Last updated: 2019-05-14

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Rmd 24c2ceb brimittleman 2019-05-02 add diff iso 
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(reshape2)

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

    smiths

In this analysis I wil use leafcutter to call PAS with differential ussage between fractions.

Prepare annotation

I first filter the annotated peak SAF file for peaks passing the 5% coverage in either fraction.

python makeSAFbothfrac5perc.py

Peak quantification

mkdir bothFrac_FC

Run feature counts with these peaks with both fractions:

sbatch bothFrac_FC.sh

Fix the header:

python fixFChead_bothfrac.py ../data/bothFrac_FC/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.fc ../data/bothFrac_FC/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.fixed.fc

Remove location demoniaiton:

Prepare leafcutter phenotype

mkdir ../data/DiffIso
python fc2leafphen.py

Fix pheno to remove location:

python removeloc_pheno.py ../data/DiffIso/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.fixed.forLC.fc ../data/DiffIso/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.fixed.forLC_noloc.fc
python subset_diffisopheno.py 1
python subset_diffisopheno.py 2
python subset_diffisopheno.py 3
python subset_diffisopheno.py 4
python subset_diffisopheno.py 5
python subset_diffisopheno.py 6
python subset_diffisopheno.py 7
python subset_diffisopheno.py 8
python subset_diffisopheno.py 9
python subset_diffisopheno.py 10
python subset_diffisopheno.py 11
python subset_diffisopheno.py 12
python subset_diffisopheno.py 13
python subset_diffisopheno.py 14
python subset_diffisopheno.py 15
python subset_diffisopheno.py 16
python subset_diffisopheno.py 18
python subset_diffisopheno.py 19
python subset_diffisopheno.py 20
python subset_diffisopheno.py 21
python subset_diffisopheno.py 22

Make the sample groups file:

python LC_samplegroups.py

Run leafcutter

The leafcutter environment is not in the three-prime-seq environment. Make sure leafcutter is installed and working.

sbatch run_leafcutterDiffIso.sh

Concatinate results:

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


awk '{if(NR>1)print}' ../data/DiffIso/TN_diff_isoform_chr*.txt_cluster_significance.txt > ../data/DiffIso/TN_diff_isoform_AllChrom_cluster_significance.txt

Evaluate results

Significant clusters

sig=read.table("../data/DiffIso/TN_diff_isoform_AllChrom_cluster_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="Leafcutter differencial isoform analysis between fractions")
abline(0,1)

Version Author Date
c561b14 brimittleman 2019-05-06 
tested_genes=nrow(sig)
tested_genes
[1] 10815
sig_genes=sig %>% filter(p.adjust<.05)
number_sig_genes=nrow(sig_genes)
number_sig_genes
[1] 9446

Effect sizes

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

write.table(effectsize,file="../data/DiffIso/EffectSizes.txt", quote = F, col.names = T, row.names = F)

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

Plot delta PAU:

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

Version Author Date
c561b14 brimittleman 2019-05-06

Filter PAU > .2

effectsize_deltaPAU= effectsize %>% filter(abs(deltaPAU) > .2) 
nrow(effectsize_deltaPAU)
[1] 2090

Genes in this set:

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] 1689

Filter >.2 in Nuclear

effectsize_deltaPAU_nuclear= effectsize_deltaPAU %>% filter(deltaPAU < -0.2)

Filter >.2 in Total:

effectsize_deltaPAU_total= effectsize_deltaPAU %>% filter(deltaPAU > 0.2)

Location of high >PAU

Total:

Pull in location information for each PAS:

PAS=read.table("../data/peaks_5perc/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.SAF",stringsAsFactors = F,header = T) %>% separate(GeneID, into=c("num", "chr", "start", "end", "strand", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"),sep="_") %>%  mutate(intron=paste("chr", Chr, ":", Start, ":", End, ":", gene,sep="")) %>% select(intron, loc)
effectsize_deltaPAU_total_loc=effectsize_deltaPAU_total %>% inner_join(PAS, by="intron") 


ggplot(effectsize_deltaPAU_total_loc,aes(x=loc)) + geom_histogram(stat="count") + labs(title="Location of Total peaks >.2 PAU") 
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
07c9125 brimittleman 2019-05-13

Nuclear:

effectsize_deltaPAU_nuclear_loc=effectsize_deltaPAU_nuclear %>% inner_join(PAS, by="intron") 


ggplot(effectsize_deltaPAU_nuclear_loc,aes(x=loc)) + geom_histogram(stat="count") + labs(title="Location of Nuclear peaks >.2 PAU")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
07c9125 brimittleman 2019-05-13

I will want to look at proportions. I need to know how many peaks are in each location:

PAS_loc =PAS%>% group_by(loc) %>% summarise(nloc=n())
effectsize_deltaPAU_total_locProp=effectsize_deltaPAU_total_loc %>% group_by(loc) %>% summarise(nloctotal=n()) 
effectsize_deltaPAU_nuclear_locProp=effectsize_deltaPAU_nuclear_loc %>% group_by(loc) %>% summarise(nlocnuclear=n()) 

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

effectsize_deltaPAUProp_nuc=effectsize_deltaPAU_nuclear_locProp %>% inner_join(PAS_loc, by="loc") %>% mutate(Proportion_nuc=nlocnuclear/nloc)
ggplot(effectsize_deltaPAUProp_tot, aes(x=loc, y=Proportion_tot)) + geom_bar(stat="identity") + labs(y="Proportion of all called PAS", title="Location of high Total used PAS")

Version Author Date
07c9125 brimittleman 2019-05-13 
ggplot(effectsize_deltaPAUProp_nuc, aes(x=loc, y=Proportion_nuc)) + geom_bar(stat="identity") + labs(y="Proportion of all called PAS", title="Location of high nuclear used PAS")

Version Author Date
07c9125 brimittleman 2019-05-13

Merge to 1 figure:

effectsize_deltaPAUProp_both= effectsize_deltaPAUProp_nuc %>% inner_join(effectsize_deltaPAUProp_tot, by=c("loc","nloc")) %>% 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") 
ggplot(effectsize_deltaPAUProp_both_melt, aes(x=loc, y=Proportion, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position="dodge") + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + labs(title="Proportion of PAS differentiall used by location")

Version Author Date
d0aa6a3 brimittleman 2019-05-13

More differentiall used in total. this makes sense because there are more used peaks in the nuclear which evens out the distribution of the ratios.

Stratify by different \(\Delta\) PAU

I want to create a data frame that has the location proportion distribution based on different \(\Delta\) PAU. 0-.1 .1-.2 .2-.3 .3-.4 .4-.5 >.5

First I will seperate the total and nuclear but the sign of the \(\Delta\) PAU.

Total_dpau= effectsize %>% filter(deltaPAU > 0) %>% inner_join(PAS, by="intron") %>% select(-logef, -Nuclear,-Total) %>%  mutate(fraction="Total", PAU_Cat=ifelse(deltaPAU <.1, "<.1", ifelse(deltaPAU >=.1 & deltaPAU <.2, "<.2", ifelse(deltaPAU >=.2 & deltaPAU <.3, "<.3", ifelse(deltaPAU >=.3 & deltaPAU <.4, "<.4", "<.5"))))) 

Nuclear_dpau= effectsize %>% filter(deltaPAU <0) %>% inner_join(PAS, by="intron") %>% select(-logef,-Nuclear,-Total) %>% mutate(fraction="Nuclear", PAU_Cat=ifelse(deltaPAU >-.1, "<.1", ifelse(deltaPAU <=-.1 & deltaPAU > -.2, "<.2", ifelse(deltaPAU <=-.2 & deltaPAU >-.3, "<.3", ifelse(deltaPAU <=-.3 & deltaPAU >-.4, "<.4", "<.5")))))

Merge these together to start grouping:

allPAU=as.data.frame(rbind(Total_dpau, Nuclear_dpau)) %>% group_by(fraction, PAU_Cat, loc ) %>% summarise(nperLoc=n()) %>% full_join(PAS_loc, by ="loc") %>% mutate(Prop=nperLoc/nloc)

Plot it:

ggplot(allPAU, aes(x=loc,y=Prop, group=fraction, fill=fraction)) + geom_bar(stat="identity", position = "dodge") + facet_wrap(~PAU_Cat)+ scale_fill_manual(values=c("deepskyblue3","darkviolet")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Proportion of PAS by location and delta PAU")

allPAU_remove.1= allPAU %>% filter(PAU_Cat != "<.1")

ggplot(allPAU_remove.1, aes(x=loc,y=Prop, group=fraction, fill=fraction)) + geom_bar(stat="identity", position = "dodge") + facet_wrap(~PAU_Cat)+ scale_fill_manual(values=c("deepskyblue3","darkviolet")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Proportion of PAS by location and delta PAU")

Proportion within group:

allPAU_ingroup= allPAU %>% mutate(nCat=sum(nperLoc),proppercat=nperLoc/nCat)

ggplot(allPAU_ingroup, aes(x=loc,y=proppercat, group=fraction, fill=fraction)) + geom_bar(stat="identity", position = "dodge") + facet_wrap(~PAU_Cat)+ scale_fill_manual(values=c("deepskyblue3","darkviolet")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Proportion of PAS by location and delta PAU")

Distance to TSS:

I need to pull in the TSS information so I can look at the distance between the differentially used peaks and by distance .

tss=read.table("../../genome_anotation_data/refseq.ProteinCoding.bed",col.names = c("chrom", "start", "end", "gene", "score", "strand") ,stringsAsFactors = F) %>% mutate(TSS= ifelse(strand=="+", start, end)) %>% select(gene, TSS, strand)

Seperate effect size introns:

PAS base for + strand is end, PAS for neg stand in -

effectsize_TSS= effectsize %>% separate(intron, into=c("chrom", "start", "end", "gene"),sep=":") %>% mutate(fraction=ifelse(deltaPAU < 0, "nuclear", "total")) %>% inner_join(tss, by="gene") %>% mutate(dist2PAS=ifelse(strand=="+", as.numeric(end)-as.numeric(TSS), as.numeric(TSS)-as.numeric(start))) 

effectsize_TSS_tot= effectsize_TSS %>% filter(fraction=="total") %>% mutate( PAU_Cat=ifelse(deltaPAU <.1, "<.1", ifelse(deltaPAU >=.1 & deltaPAU <.2, "<.2", ifelse(deltaPAU >=.2 & deltaPAU <.3, "<.3", ifelse(deltaPAU >=.3 & deltaPAU <.4, "<.4", "<.5"))))) 


effectsize_TSS_nuc=effectsize_TSS %>% filter(fraction=="nuclear") %>% mutate( PAU_Cat=ifelse(deltaPAU >-.1, "<.1", ifelse(deltaPAU <=-.1 & deltaPAU > -.2, "<.2", ifelse(deltaPAU <=-.2 & deltaPAU >-.3, "<.3", ifelse(deltaPAU <=-.3 & deltaPAU >-.4, "<.4", "<.5")))))


effectsize_TSS_cat=as.data.frame(rbind(effectsize_TSS_tot, effectsize_TSS_nuc)) %>% filter(dist2PAS >0)
ggplot(effectsize_TSS_cat, aes(x=log10(dist2PAS), by=fraction, fill=fraction))+ geom_density(alpha=.4) + facet_grid(~PAU_Cat) + labs(title="Distance to TSS for differentialy used PAS")+scale_fill_manual(values=c("deepskyblue3","darkviolet")) 


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 workflowr_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.23.0     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4