Last updated: 2020-04-07

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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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.

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Rmd 81e9838 brimittleman 2020-04-07 add pol2 initial data

The human ChIPSeq data is deposited at this location (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE47991) while the chimp and rhesus data are deposited at this location (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60269). The bed files there contain peak calls, and are the result of the two-step filtering process described in the paper, to focus on only the high-confidence MACS peaks. The columns in the bed files correspond to MACS output; column headers are listed below: chr start end length summit tags -10*log10(pvalue) fold_enrichment FDR(%)

The deposited bed files on GEO are copied/renamed from the original files and the original file names are in the following format: H3K27ac_H_18522_111025_peaks.full.5.15.map.bed. All these original bed files are available at this location on the PPS cluster (/mnt/lustre/data/share/HCR_Chipseq/.Mapped_nb/H3K27ac/MACS/Full_Output/Datafreeze_20130122). All original data (e.g. fastq files) and all processing scripts used in the GB paper are available at /mnt/lustre/data/share/HCR_Chipseq/.

Downloading the human and chimp files:

My goal will be to get mean enrichemnt values for the peaks. I want the files with C_Pol_ and _____

mkdir ../data/Pol2Chip

I will have to lift these peaks to hg38. Chimp at pantro3. Human are HG19.

put pantro3tog38 chain file in ../data/chainFiles

Join the bedfiles

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()
polHead=c('chr','start' , 'end','length','summit','tags' , 'nedlog10p', 'Enrich',"FDR")
Pol2_18358=read.table("../data/Pol2Chip/GSM1468890_C_PolII_18358.bed", col.names = polHead, stringsAsFactors = F)
Pol2_18359=read.table("../data/Pol2Chip/GSM1468891_C_PolII_18359.bed", col.names = polHead,stringsAsFactors = F)
Pol2_3610=read.table("../data/Pol2Chip/GSM1468892_C_PolII_3610.bed", col.names = polHead,stringsAsFactors = F)
Pol2_3659=read.table("../data/Pol2Chip/GSM1468894_C_PolII_3659.bed", col.names = polHead,stringsAsFactors = F)
Pol2_3641=read.table("../data/Pol2Chip/GSM1468893_C_PolII_3641.bed", col.names = polHead,stringsAsFactors = F)
Pol2_4973=read.table("../data/Pol2Chip/GSM1468895_C_PolII_4973.bed", col.names = polHead,stringsAsFactors = F)
Pol2_pt30=read.table("../data/Pol2Chip/GSM1468896_C_PolII_Pt30.bed", col.names = polHead,stringsAsFactors = F)
Pol2_pt91=read.table("../data/Pol2Chip/GSM1468897_C_PolII_Pt91.bed", col.names = polHead,stringsAsFactors = F)

I will only consider high quality sites in all species.
See if I can do this with inner join.

Pol2_18359_small=Pol2_18359 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="18359")
Pol2_18358_small=Pol2_18358 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="18358")
Pol2_3610_small=Pol2_3610 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="3610")
Pol2_3659_small=Pol2_3659 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="3659")
Pol2_3641_small=Pol2_3641 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="3641")
Pol2_4973_small=Pol2_4973 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="4973")
Pol2_pt30_small=Pol2_pt30 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="pt30")
Pol2_pt91_small=Pol2_pt91 %>% select('chr','start' , 'end') %>% mutate(size=end-start, Ind="pt91")

AllChimp=Pol2_18359_small %>%
  bind_rows(Pol2_18358_small)%>%
  bind_rows(Pol2_18358_small)%>% 
  bind_rows(Pol2_3610_small)%>%
  bind_rows(Pol2_3659_small)%>%
  bind_rows(Pol2_3641_small)%>% 
  bind_rows(Pol2_4973_small)%>% 
  bind_rows(Pol2_pt30_small)%>% 
  bind_rows(Pol2_pt91_small)

This doesnt work….

test=Pol2_18359_small %>% inner_join(Pol2_18358_small,by = c("chr", "start", "end"))

I will have to intersect and get regions.

bedtools multiinter -header -i ../data/Pol2Chip/GSM*_C_PolII* > ../data/Pol2Chip/IntersectionChimpPol2.txt 
ChimpInter=read.table("../data/Pol2Chip/IntersectionChimpPol2.txt",header = T,stringsAsFactors = F)

Plot how many PAS are in overlaps 1-7

Individaul=seq(1,7)
Peaks=c()
size=c()
sizesd=c()
for (i in Individaul){
  df=ChimpInter %>% filter(num==i) %>% mutate(size=end-start)
  val= df %>% nrow()
  sizei=mean(df$size)
  sdsize=sd(df$size)
  Peaks=c(Peaks, val)
  size=c(size, sizei)
  sizesd=c(sizesd, sdsize)
}

ChimpDF=as.data.frame(cbind(Individaul,Peaks,size,sizesd))
ChimpDF$Individaul=as.factor(ChimpDF$Individaul)


ggplot(ChimpDF, aes(x=Individaul,y=Peaks,fill=Individaul)) + geom_bar(stat="identity") + scale_fill_brewer(palette = "Dark2") + labs(x="Number of Chimps",y="PolII Peaks",title="Chimp PolII interlap") + theme(legend.position = "none")

ggplot(ChimpDF, aes(x=Individaul,y=size,fill=Individaul)) + geom_bar(stat="identity")+ scale_fill_brewer(palette = "Dark2") +geom_errorbar(aes(ymin=size-sizesd, ymax=size+sizesd),width=.2,position=position_dodge(.9)) + labs(x="Number of Chimps",y="Mean Peak Size",title="Chimp PolII peak size") + theme(legend.position = "none")

Compare this to original:

AllChimp_sum= AllChimp %>% group_by(Ind ) %>% summarise(mean=mean(size), sd=sd(size))
ggplot(AllChimp,aes(x=Ind,y=size,fill=Ind)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2")

ggplot(AllChimp_sum, aes(x=Ind,y=mean,fill=Ind)) + geom_bar(stat="identity")+ scale_fill_brewer(palette = "Dark2") +geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd),width=.2,position=position_dodge(.9)) + labs(x="Chimps",y="Mean Peak Size",title="Chimp PolII peak size") + theme(legend.position = "none")

Is this even what I want to do. I want the differentially used PAS between species. I am interested to know if these are in genes with dAPA.


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_1.6.0    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