Compare Nuclear Fraction PAS

Last updated: 2019-10-09

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/Human_TvN.Rmd
    Modified:   analysis/annotatePAS.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd

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File	Version	Author	Date	Message
Rmd	14a3f66	brimittleman	2019-10-09	add pca and human v chimp in nuc analysis

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()

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

Compare nuclear fraction PAS between human and chimp. I need to merge the 5% phenotypes from the human and chimp. I need a fc file with the human and chimp nuclear samples. I will make a group file with the identifier being human or chimp.

../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc ../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc

mkdir ../data/NuclearHvC

human=read.table("../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc", stringsAsFactors = F, header = T) %>% rownames_to_column(var="chrom")
chimp=read.table("../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc", stringsAsFactors = F, header = T)%>% rownames_to_column(var="chrom")

Allsamps=human %>% full_join(chimp,by="chrom") 

AllNuclear=Allsamps %>% select(chrom,contains("_N")) %>% column_to_rownames(var="chrom")

write.table(AllNuclear, "../data/NuclearHvC/ALLPAS_postLift_LocParsed_HvC_Nuclear_fixed4LC.fc",row.names = T, col.names = T, quote = F)

I will make the id file here.

Inds=colnames(AllNuclear) 
Species=c(rep("Human",6), rep("Chimp", 6))

idFileDF=as.data.frame(cbind(Inds,Species))

write.table(idFileDF, "../data/NuclearHvC/sample_goups.txt",row.names = F, col.names = F, quote = F)

Split by chromosome.

mkdir ../data/DiffIso_Nuclear/

python subset_diffisopheno_Nuclear_HvC.py 1
python subset_diffisopheno_Chimp_tvN.py 2
python subset_diffisopheno_Chimp_tvN.py 3
python subset_diffisopheno_Chimp_tvN.py 4
python subset_diffisopheno_Chimp_tvN.py 5
python subset_diffisopheno_Chimp_tvN.py 6
python subset_diffisopheno_Chimp_tvN.py 7
python subset_diffisopheno_Chimp_tvN.py 8
python subset_diffisopheno_Chimp_tvN.py 9
python subset_diffisopheno_Chimp_tvN.py 10
python subset_diffisopheno_Chimp_tvN.py 11
python subset_diffisopheno_Chimp_tvN.py 12
python subset_diffisopheno_Chimp_tvN.py 13
python subset_diffisopheno_Chimp_tvN.py 14
python subset_diffisopheno_Chimp_tvN.py 15
python subset_diffisopheno_Chimp_tvN.py 16
python subset_diffisopheno_Chimp_tvN.py 18
python subset_diffisopheno_Chimp_tvN.py 19
python subset_diffisopheno_Chimp_tvN.py 20
python subset_diffisopheno_Chimp_tvN.py 21
python subset_diffisopheno_Chimp_tvN.py 22

Run leafcutter:


sbatch runNuclearDifffIso.sh

Concatinate results:

awk '{if(NR>1)print}' ../data/DiffIso_Nuclear/TN_diff_isoform_chr*.txt_effect_sizes.txt > ../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_effect_sizes.txt


awk '{if(NR>1)print}' ../data/DiffIso_Nuclear/TN_diff_isoform_chr*.txt_cluster_significance.txt > ../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_significance.txt

Significant clusters:

sig=read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") 

sig$p.adjust=as.numeric(as.character(sig$p.adjust))

qqplot(-log10(runif(nrow(sig))), -log10(sig$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Chimp: Leafcutter differencial isoform analysis between fractions")
abline(0,1)

tested_genes=nrow(sig)
tested_genes

[1] 7160

sig_genes=sig %>% filter(p.adjust<.05)
number_sig_genes=nrow(sig_genes)
number_sig_genes

[1] 1764

Effect Sizes

effectsize=read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltaPAU')) %>% filter(intron != "intron")

effectsize$deltaPAU=as.numeric(as.character(effectsize$deltaPAU))
effectsize$logef=as.numeric(as.character(effectsize$logef))

plot(sort(effectsize$deltaPAU),main="Leafcutter delta PAU", ylab="Delta PAU", xlab="PAS Index")

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] reshape2_1.4.3  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

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       workflowr_1.4.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4

Compare Nuclear Fraction PAS

Briana Mittleman

10/9/2019