Last updated: 2019-06-21

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLandeQTLoverlap.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	842be25	brimittleman	2019-06-21	fix fif
html	4f2326e	brimittleman	2019-06-21	Build site.
Rmd	abd1a73	brimittleman	2019-06-21	fix figures
html	ae5c5a1	brimittleman	2019-06-21	Build site.
Rmd	0d606c1	brimittleman	2019-06-21	fix figures
html	2d1a80c	brimittleman	2019-06-16	Build site.
Rmd	8944f90	brimittleman	2019-06-16	fix effect size header
html	9d0950c	brimittleman	2019-06-13	Build site.
Rmd	17955ab	brimittleman	2019-06-13	fix big bug
html	b6ed10c	brimittleman	2019-05-22	Build site.
Rmd	312d7d7	brimittleman	2019-05-22	add non facet plot
html	bf3a1e0	brimittleman	2019-05-14	Build site.
Rmd	77ca26a	brimittleman	2019-05-14	results by logef
html	760b297	brimittleman	2019-05-14	Build site.
Rmd	4c10e8f	brimittleman	2019-05-14	add dist to PAS plot
html	d0aa6a3	brimittleman	2019-05-13	Build site.
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  
✔ 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

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

Rscript /project2/gilad/briana/davidaknowles-leafcutter-c3d9474/scripts/leafcutter_ds.R –num_threads 4 ../data/DiffIso/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.fixed.forLC.fc_22.txt ../data/bothFrac_FC/sample_groups.txt -o ../data/DiffIso/TN_diff_isoform_chr22.txt

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
9d0950c	brimittleman	2019-06-13
c561b14	brimittleman	2019-05-06

tested_genes=nrow(sig)
tested_genes

[1] 9564

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

[1] 7479

sig_genesonly=sig_genes %>% separate(cluster,into=c("chrom", "geneName"), sep = ":") %>% dplyr::select(geneName)

write.table(sig_genesonly, file="../data/sigDiffGenes.txt", col.names = T, row.names = F, quote = F)

Effect sizes

effectsize=read.table("../data/DiffIso/TN_diff_isoform_allChrom.txt_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
9d0950c	brimittleman	2019-06-13
c561b14	brimittleman	2019-05-06

Filter PAU > .2

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

[1] 2096

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


write.table(effectSize_highdiffGenes, file="../data/highdiffsiggenes.txt", col.names = F, row.names = F, quote = F)

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] 1593

Filter >.2 in

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

#write out at bed
#need strand info
PAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F,col.names = c("chrom", "start", "end", "peak", "score", "strand") )%>% separate(peak, into=c("peaknum","peakID"), sep=":") %>% separate(peakID, into=c("gene", "loc"), sep="_") %>% dplyr::select(gene, strand) %>% unique()
effectsize_deltaPAU_nuclear_bed=effectsize_deltaPAU_nuclear %>% separate(intron, into=c("chr", "peakStart", "peakEnd", "gene"), sep=":") %>% inner_join(PAS, by="gene")  %>% mutate(PASstart=ifelse(strand=="+", as.integer(peakEnd)-1, as.integer(peakStart)+1)) %>% mutate(PASend=ifelse(strand=="+", as.integer(peakEnd), as.integer(peakStart))) %>% mutate(score=".") %>%  dplyr::select(chr, peakStart, peakEnd, gene, score, strand) 

write.table(effectsize_deltaPAU_nuclear_bed, file="../data/PAS/UsedMoreNuclearPAU2.bed", col.names = F, row.names = F, quote = F,sep = "\t")

Filter >.2 in Total:

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

effectsize_deltaPAU_total_bed=effectsize_deltaPAU_total %>% separate(intron, into=c("chr", "peakStart", "peakEnd", "gene"), sep=":") %>% inner_join(PAS, by="gene")  %>% mutate(PASstart=ifelse(strand=="+", as.integer(peakEnd)-1, as.integer(peakStart)+1)) %>% mutate(PASend=ifelse(strand=="+", as.integer(peakEnd), as.integer(peakStart))) %>% mutate(score=".") %>%  dplyr::select(chr, peakStart, peakEnd, gene, score, strand) 

write.table(effectsize_deltaPAU_total_bed, file="../data/PAS/UsedMoreTotalPAU2.bed", col.names = F, row.names = F, quote = F,sep="\t")

Sort the files:

sort -k1,1 -k2,2n ../data/PAS/UsedMoreTotalPAU2.bed > ../data/PAS/UsedMoreTotalPAU2.sort.bed
sort -k1,1 -k2,2n ../data/PAS/UsedMoreNuclearPAU2.bed > ../data/PAS/UsedMoreNuclearPAU2.sort.bed

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
9d0950c	brimittleman	2019-06-13
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
9d0950c	brimittleman	2019-06-13

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
9d0950c	brimittleman	2019-06-13
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
9d0950c	brimittleman	2019-06-13

Merge to 1 figure:

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)

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 differential used by location",x="") +scale_x_discrete(labels = c('Coding','5kb downstream','Intronic',"3' UTR", "5' UTR")) +theme(axis.text.x = element_text(angle = 90, hjust = 1)) +  theme(legend.position = c(0.1,.9), legend.direction = "horizontal") +  theme(panel.background = element_blank())

Version	Author	Date
4f2326e	brimittleman	2019-06-21
ae5c5a1	brimittleman	2019-06-21
9d0950c	brimittleman	2019-06-13

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.

colnames(effectsize)=c("intron", "logef","Nuclear", "Total", "deltaPAU")
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")

Version	Author	Date
9d0950c	brimittleman	2019-06-13
760b297	brimittleman	2019-05-14

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")

Version	Author	Date
9d0950c	brimittleman	2019-06-13
760b297	brimittleman	2019-05-14

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")

Version	Author	Date
9d0950c	brimittleman	2019-06-13

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"))

Version	Author	Date
9d0950c	brimittleman	2019-06-13

By length of gene

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

effectsize_length= effectsize %>% separate(intron, into=c("chrom", "start", "end", "gene"),sep=":") %>% mutate(fraction=ifelse(deltaPAU < 0, "nuclear", "total")) %>% inner_join(length, by="gene") %>% mutate(PercLength=ifelse(strand=="+", ((as.numeric(end)-as.numeric(TSS))/as.numeric(length)), (1-(as.numeric(start)-as.numeric(TSS))/as.numeric(length)))) 

effectsize_length_tot= effectsize_length %>% 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_length_nuc=effectsize_length %>% 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_length_cat=as.data.frame(rbind(effectsize_length_tot, effectsize_length_nuc)) %>% filter(PercLength<=1 & PercLength >0)

effectsize_length_catall=as.data.frame(rbind(effectsize_length_tot, effectsize_length_nuc))

ggplot(effectsize_length_cat, aes(x=PercLength, by=fraction, fill=fraction))+ geom_histogram(alpha=.4,bins=10) + facet_grid(~PAU_Cat) + labs(title="Location of differentially used PAS within a gene body ")+scale_fill_manual(values=c("deepskyblue3","darkviolet"))

Version	Author	Date
9d0950c	brimittleman	2019-06-13

summary(effectsize_length_catall$PercLength)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-16763.99      0.87      1.03     28.84      1.89  86510.07

summary(effectsize$logef)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-2.44401 -0.33487 -0.01384  0.00000  0.34328  2.47805

ggplot(effectsize_length_cat, aes(x=PercLength, by=fraction, fill=fraction))+ geom_histogram(,bins=50)  + labs(title="Location of differentially used PAS \nwithin a gene body", fill="Fraction", y="Number of PAS", x="Percent of Gene Length")+scale_fill_manual(values=c("deepskyblue3","darkviolet"),labels = c("Nuclear", "Total"))+ theme(legend.position = c(0.1,.9), legend.direction = "horizontal")+  theme(panel.background = element_blank())

Version	Author	Date
4f2326e	brimittleman	2019-06-21
ae5c5a1	brimittleman	2019-06-21
9d0950c	brimittleman	2019-06-13

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_0.25.2     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

Total v Nuclear

Briana Mittleman

5/2/2019