Nuclear fraction and nascent transcription

Last updated: 2019-06-13

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.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	5ea9c06	brimittleman	2019-06-13	fix bug
html	7aeba54	brimittleman	2019-05-17	Build site.
Rmd	78b53a1	brimittleman	2019-05-17	add full apa by loc
html	a295d27	brimittleman	2019-05-16	Build site.
Rmd	75f4567	brimittleman	2019-05-16	add total intron/all
html	460e1fb	brimittleman	2019-05-16	Build site.
Rmd	1df3fe1	brimittleman	2019-05-16	seperate fractions by locations
html	81a3e16	brimittleman	2019-05-15	Build site.
Rmd	f484dcd	brimittleman	2019-05-15	add nascent transcription plot

library(reshape2)
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(viridis)

Loading required package: viridisLite

Gene name switch file:

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F)

Create transcription phenotype

4su data

FourSU=read.table(file = "../data/fourSU//tr_decay_table_norm.txt", header=T, stringsAsFactors = F) %>%  dplyr::select(gene_id,contains("4su_30"))


FourSU_geneNames=FourSU %>% inner_join(geneNames, by="gene_id") %>% dplyr::select(GeneName, contains("4su_30"))

FourgeneNames_long=melt(FourSU_geneNames,id.vars = "GeneName", value.name = "FourSU", variable.name = "FourSU_ind") %>% separate(FourSU_ind, into=c("type","time", "1400", "MAf", "Individual"), sep="_") %>% dplyr::select(GeneName, Individual, FourSU) 

FourSU_geneMean=FourgeneNames_long %>% group_by(GeneName) %>%summarise(Mean_4su=mean(FourSU))

rna seq

RNA=read.table(file = "../data/fourSU/tr_decay_table_norm.txt", header=T, stringsAsFactors = F) %>%  dplyr::select(gene_id,contains("RNAseq_14000"))


RNA_geneNames=RNA %>% inner_join(geneNames, by="gene_id") %>% dplyr::select(GeneName, contains("RNA"))

RNAgeneNames_long=melt(RNA_geneNames,id.vars = "GeneName", value.name = "RNA", variable.name = "RNA_ind") %>%   separate(RNA_ind, into=c("type", "1400", "MAf", "Individual"), sep="_") %>% dplyr::select(GeneName, Individual, RNA) 

RNA_geneMean=RNAgeneNames_long %>% group_by(GeneName) %>%summarise(Mean_RNA=mean(RNA))

Make transcription phenotype

Transcription=FourSU_geneMean %>% inner_join(RNA_geneMean, by="GeneName") %>% mutate(Transcription=Mean_4su/(Mean_4su + Mean_RNA)) %>% dplyr::select(GeneName, Transcription) %>% dplyr::rename("gene"=GeneName)

Transcription2=FourSU_geneMean %>% inner_join(RNA_geneMean, by="GeneName") %>% mutate(Transcription=Mean_4su/Mean_RNA) %>% dplyr::select(GeneName, Transcription) %>% dplyr::rename("gene"=GeneName)

APA phenotype

5 perc apa

peaknumlist=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.5perc.bed", stringsAsFactors = F, header=F, col.names = c("chr", "start","end", "id", "score", "strand"))  %>% separate(id, into=c("peaknum", "geneid"), sep=":") %>% mutate(peakid=paste("peak", peaknum,sep=""))

Restrict to genes with large diff between file:

sig_genes=read.table(file="../data/highdiffsiggenes.txt",col.names = "gene",stringsAsFactors = F)

Nuclear apa

NucAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% dplyr::select(-chrom , -start, -end, -strand, -loc) %>% semi_join(sig_genes, by="gene")

NucApaMelt=melt(NucAPA, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


NucAPA_bygene= NucApaMelt %>% group_by(gene,Individual) %>% summarise(NuclearSum=sum(count))

total apa

TotAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% dplyr::select(-chrom , -start, -end, -strand, -loc) %>% semi_join(sig_genes, by="gene")

TotApaMelt=melt(TotAPA, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


TotAPA_bygene= TotApaMelt %>% group_by(gene,Individual) %>% summarise(TotalSum=sum(count))

Sum together:

ApaBothFrac=TotAPA_bygene %>% inner_join(NucAPA_bygene, by=c("gene", "Individual"))

ApaBothFrac_melt=melt(ApaBothFrac, id.vars=c("gene", "Individual"),value.name="APA_val" ) %>% mutate(fraction=ifelse(variable=="TotalSum", "total", "nuclear"), line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, APA_val)

Normalize with meta data info:

metadata=read.table("../data/MetaDataSequencing.txt", header = T,stringsAsFactors = F) %>% dplyr::select(line, fraction, Mapped_noMP)

metadata$line= as.character(metadata$line)

ApaBothFracStand=ApaBothFrac_melt %>% full_join(metadata, by=c("line", "fraction")) %>% mutate(StandApa=APA_val/Mapped_noMP)

ApaBothFracStand_geneMean=ApaBothFracStand %>% group_by(fraction, gene) %>% summarise(meanAPA=mean(StandApa, na.rm=T))

ApaBothFracStand_geneMean_spread= spread(ApaBothFracStand_geneMean,fraction,meanAPA ) %>% mutate(APAVal=nuclear/(total+ nuclear))

Join data and plot

Density function:

get_density <- function(x, y, ...) {
  dens <- MASS::kde2d(x, y, ...)
  ix <- findInterval(x, dens$x)
  iy <- findInterval(y, dens$y)
  ii <- cbind(ix, iy)
  return(dens$z[ii])
}

set.seed(1)
dat <- data.frame(
  x = c(
    rnorm(1e4, mean = 0, sd = 0.1),
    rnorm(1e3, mean = 0, sd = 0.1)
  ),
  y = c(
    rnorm(1e4, mean = 0, sd = 0.1),
    rnorm(1e3, mean = 0.1, sd = 0.2)
  )
)

Joing apa and transcription

APAandTranscrption= Transcription %>% inner_join(ApaBothFracStand_geneMean_spread, by="gene")
APAandTranscrption$density <- get_density(APAandTranscrption$APAVal, APAandTranscrption$Transcription, n = 100)



summary(lm(data=APAandTranscrption, APAVal~Transcription))


Call:
lm(formula = APAVal ~ Transcription, data = APAandTranscrption)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.33795 -0.08278  0.00284  0.08772  0.30261 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.46672    0.01894  24.641  < 2e-16 ***
Transcription  0.16317    0.03465   4.709 2.88e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1153 on 917 degrees of freedom
Multiple R-squared:  0.02361,   Adjusted R-squared:  0.02254 
F-statistic: 22.17 on 1 and 917 DF,  p-value: 2.877e-06

Plot:

ggplot(APAandTranscrption, aes(x=Transcription, y=APAVal))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="Nuclear/Nuclear+Total", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Split Nuclear by intronic

Nuclear intronic:

I will have to change the gene names for the 3’ info:

NucAPAIntron=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% filter(loc=="intron")%>% dplyr::select(-chrom , -start, -end, -strand, -loc)

NucApaIntronMelt=melt(NucAPAIntron, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


NucAPAIntron_bygene= NucApaIntronMelt %>% group_by(gene,Individual) %>% summarise(NuclearIntronSum=sum(count))

Total UTR

TotUTRAPA=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>%filter(loc=="utr3") %>%  dplyr::select(-chrom , -start, -end, -strand, -loc)

TotApaUTRMelt=melt(TotUTRAPA, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


TotAPAUTR_bygene= TotApaUTRMelt %>% group_by(gene,Individual) %>% summarise(TotalUTRSum=sum(count))

ApaBothFracLoc=TotAPAUTR_bygene %>% inner_join(NucAPAIntron_bygene, by=c("gene", "Individual"))

ApaBothFracLoc_melt=melt(ApaBothFracLoc, id.vars=c("gene", "Individual"),value.name="APA_val" ) %>% mutate(fraction=ifelse(variable=="TotalUTRSum", "total", "nuclear"), line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, APA_val)


ApaBothFracLocStand=ApaBothFracLoc_melt %>% full_join(metadata, by=c("line", "fraction")) %>% mutate(StandApa=APA_val/Mapped_noMP)

ApaBothFracLocStand_geneMean=ApaBothFracLocStand %>% group_by(fraction, gene) %>% summarise(meanAPA=mean(StandApa, na.rm=T))

ApaBothFracLocStand_geneMean_spread= spread(ApaBothFracLocStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/(total+nuclear)) 

ApaBothFracLocStand_geneMean_spread2= spread(ApaBothFracLocStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/total)

Join this with the transcription info:

APAlocationandTranscrption= Transcription %>% inner_join(ApaBothFracLocStand_geneMean_spread, by="gene")
APAlocationandTranscrption$density <- get_density(APAlocationandTranscrption$APAValLoc, APAlocationandTranscrption$Transcription, n = 100)

ggplot(APAlocationandTranscrption, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="NuclearIntron/TotalUTR + IntronNuclear", title="Relationship between APA fraction and transcription") + scale_color_viridis()

summary(lm(data=APAlocationandTranscrption, APAValLoc~Transcription))


Call:
lm(formula = APAValLoc ~ Transcription, data = APAlocationandTranscrption)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.37748 -0.19928 -0.06962  0.15961  0.73348 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.13859    0.02161   6.414  1.6e-10 ***
Transcription  0.33661    0.03889   8.656  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2425 on 3618 degrees of freedom
Multiple R-squared:  0.02029,   Adjusted R-squared:  0.02002 
F-statistic: 74.93 on 1 and 3618 DF,  p-value: < 2.2e-16

Just the ratio:

APAlocationandTranscrption2= Transcription2 %>% inner_join(ApaBothFracLocStand_geneMean_spread2, by="gene")
APAlocationandTranscrption2$density <- get_density(APAlocationandTranscrption2$APAValLoc, APAlocationandTranscrption2$Transcription, n = 100)
summary(lm(data=APAlocationandTranscrption2, log10(APAValLoc)~log10(Transcription)))


Call:
lm(formula = log10(APAValLoc) ~ log10(Transcription), data = APAlocationandTranscrption2)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.59020 -0.44292 -0.04325  0.39823  2.53804 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -0.47676    0.01104 -43.202   <2e-16 ***
log10(Transcription)  0.51566    0.05285   9.756   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.608 on 3618 degrees of freedom
Multiple R-squared:  0.02563,   Adjusted R-squared:  0.02536 
F-statistic: 95.18 on 1 and 3618 DF,  p-value: < 2.2e-16

ggplot(APAlocationandTranscrption2, aes(x=log10(Transcription), y=log10(APAValLoc)))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(4su/RNA)", y="log10(NuclearIntron/TotalUTR)", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Compare nuclear and total UTR

NucAPAUTR=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Nuclear.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% filter(loc=="utr3")%>% dplyr::select(-chrom , -start, -end, -strand, -loc)

NucAPAUTRMelt=melt(NucAPAUTR, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


NucAPAUTR_bygene= NucAPAUTRMelt %>% group_by(gene,Individual) %>% summarise(NuclearUTRSum=sum(count))

ApaBothFracUTR=TotAPAUTR_bygene %>% inner_join(NucAPAUTR_bygene, by=c("gene", "Individual"))

ApaBothFracUTR_melt=melt(ApaBothFracUTR, id.vars=c("gene", "Individual"),value.name="APA_val" ) %>% mutate(fraction=ifelse(variable=="TotalUTRSum", "total", "nuclear"), line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, APA_val)


ApaBothFracUTRStand=ApaBothFracUTR_melt %>% full_join(metadata, by=c("line", "fraction")) %>% mutate(StandApa=APA_val/Mapped_noMP)

ApaBothFracUTRStand_geneMean=ApaBothFracUTRStand %>% group_by(fraction, gene) %>% summarise(meanAPA=mean(StandApa, na.rm=T))

ApaBothFracUTRStand_geneMean_spread= spread(ApaBothFracUTRStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/total)
ApaBothFracUTRStand_geneMean_spread2= spread(ApaBothFracUTRStand_geneMean,fraction,meanAPA ) %>% mutate(APAValLoc=nuclear/(total+nuclear))

THis is nuclear vs total only looking at teh UTR:

APAUTRandTranscrption= Transcription %>% inner_join(ApaBothFracUTRStand_geneMean_spread, by="gene")
APAUTRandTranscrption$density <- get_density(APAUTRandTranscrption$APAValLoc, APAUTRandTranscrption$Transcription, n = 100)


summary(lm(data=APAUTRandTranscrption, log10(APAValLoc)~log10(Transcription)))


Call:
lm(formula = log10(APAValLoc) ~ log10(Transcription), data = APAUTRandTranscrption)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.69129 -0.18916 -0.00344  0.19699  0.90698 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.153728   0.008935   17.20   <2e-16 ***
log10(Transcription) 0.481833   0.029049   16.59   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2526 on 7709 degrees of freedom
Multiple R-squared:  0.03446,   Adjusted R-squared:  0.03433 
F-statistic: 275.1 on 1 and 7709 DF,  p-value: < 2.2e-16

ggplot(APAUTRandTranscrption, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/RNA", y="NuclearUTR/TotalUTR", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
460e1fb	brimittleman	2019-05-16

APAUTRandTranscrption2= Transcription %>% inner_join(ApaBothFracUTRStand_geneMean_spread2, by="gene")
APAUTRandTranscrption2$density <- get_density(APAUTRandTranscrption2$APAValLoc, APAUTRandTranscrption2$Transcription, n = 100)


summary(lm(data=APAUTRandTranscrption2, log10(APAValLoc)~log10(Transcription)))


Call:
lm(formula = log10(APAValLoc) ~ log10(Transcription), data = APAUTRandTranscrption2)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.45003 -0.08569  0.01581  0.10162  0.37243 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -0.234761   0.004493  -52.25   <2e-16 ***
log10(Transcription)  0.268002   0.014607   18.35   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.127 on 7709 degrees of freedom
Multiple R-squared:  0.04184,   Adjusted R-squared:  0.04171 
F-statistic: 336.6 on 1 and 7709 DF,  p-value: < 2.2e-16

ggplot(APAUTRandTranscrption2, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/RNA+4su", y="NuclearUTR/TotalUTR+NuclearUTR", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Intron Nuclear over all nuclear

Nuclear intron= NucAPAIntron_bygene

all nuclear =NucAPA_bygene

Create this pheno:

ApaNuclear_byloc=NucAPAIntron_bygene %>% inner_join(NucAPA_bygene, by=c("gene", "Individual")) %>% mutate(IntronOverAll=NuclearIntronSum/NuclearSum) %>% mutate(fraction="nuclear",line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, IntronOverAll) %>% group_by(gene) %>% filter(IntronOverAll!=0) %>%  summarise(MeanIntronoverAll=mean(IntronOverAll)) %>% dplyr::rename("GeneName"=gene)

Join with RNA

nuclearandRNA=ApaNuclear_byloc %>% inner_join(RNA_geneMean, by="GeneName")

nuclearandRNA$density <- get_density(nuclearandRNA$MeanIntronoverAll, nuclearandRNA$Mean_RNA, n = 100)

Plot:

ggplot(nuclearandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="NuclearIntron/NuclearAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

summary(lm(data=nuclearandRNA, MeanIntronoverAll~log10(Mean_RNA)))


Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = nuclearandRNA)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.47913 -0.12703 -0.01765  0.12618  0.55620 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)      0.18933    0.06799   2.784  0.00549 ** 
log10(Mean_RNA) -0.06897    0.01559  -4.425  1.1e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1865 on 811 degrees of freedom
Multiple R-squared:  0.02357,   Adjusted R-squared:  0.02237 
F-statistic: 19.58 on 1 and 811 DF,  p-value: 1.098e-05

Same plot with transcription phenotype on bottom:

ApaNuclear_byloc_rename=ApaNuclear_byloc %>% dplyr::rename("gene"=GeneName)
nuclearandtranscription=ApaNuclear_byloc_rename %>% inner_join(Transcription, by="gene")

nuclearandtranscription$density <- get_density(nuclearandtranscription$MeanIntronoverAll, nuclearandtranscription$Transcription, n = 100)

ggplot(nuclearandtranscription, aes(x=Transcription, y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="NuclearIntron/NuclearAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

summary(lm(data=nuclearandtranscription, MeanIntronoverAll~Transcription))


Call:
lm(formula = MeanIntronoverAll ~ Transcription, data = nuclearandtranscription)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.44181 -0.13029 -0.01467  0.12672  0.51883 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.51462    0.03432  14.994   <2e-16 ***
Transcription -0.04813    0.06269  -0.768    0.443    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1887 on 811 degrees of freedom
Multiple R-squared:  0.0007261, Adjusted R-squared:  -0.000506 
F-statistic: 0.5893 on 1 and 811 DF,  p-value: 0.4429

Intron Total over all Total

First I need to get the total intronic:

TotAPAIntron=read.table("../data/peakCoverage/APAPeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.Total.Quant.Fixed.fc", stringsAsFactors = F, header = T) %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peakid","chrom", "start", "end", "strand", "geneID"),sep=":") %>% semi_join(peaknumlist, by="peakid") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>%filter(loc=="intron") %>%  dplyr::select(-chrom , -start, -end, -strand, -loc)

TotAPAIntronMelt=melt(TotAPAIntron, id.vars =c( "peakid", "gene"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_")%>% dplyr::select(peakid, gene, Individual, count)


TotAPAIntron_bygene= TotAPAIntronMelt %>% group_by(gene,Individual) %>% summarise(TotalIntronSum=sum(count))

ApaTotal_byloc=TotAPAIntron_bygene %>% inner_join(TotAPA_bygene, by=c("gene", "Individual")) %>% mutate(IntronOverAll=TotalIntronSum/TotalSum) %>% mutate(fraction="total",line=paste("NA", substring(Individual, 2), sep="")) %>% dplyr::select(gene, fraction, line, IntronOverAll) %>% group_by(gene) %>% filter(IntronOverAll!=0) %>%  summarise(MeanIntronoverAll=mean(IntronOverAll)) %>% dplyr::rename("GeneName"=gene)

Join with RNA

totalandRNA=ApaTotal_byloc %>% inner_join(RNA_geneMean, by="GeneName")

totalandRNA$density <- get_density(totalandRNA$MeanIntronoverAll, totalandRNA$Mean_RNA, n = 100)

Plot:

ggplot(totalandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="TotalIntron/TotalAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

summary(lm(data=totalandRNA, MeanIntronoverAll~log10(Mean_RNA)))


Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = totalandRNA)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.27714 -0.12468 -0.04078  0.06442  0.82065 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -0.32230    0.06483  -4.972  8.1e-07 ***
log10(Mean_RNA) -0.13596    0.01486  -9.149  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1779 on 811 degrees of freedom
Multiple R-squared:  0.09355,   Adjusted R-squared:  0.09243 
F-statistic:  83.7 on 1 and 811 DF,  p-value: < 2.2e-16

Same plot with transcription phenotype on bottom:

ApaTotal_byloc_rename=ApaTotal_byloc %>% dplyr::rename("gene"=GeneName)
totalandtranscription=ApaTotal_byloc_rename %>% inner_join(Transcription, by="gene")

totalandtranscription$density <- get_density(totalandtranscription$MeanIntronoverAll, totalandtranscription$Transcription, n = 100)

ggplot(totalandtranscription, aes(x=Transcription, y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="TotalIntron/TotalAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()

Version	Author	Date
7aeba54	brimittleman	2019-05-17

summary(lm(data=totalandtranscription, MeanIntronoverAll~Transcription))


Call:
lm(formula = MeanIntronoverAll ~ Transcription, data = totalandtranscription)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.25236 -0.13687 -0.04965  0.08509  0.73980 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.24548    0.03396   7.227 1.14e-12 ***
Transcription  0.04196    0.06204   0.676    0.499    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1868 on 811 degrees of freedom
Multiple R-squared:  0.0005638, Adjusted R-squared:  -0.0006685 
F-statistic: 0.4575 on 1 and 811 DF,  p-value: 0.499

Put it all together:

(Nuclear intronic/nuclear all)/(total intronic/total all) vs 4su/(4su+RNA)

Nucintron v nuc all:

ApaNuclear_byloc_rename

ApaTotal_byloc_rename

Transcription

fullapa=ApaNuclear_byloc_rename %>% dplyr::rename("NuclearIntronoverall"=MeanIntronoverAll)%>% inner_join(ApaTotal_byloc_rename, by="gene") %>% mutate(fullAPA=NuclearIntronoverall/MeanIntronoverAll) %>% dplyr::select(gene,fullAPA)


#join with transcription 


BothlocPhenoandtranscription=fullapa %>% inner_join(Transcription, by="gene")

BothlocPhenoandtranscription$density <- get_density(BothlocPhenoandtranscription$fullAPA, BothlocPhenoandtranscription$Transcription, n = 100)

ggplot(BothlocPhenoandtranscription, aes(x=Transcription, y=fullAPA))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="log10(NuclearIntron/Nuclearall)/(TotalIntron/TotalAll)", title="Relationship between APA fraction and transcription") + scale_color_viridis()

summary(lm(data=BothlocPhenoandtranscription, log10(fullAPA)~Transcription))


Call:
lm(formula = log10(fullAPA) ~ Transcription, data = BothlocPhenoandtranscription)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.84376 -0.12135 -0.00255  0.12460  0.74230 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.48793    0.03819  12.777  < 2e-16 ***
Transcription -0.29941    0.06975  -4.292 1.98e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.21 on 811 degrees of freedom
Multiple R-squared:  0.02221,   Adjusted R-squared:  0.02101 
F-statistic: 18.42 on 1 and 811 DF,  p-value: 1.981e-05

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] viridis_0.5.1     viridisLite_0.3.0 forcats_0.3.0    
 [4] stringr_1.3.1     dplyr_0.8.0.1     purrr_0.3.2      
 [7] readr_1.3.1       tidyr_0.8.3       tibble_2.1.1     
[10] ggplot2_3.1.1     tidyverse_1.2.1   workflowr_1.3.0  
[13] reshape2_1.4.3   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.3.1     
 [9] pillar_1.3.1     glue_1.3.0       withr_2.1.2      modelr_0.1.2    
[13] readxl_1.1.0     plyr_1.8.4       munsell_0.5.0    gtable_0.2.0    
[17] cellranger_1.1.0 rvest_0.3.2      evaluate_0.12    labeling_0.3    
[21] knitr_1.20       broom_0.5.1      Rcpp_1.0.0       scales_1.0.0    
[25] backports_1.1.2  jsonlite_1.6     fs_1.2.6         gridExtra_2.3   
[29] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[33] rprojroot_1.3-2  cli_1.0.1        tools_3.5.1      magrittr_1.5    
[37] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[41] MASS_7.3-51.1    xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.25.2     compiler_3.5.1

Nuclear fraction and nascent transcription

Briana Mittleman

5/15/2019

Create transcription phenotype

APA phenotype

Join data and plot

Split Nuclear by intronic

Nuclear intronic:

Total UTR

Compare nuclear and total UTR

Intron Nuclear over all nuclear

Same plot with transcription phenotype on bottom:

Intron Total over all Total

Same plot with transcription phenotype on bottom:

Put it all together: