Last updated: 2020-04-03

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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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File Version Author Date Message
Rmd fa2203f brimittleman 2020-04-03 add res from merge
html 4722051 brimittleman 2020-04-02 Build site.
Rmd 9ba1428 brimittleman 2020-04-02 add start of merge analysis 
library(scales)
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

Exploration

I noticed there are overlapping PAS within species. I deal with overlapping PAS between species by merging the PAS into one when 125 of the bases overlap.

For the within species I will look at a merge on the PAS that pass liftover.

../data/cleanPeaks_lifted/Chimp_PASregions_humanCoord.sort.bed ../data/cleanPeaks_lifted/Chimp_PASregions.bed ../data/cleanPeaks_lifted/Human_PASregions.sort.bed

I will test the human coordinates and the chimp coordinates for the chimp pas.

I will use bedtools merge

mkdir ../data/TestWithinMergePAS
bedtools merge -s -c 4,4 -o distinct,count -i ../data/cleanPeaks_lifted/Chimp_PASregions_humanCoord.sort.bed > ../data/TestWithinMergePAS/Chimp_PASregions_humanCoord.merge.bed

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

bedtools merge -s -c 4,4 -o distinct,count  -i ../data/cleanPeaks_lifted/Chimp_PASregions.sort.bed > ../data/TestWithinMergePAS/Chimp_PASregions_chimpCoord.merge.bed

bedtools merge -s -c 4,4 -o distinct,count  -i ../data/cleanPeaks_lifted/Human_PASregions.sort.bed > ../data/TestWithinMergePAS/Human_PASregions.merge.bed

Chimp

Compare 2 chimp conditions:

ChimpHumanCoord=read.table("../data/TestWithinMergePAS/Chimp_PASregions_humanCoord.merge.bed",col.names = c("chr",'start','end','PAS', 'nPAS'),stringsAsFactors = F)


ChimpPAS=read.table("../data/TestWithinMergePAS/Chimp_PASregions_chimpCoord.merge.bed",col.names = c("chr",'start','end','PAS', 'nPAS'),stringsAsFactors = F)
ChimpHumanCoord %>% group_by(nPAS) %>% summarise(n())
# A tibble: 7 x 2
   nPAS  `n()`
  <int>  <int>
1     1 250102
2     2  18158
3     3   1937
4     4    236
5     5     30
6     6      9
7     7      3
ggplot(ChimpHumanCoord,aes(x=nPAS)) + geom_bar(stat="count")

Version Author Date
4722051 brimittleman 2020-04-02 
ChimpPAS %>% group_by(nPAS) %>% summarise(n())
# A tibble: 7 x 2
   nPAS  `n()`
  <int>  <int>
1     1 250102
2     2  18158
3     3   1937
4     4    236
5     5     30
6     6      9
7     7      3
ggplot(ChimpPAS,aes(x=nPAS)) + geom_bar(stat="count")

Version Author Date
4722051 brimittleman 2020-04-02

Numbers are the same. Hopfully they are the same overlaps.

length(intersect(ChimpHumanCoord$PAS,ChimpPAS$PAS))
[1] 270475

Ok these are the same. I can do it all in human coordinate.

Explore the size of these bins.

ChimpHumanCoord_details= ChimpHumanCoord %>% mutate(size=end-start)
ChimpHumanCoord_details$nPAS=as.factor(ChimpHumanCoord_details$nPAS)
hue_pal()(7)
[1] "#F8766D" "#C49A00" "#53B400" "#00C094" "#00B6EB" "#A58AFF" "#FB61D7"
chimpsize=ggplot(ChimpHumanCoord_details, aes(x=nPAS, y=size,fill=nPAS))+ geom_boxplot() + geom_hline(yintercept = 200,col="#F8766D")+  geom_hline(yintercept = 400,col="#C49A00") + geom_hline(yintercept = 600,col="#53B400") + geom_hline(yintercept = 800,col="#00C094") + geom_hline(yintercept = 1000,col="#00B6EB") + geom_hline(yintercept = 1200,col="#A58AFF")+ geom_hline(yintercept = 1400,col="#FB61D7")+ labs(title="Size of chimp overlap PAS, colored by expected if overlap = 1 base")

Look at some of the top examples in the data. Is there evidence that high level groups should be filtered out? I can also decide whether I take the middle 200 or use the full region.

ChimpHumanCoord_details %>% arrange(desc(nPAS)) %>% head()
    chr     start       end
1  chr1 113855646 113856861
2 chr10  48902937  48904077
3  chr2 121765374 121766466
4  chr1  86743781  86744738
5  chr1 116524622 116525562
6 chr12    402215    403226
                                                                                  PAS
1        chimp15807,chimp15808,chimp15809,chimp15810,chimp15811,chimp15812,chimp15813
2        chimp37068,chimp37069,chimp37070,chimp37071,chimp37072,chimp37073,chimp37074
3 chimp186777,chimp186778,chimp186779,chimp186780,chimp186781,chimp186782,chimp186783
4                   chimp12419,chimp12420,chimp12421,chimp12422,chimp12423,chimp12424
5                   chimp16131,chimp16132,chimp16133,chimp16134,chimp16135,chimp16136
6                   chimp63365,chimp63366,chimp63367,chimp63368,chimp63369,chimp63370
  nPAS size
1    7 1215
2    7 1140
3    7 1092
4    6  957
5    6  940
6    6 1011

This is before I do any combination with the human PAS.

Human

HumanPAS=read.table("../data/TestWithinMergePAS/Human_PASregions.merge.bed",col.names = c("chr",'start','end','PAS', 'nPAS'),stringsAsFactors = F)
HumanPAS %>% group_by(nPAS) %>% summarise(n())
# A tibble: 7 x 2
   nPAS  `n()`
  <int>  <int>
1     1 260836
2     2  20167
3     3   2189
4     4    288
5     5     42
6     6      6
7     7      2
ggplot(HumanPAS,aes(x=nPAS)) + geom_bar(stat="count")

There are more overlaps in the human set.

HumanPAS_details= HumanPAS %>% mutate(size=end-start)
HumanPAS_details$nPAS=as.factor(HumanPAS_details$nPAS)


humansize=ggplot(HumanPAS_details, aes(x=nPAS, y=size,fill=nPAS))+ geom_boxplot() + geom_hline(yintercept = 200,col="#F8766D")+  geom_hline(yintercept = 400,col="#C49A00") + geom_hline(yintercept = 600,col="#53B400") + geom_hline(yintercept = 800,col="#00C094") + geom_hline(yintercept = 1000,col="#00B6EB") + geom_hline(yintercept = 1200,col="#A58AFF")+ geom_hline(yintercept = 1400,col="#FB61D7")+ labs(title="Size of human overlap PAS, colored by expected if overlap = 1 base")
plot_grid(chimpsize,humansize)

This shows that the human overlaps at the higher numbers are closer together.

HumanPAS_details %>% arrange(desc(nPAS)) %>% head()
    chr     start       end
1  chr3  25745548  25746665
2  chr4  25250500  25251656
3 chr10  48902772  48903819
4  chr2  88614337  88615211
5  chr2 121765478 121766460
6  chr2 159805437 159806359
                                                                                  PAS
1 human215546,human215547,human215548,human215549,human215550,human215551,human215552
2 human240331,human240332,human240333,human240334,human240335,human240336,human240337
3                   human39096,human39097,human39098,human39099,human39100,human39101
4             human174806,human174807,human174808,human174809,human174810,human174811
5             human178226,human178227,human178228,human178229,human178230,human178231
6             human182346,human182347,human182348,human182349,human182350,human182351
  nPAS size
1    7 1117
2    7 1156
3    6 1047
4    6  874
5    6  982
6    6  922

Passing filters

Focus on examples that make it in the final set.

overlapres=read.table("../data/OverlappingPAS/MergedPAS_HumanCords.txt", col.names = c("chr", "start", "end", "name", "numb")) %>% mutate(length=end-start) 

overlapres %>% arrange(desc(numb)) %>% head()
    chr     start       end
1  chr1  26691964  26692567
2 chr19  45408963  45409714
3  chr2 143172078 143172946
4  chr3 112565734 112566493
5  chr8  81701986  81702782
6  chr1  89184993  89185569
                                                         name numb length
1           chimp3956,chimp3958,human4143,human4144,human4145    5    603
2 chimp152730,human159918,human159919,human159920,human159922    5    751
3 human180107,human180108,human180109,human180110,human180111    5    868
4 chimp213715,chimp213717,human224753,human224754,human224755    5    759
5 chimp308243,human326513,human326514,human326515,human326516    5    796
6                 chimp12728,chimp12729,human13580,human13581    4    576

After looking at examples, it does not seem like merging is the right way to handle this. It would lead to inflatted numbers in regions and I do not want that. I will leave this for now.


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] cowplot_0.9.4   forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.6.0 scales_1.0.0   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1