Last updated: 2018-07-16

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    Rmd 4fede84 Briana Mittleman 2018-07-16 add eval brain analysis

I downloaded the brain 3’ seq data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM747470 and I want to use this analysis to see how similar their peaks are to ours eventhough the data is from different cell types.

First I will use the bedtools jaccard function to explore the overlaps. It will give me one stat that is the length(intersection)/length(union) - length(intersection). Here I can have file A brain peaks and file B be our peaks to see the similarity between the sets.

#!/bin/bash

#SBATCH --job-name=jaccard_brain
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=jacard_brain.out
#SBATCH --error=jacard_brain.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END



module load Anaconda3
source activate three-prime-env 

bedtools jaccard -a /project2/gilad/briana/threeprimeseq/data/derti_brain/GSM747470_human_brain.sites.clustered.hg19.sort.bed -b /project2/gilad/briana/threeprimeseq/data/peaks/APApeaksYL.total.bed  > /project2/gilad/briana/threeprimeseq/data/derti_brain/total.jaccard.txt

Results: intersection union-intersection jaccard n_intersections 21371 25414133 0.00084091 21352

The brain set has 89110 peaks and our set has 288350. I will filter ours by score then see if the top 25% have a higher overlap percentage.

library(workflowr)
Loading required package: rmarkdown
This is workflowr version 1.0.1
Run ?workflowr for help getting started
library(dplyr)
Warning: package 'dplyr' was built under R version 3.4.4

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(reshape2)
Warning: package 'reshape2' was built under R version 3.4.3

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
YL_peaks=read.table("../data/bedgraph_peaks/APApeaks.bed", col.names = c("chr", "start", "end", "count", "strand", "score")) %>% mutate(length=end-start)
Warning: package 'bindrcpp' was built under R version 3.4.4

I want the counts for the top 25% of the peaks.

quantile(YL_peaks$count)
          0%          25%          50%          75%         100% 
1.000000e+00 1.343902e+01 2.353933e+01 6.091061e+01 1.604636e+06

I will subset the peaks by having a count > 61.

awk '$4 >= 60 {print}' APApeaksYL.total.bed > APApeaksYL.top25.total.bed

I can rerun the jaccard with this and see if it changes, this new file has 72877 peaks.

Results:
intersection union-intersection jaccard n_intersections 13221 6452066 0.00204911 13207

The proportion of overlap increased. Next I can try to make plots where I seperate my peaks by if they have a corresponding one in the brain file then plot the scores. To do this I will first use bedtool intersect to get just my peaks that contain a peak in the brain file. I can then use dplyr to merge them.

Here A is my file and B is the brain file.

#!/bin/bash

#SBATCH --job-name=int_brain
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=int.brain.out
#SBATCH --error=int.brain.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env 

bedtools intersect -wa -a /project2/gilad/briana/threeprimeseq/data/peaks/APApeaksYL.total.bed  -b /project2/gilad/briana/threeprimeseq/data/derti_brain/GSM747470_human_brain.sites.clustered.hg19.sort.bed > /project2/gilad/briana/threeprimeseq/data/derti_brain/APApeaksYL.total.inbrain.bed

The resulting file has 21378 peaks.

YL_peaks_overlap=read.table("../data/APApeaksYL.total.inbrain.bed", col.names = c("chr", "start", "end", "count", "strand", "score")) %>% mutate(length=end-start) %>% mutate(in_brain="Y")

Now I need to join them.

YL_peaks_join=YL_peaks %>% full_join(YL_peaks_overlap, by = c("chr", "start", "end", "count", "strand", "score", "length"))

YL_peaks_join$in_brain[is.na(YL_peaks_join$in_brain)]="N"

YL_peaks_join_sel=YL_peaks_join %>% select(count, in_brain)

Plot these.

ggplot(YL_peaks_join_sel, aes(y=log10(count), x=in_brain, fill=in_brain)) + geom_boxplot() + labs(x="Peak called in brain dataset", y="log10 Score", title="Peak score distribution by inclusion in brain dataset")

ggplot(YL_peaks_join_sel, aes(x=log10(count), fill=in_brain), bins=50) + geom_density(position="identity", alpha=.5) + labs(x="log10 of Score", title="Distribution of log10 Scores in peaks included in brain dataset")

It would be better if the background was just a random subset of the same number. There are 21378 included peaks so I should select a random 21378 to make a background distribution.

samp_YLpeaks= sample_n(YL_peaks, 21378)

ggplot() + geom_histogram(data=samp_YLpeaks, aes(log10(count)), bins=100) + geom_histogram(data=YL_peaks_overlap, aes(log10(count)),fill="Red", bins=100) + labs(x="Log10 of Score", title="Scores in Overlapping set compared to scores in random set")

Session information

sessionInfo()
R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] bindrcpp_0.2.2  reshape2_1.4.3  ggplot2_2.2.1   tidyr_0.7.2    
[5] dplyr_0.7.5     workflowr_1.0.1 rmarkdown_1.8.5

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17      compiler_3.4.2    pillar_1.1.0     
 [4] git2r_0.21.0      plyr_1.8.4        bindr_0.1.1      
 [7] R.methodsS3_1.7.1 R.utils_2.6.0     tools_3.4.2      
[10] digest_0.6.15     evaluate_0.10.1   tibble_1.4.2     
[13] gtable_0.2.0      pkgconfig_2.0.1   rlang_0.2.1      
[16] yaml_2.1.19       stringr_1.3.1     knitr_1.18       
[19] rprojroot_1.3-2   grid_3.4.2        tidyselect_0.2.4 
[22] glue_1.2.0        R6_2.2.2          purrr_0.2.5      
[25] magrittr_1.5      whisker_0.3-2     backports_1.1.2  
[28] scales_0.5.0      htmltools_0.3.6   assertthat_0.2.0 
[31] colorspace_1.3-2  labeling_0.3      stringi_1.2.2    
[34] lazyeval_0.2.1    munsell_0.4.3     R.oo_1.22.0

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