Chimp Total vs Nuclear

Last updated: 2019-10-07

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Knit directory: Comparative_APA/analysis/

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File	Version	Author	Date	Message
Rmd	5759e7e	brimittleman	2019-10-07	fix name
html	bf8c7fc	brimittleman	2019-10-07	Build site.
Rmd	4263687	brimittleman	2019-10-07	fix graph label
html	62b5285	brimittleman	2019-10-07	Build site.
Rmd	8466418	brimittleman	2019-10-07	add human and chimp tvN res
html	c3aa529	brimittleman	2019-10-07	Build site.
Rmd	da85ad8	brimittleman	2019-10-07	add code to prepare human and chimp TvN

library(tidyverse)

── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
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── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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✖ dplyr::lag()    masks stats::lag()

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

In this anaylsis I will complete the Chimp total vs nuclear analysis. This analysis is similar to the analysis in the apaQTL project

I need the human 5% both fraction feature counts.

Thee feature counts are in ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc. I need to subset these for those in the annotations. Keep the PAS in this file: ../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed. I will do this with a python script.

mkdir ../Chimp/data/CleanLiftedPeaks4LC/
python prepareCleanLiftedFC_5perc4LC.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc ../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed ../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc

This will only look at PAS on chromosomes 1-22 no extra haplotpyes.

mkdir ../Chimp/data/DiffIso_Chimp/

python subset_diffisopheno_Chimp_tvN.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

Make sample groups:

python makeSamplyGroupsChimp_TvN.py

Run Leafcutter:

sbatch runChimpDiffIso.sh

Concatinate results:

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


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

Significant clusters:

sig=read.table("../Chimp/data/DiffIso_Chimp/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)

Version	Author	Date
62b5285	brimittleman	2019-10-07

tested_genes=nrow(sig)
tested_genes

[1] 7571

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

[1] 2528

Effect Sizes

effectsize=read.table("../Chimp/data/DiffIso_Chimp/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="Chimp: Leafcutter delta PAU", ylab="Delta PAU", xlab="PAS Index")

Version	Author	Date
62b5285	brimittleman	2019-10-07

effectsize_deltaPAU= effectsize %>% filter(abs(deltaPAU) > .2) 
nrow(effectsize_deltaPAU)

[1] 1153

effectSize_highdiffGenes=effectsize_deltaPAU %>% separate(intron, into=c("chrom", "start", "end", "GeneName"), sep=":") %>% dplyr::select(GeneName) %>% unique()

effectsize_deltaPAU_Genes= effectsize_deltaPAU %>% separate(intron, into=c("chrom", "start", "end","gene"),sep=":") %>% group_by(gene) %>% summarise(nperGene=n()) 

nrow(effectsize_deltaPAU_Genes)

[1] 976

Pull in the PAS to see where these PAS are located.

PAS=read.table("../data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", stringsAsFactors = F, header = T) %>% filter(loc !="008559")

PAS$start=as.character(PAS$start)
PAS$end=as.character(PAS$end)

Increased usage in nuclear:

effectsize_deltaPAU_nuclear= effectsize_deltaPAU %>% filter(deltaPAU < -0.2) %>% separate(intron,into=c("chr", "start", "end", "gene"), sep=":" )%>% inner_join(PAS, by=c("gene", "chr","start", "end"))

ggplot(effectsize_deltaPAU_nuclear,aes(x=loc,fill=loc)) + geom_histogram(stat="count") + scale_fill_brewer(palette = "Dark2") +labs(title="Chimp: Location of PAS used more in Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
bf8c7fc	brimittleman	2019-10-07
62b5285	brimittleman	2019-10-07

Increased usage in total:

effectsize_deltaPAU_total= effectsize_deltaPAU %>% filter(deltaPAU > 0.2)  %>% separate(intron,into=c("chr", "start", "end", "gene"), sep=":" )%>% inner_join(PAS, by=c("gene", "chr","start", "end"))

ggplot(effectsize_deltaPAU_total,aes(x=loc,fill=loc)) + geom_histogram(stat="count") + scale_fill_brewer(palette = "Dark2") +labs(title="Chimp: Location of PAS used more in Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
bf8c7fc	brimittleman	2019-10-07
62b5285	brimittleman	2019-10-07

This is in line with expectation. Now I can compare this to the number of discovered PAS by plotting the proportion of PAS used more in each fraction by location.

PAS_loc =PAS%>% group_by(loc) %>% summarise(nloc=n())

effectsize_deltaPAU_nuclear_loc=effectsize_deltaPAU_nuclear %>% group_by(loc) %>% summarise(nlocNuclear=n()) 

effectsize_deltaPAU_total_loc=effectsize_deltaPAU_total %>% group_by(loc) %>% summarise(nloctotal=n()) 

effectsize_deltaPAUProp_tot=effectsize_deltaPAU_total_loc %>% inner_join(PAS_loc, by="loc") %>% mutate(Proportion_tot=nloctotal/nloc)

effectsize_deltaPAUProp_nuc=effectsize_deltaPAU_nuclear_loc %>% inner_join(PAS_loc, by="loc") %>% mutate(Proportion_nuc=nlocNuclear/nloc)


effectsize_deltaPAUProp_both= effectsize_deltaPAUProp_nuc %>% inner_join(effectsize_deltaPAUProp_tot, by=c("loc","nloc")) %>% dplyr::rename(Nuclear=Proportion_nuc, Total=Proportion_tot) %>% select(loc, Nuclear, Total) 
effectsize_deltaPAUProp_both_melt= effectsize_deltaPAUProp_both %>% melt(id.vars="loc", variable.name="Fraction", value.name = "Proportion") 
effectsize_deltaPAUProp_both_melt$Fraction=as.character(effectsize_deltaPAUProp_both_melt$Fraction)

Plot this:

ggplot(effectsize_deltaPAUProp_both_melt, aes(x=loc, y=Proportion, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position="dodge") + scale_fill_brewer(palette = "Dark2")+ labs(title="Chimp: Proportion of PAS differential used by location",x="") +scale_x_discrete(labels = c('Coding','5kb downstream','Intronic',"3' UTR", "5' UTR"))

Version	Author	Date
62b5285	brimittleman	2019-10-07

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         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] pillar_1.3.1       compiler_3.5.1     git2r_0.25.2      
 [7] plyr_1.8.4         workflowr_1.4.0    tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       magrittr_1.5       whisker_0.3-2     
[40] backports_1.1.2    scales_1.0.0       htmltools_0.3.6   
[43] rvest_0.3.2        assertthat_0.2.0   colorspace_1.3-2  
[46] labeling_0.3       stringi_1.2.4      lazyeval_0.2.1    
[49] munsell_0.5.0      broom_0.5.1        crayon_1.3.4

Chimp Total vs Nuclear

Briana Mittleman

10/7/2019