Last updated: 2019-05-13

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File Version Author Date Message
Rmd f514b6e brimittleman 2019-05-13 add combined plot
html 07c9125 brimittleman 2019-05-13 Build site.
Rmd 981ac33 brimittleman 2019-05-13 add location of highly used
html c561b14 brimittleman 2019-05-06 Build site.
Rmd 1d8a0a3 brimittleman 2019-05-06 add res
html 60093ce brimittleman 2019-05-02 Build site.
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 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
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── Conflicts ──────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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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")

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")

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.


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