPAS.sh
OverlapBoth_Test=read.table("../data/cleanPeaks_lifted/PASregions_identifiedbothTEST.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "Hscore", "Hstrand", "Cchr", "Cstart", "Cend", "Cname", "Cscore", "Cstrand", "overlap"),stringsAsFactors = F)

ggplot(OverlapBoth_Test, aes(x=overlap)) + geom_histogram(bins=30) + scale_y_log10() + geom_vline(xintercept = 125) + labs(title="Test for how many basepairs of 200 overlap", y="Number of Sites", x="Number of Overlaped bases")

Version Author Date
aee923e brimittleman 2019-12-17 
125/200
[1] 0.625

I will go with 62.5% overlap. I can prepare the files to make a full set.

OverlapBoth=read.table("../data/cleanPeaks_lifted/PASregions_identifiedboth.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "Hscore", "Hstrand", "Cchr", "Cstart", "Cend", "Cname", "Cscore", "Cstrand", "overlap"),stringsAsFactors = F) %>% mutate(meanScore=(Hscore+Cscore)/2, name=paste("Both", Hname, sep=":"), Bothname=paste(Hname, Cname, sep=":"))

overlap2= OverlapBoth %>% group_by(name) %>% filter(n()>1) %>%  mutate(id = row_number()) %>% filter(id==2)

OverlapBoth_format=read.table("../data/cleanPeaks_lifted/PASregions_identifiedboth.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "Hscore", "Hstrand", "Cchr", "Cstart", "Cend", "Cname", "Cscore", "Cstrand", "overlap"),stringsAsFactors = F) %>% mutate(meanScore=(Hscore+Cscore)/2, name=paste("Both", Hname, sep=":"),Bothname=paste(Hname, Cname, sep=":")) %>% filter(!Bothname %in% overlap2$Bothname) %>% dplyr::select(Hchr,Hstart,Hend,name,meanScore,Hstrand) 



HumanSpec=read.table("../data/cleanPeaks_lifted/PASregions_identifiedHuman.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "meanScore", 'Hstrand'),stringsAsFactors = F) %>% mutate(name=paste("Human", Hname, sep=":")) %>% dplyr::select(Hchr, Hstart,Hend, name, meanScore, Hstrand) 


ChimpSpec=read.table("../data/cleanPeaks_lifted/PASregions_identifiedChimp.txt", col.names = c("Hchr", "Hstart","Hend", "Cname", "meanScore", 'Hstrand'),stringsAsFactors = F) %>% mutate(name=paste("Chimp", Cname, sep=":")) %>% dplyr::select(Hchr, Hstart,Hend, name, meanScore, Hstrand)

Join all of these and plot characteristics

AllPAS=as.data.frame(rbind(OverlapBoth_format,HumanSpec,ChimpSpec)) %>% separate(name, into=c("discovery", "PAS"))


ggplot(AllPAS, aes(x=discovery, fill=discovery)) + geom_bar(stat="count")+ scale_fill_brewer(palette = "Dark2")

Version Author Date
aee923e brimittleman 2019-12-17
3da520f brimittleman 2019-10-04 
ggplot(AllPAS, aes(x=discovery, fill=discovery)) +  geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2")

Version Author Date
aee923e brimittleman 2019-12-17

Plot the difference in scores for both and discovered in 1 or other:

ggplot(AllPAS, aes(x=discovery, fill=discovery, y=meanScore)) + geom_boxplot() + scale_y_log10()+ scale_fill_brewer(palette = "Dark2")

Version Author Date
aee923e brimittleman 2019-12-17

This is expected. Those found in both will be used more often. I expect many of those only discovered in 1 will drop out at the 5% cutoff.

AllPAS_use=as.data.frame(rbind(OverlapBoth_format,HumanSpec,ChimpSpec))

write.table(AllPAS_use,  "../data/cleanPeaks_lifted/AllPAS_postLift.bed", col.names = F, row.names = F, quote = F, sep = "\t")

sort these:

sort -k1,1 -k2,2n ../data/cleanPeaks_lifted/AllPAS_postLift.bed > ../data/cleanPeaks_lifted/AllPAS_postLift.sort.bed

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] 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] workflowr_1.5.0    cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.2        
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[46] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[49] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1