Last updated: 2019-12-17

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PASnumperSpecies.Rmd
    Modified:   analysis/annotatePAS.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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 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 ──
✔ 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
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
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] 0.963198
#chimp 
nrow(Chimp_recLift)/originalC
[1] 0.9675151

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)

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

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")

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