Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    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
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Deleted:    docs/figure/DiffIsoAnalysis.Rmd/figure1Emain-1.pdf
    Deleted:    docs/figure/DiffIsoAnalysis.Rmd/figure1Esubset-1.pdf
    Deleted:    reads_graphs.Rmd

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Rmd	aa1ad85	brimittleman	2019-07-31	add pdf for figrues
html	c5bf3fd	brimittleman	2019-07-02	Build site.
Rmd	36d86c0	brimittleman	2019-07-02	post LM plot midifications
html	3e79995	brimittleman	2019-06-24	Build site.
Rmd	494ab8a	brimittleman	2019-06-24	add diff prop test
html	499e504	brimittleman	2019-06-22	Build site.
Rmd	97e2ea8	brimittleman	2019-06-22	add pie chart
html	6679c95	brimittleman	2019-06-21	Build site.
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
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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
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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 1.4.0
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] 9221

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

[1] 6946

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] 1764

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] 1334

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
6679c95	brimittleman	2019-06-21
4f2326e	brimittleman	2019-06-21
ae5c5a1	brimittleman	2019-06-21
9d0950c	brimittleman	2019-06-13

effectsize_deltaPAU_total_locProp

# A tibble: 5 x 2
  loc    nloctotal
  <chr>      <int>
1 cds           30
2 end           63
3 intron       126
4 utr3         916
5 utr5          38

sum(effectsize_deltaPAU_total_locProp$nloctotal)

[1] 1173

effectsize_deltaPAU_nuclear_locProp

# A tibble: 5 x 2
  loc    nlocnuclear
  <chr>        <int>
1 cds              9
2 end             83
3 intron         387
4 utr3           101
5 utr5            11

sum(effectsize_deltaPAU_nuclear_locProp$nlocnuclear)

[1] 591

effectsize_deltaPAUProp_both_melt_sm=effectsize_deltaPAUProp_both_melt %>% filter(loc=="intron" | loc=="utr3")


ggplot(effectsize_deltaPAUProp_both_melt_sm, aes(x=loc, y=Proportion, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position="dodge") + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + labs(title="Differencially Used PAS",x="", y="Proportion of All PAS at Location") +scale_x_discrete(labels = c('Intronic',"3' UTR")) +theme(axis.text.x = element_text(angle = 45, hjust = 1)) +  theme(legend.position ="bottom", legend.direction = "horizontal") +  theme(panel.background = element_blank(),text = element_text(size=16), plot.title = element_text(size = 20, face = "bold"),axis.text.x = element_text(size=14),axis.text.y = element_text(size=14))

Version	Author	Date
15fbb3c	brimittleman	2019-07-31
3e79995	brimittleman	2019-06-24
760b297	brimittleman	2019-05-14

effectsize_deltaPAUProp_both_melt_sm

     loc Fraction  Proportion
1 intron  Nuclear 0.030250918
2   utr3  Nuclear 0.004996537
3 intron    Total 0.009849136
4   utr3    Total 0.045315128

#intronic
prop.test(x=c(387,126), n=c(591,1173),alternative = "greater")


    2-sample test for equality of proportions with continuity
    correction

data:  c(387, 126) out of c(591, 1173)
X-squared = 568.34, df = 1, p-value < 2.2e-16
alternative hypothesis: greater
95 percent confidence interval:
 0.5106946 1.0000000
sample estimates:
   prop 1    prop 2 
0.6548223 0.1074169

#3' utr
prop.test(x=c(101,916), n=c(591,1173),alternative = "less")


    2-sample test for equality of proportions with continuity
    correction

data:  c(101, 916) out of c(591, 1173)
X-squared = 596.48, df = 1, p-value < 2.2e-16
alternative hypothesis: less
95 percent confidence interval:
 -1.0000000 -0.5764348
sample estimates:
   prop 1    prop 2 
0.1708968 0.7809037

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
3e79995	brimittleman	2019-06-24

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
3e79995	brimittleman	2019-06-24
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
3e79995	brimittleman	2019-06-24
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
3e79995	brimittleman	2019-06-24
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
3e79995	brimittleman	2019-06-24
6679c95	brimittleman	2019-06-21
4f2326e	brimittleman	2019-06-21
ae5c5a1	brimittleman	2019-06-21
9d0950c	brimittleman	2019-06-13

summary(effectsize_length_catall$PercLength)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-16763.99      0.89      1.04     22.93      1.91  86510.07

summary(effectsize$logef)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-2.241524 -0.319633 -0.006858  0.000000  0.329087  2.456682

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
3e79995	brimittleman	2019-06-24

densitylocdifuse=ggplot(effectsize_length_cat, aes(x=PercLength, by=fraction, fill=fraction))+ geom_density(alpha=.75)  + labs(title="Location of differentially used PAS \nwithin a gene body", fill="Fraction", x="Percent of Gene Length")+scale_fill_manual(values=c("deepskyblue3","darkviolet"),labels = c("Nuclear", "Total"))+ theme(legend.position = "bottom", legend.direction = "horizontal")+  theme(panel.background = element_blank(),text = element_text(size=16), plot.title = element_text(size = 20, face = "bold"))

densitylocdifuse

Version	Author	Date
15fbb3c	brimittleman	2019-07-31
c5bf3fd	brimittleman	2019-07-02
3e79995	brimittleman	2019-06-24

Diff iso gene proportion:

genes_sig=sig %>% separate(cluster,into=c("chr", "gene"), sep=":") %>% group_by(gene) %>% summarise(n=n()) %>% nrow
genes_detlapau= effectSize_highdiffGenes %>% nrow()
testedgenes=read.table("../data/DiffIso/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.fixed.forLC.fc",header = T, stringsAsFactors = F) %>% rownames_to_column("ID") %>% select(ID)%>% separate(ID, into=c("chr", "start", "end", "geneID"),sep=":") %>% separate(geneID, into=c("gene", "loc"),sep="_")  %>% group_by(gene) %>% summarise(n=n()) %>% nrow()
notsig=testedgenes-genes_sig
sighothighpau=genes_sig-genes_detlapau

cat=c("NotSig", "SigNotHighPAU", "SigandHighPAU")
values=c(unlist(notsig),unlist(sighothighpau),unlist(genes_detlapau))

difiso_df=as.data.frame(cbind(cat, values)) 
difiso_df$values=as.numeric(as.character(difiso_df$values))
difiso_df=difiso_df%>% mutate(proportion=values/testedgenes)


ggplot(difiso_df, aes(x="",y=proportion, fill=cat)) + geom_bar(stat="identity")+geom_text(aes(label=values))

Version	Author	Date
c5bf3fd	brimittleman	2019-07-02
3e79995	brimittleman	2019-06-24

slices <- c(notsig, sighothighpau,genes_detlapau)
lbls <- c("No Sig PAS", "At least 1 \nSig PAS", "At least 1 Sig PAS\n High Delta PAU")
pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, pct, sep="\n   ") # add percents to labels 
lbls <- paste(lbls,"%",sep="") # ad % to labels 
pie(slices, labels = lbls,col=c("Azure2", "Aquamarine1","Darkslateblue"))

Version	Author	Date
c5bf3fd	brimittleman	2019-07-02

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_1.4.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        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         fansi_0.4.0      readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 labeling_0.3     utf8_1.1.4       stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Total v Nuclear

Briana Mittleman

5/2/2019