Last updated: 2020-04-03
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Knit directory: Comparative_APA/analysis/
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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)
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library(tidyverse)
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library(cowplot)
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ggsave
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
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.
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
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