Last updated: 2019-06-25

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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/propeQTLs_explained.Rmd
    Modified:   analysis/signalsiteanalysis.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 8e9e91c brimittleman 2019-06-25 all genes for first plot
html 9dd4b6e brimittleman 2019-06-24 Build site.
Rmd 438b5c0 brimittleman 2019-06-24 add npas
html 5b239b1 brimittleman 2019-06-13 Build site.
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 1.4.0
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(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 
-0.36850 -0.10126  0.00913  0.10928  0.38590 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   0.390735   0.007327   53.33   <2e-16 ***
Transcription 0.279417   0.013667   20.45   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.135 on 7885 degrees of freedom
Multiple R-squared:  0.05034,   Adjusted R-squared:  0.05022 
F-statistic:   418 on 1 and 7885 DF,  p-value: < 2.2e-16

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()

Version Author Date
5b239b1 brimittleman 2019-06-13

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()

Version Author Date
5b239b1 brimittleman 2019-06-13
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()

Version Author Date
5b239b1 brimittleman 2019-06-13

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
5b239b1 brimittleman 2019-06-13
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()

Version Author Date
5b239b1 brimittleman 2019-06-13

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()

Version Author Date
5b239b1 brimittleman 2019-06-13
summary(lm(data=nuclearandRNA, MeanIntronoverAll~log10(Mean_RNA)))

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.35450 -0.15791 -0.05443  0.11943  0.79506 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -0.219268   0.033634  -6.519 8.02e-11 ***
log10(Mean_RNA) -0.124686   0.007854 -15.876  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2087 on 3714 degrees of freedom
Multiple R-squared:  0.06355,   Adjusted R-squared:  0.0633 
F-statistic:   252 on 1 and 3714 DF,  p-value: < 2.2e-16

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()

Version Author Date
5b239b1 brimittleman 2019-06-13
summary(lm(data=nuclearandtranscription, MeanIntronoverAll~Transcription))

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.29672 -0.17586 -0.05984  0.12726  0.69787 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.29093    0.01895  15.350   <2e-16 ***
Transcription  0.03847    0.03412   1.128     0.26    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2156 on 3714 degrees of freedom
Multiple R-squared:  0.0003423, Adjusted R-squared:  7.31e-05 
F-statistic: 1.272 on 1 and 3714 DF,  p-value: 0.2595

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()

Version Author Date
5b239b1 brimittleman 2019-06-13
summary(lm(data=totalandRNA, MeanIntronoverAll~log10(Mean_RNA)))

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.31993 -0.11685 -0.05411  0.05877  0.91482 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -0.382135   0.029020  -13.17   <2e-16 ***
log10(Mean_RNA) -0.137356   0.006776  -20.27   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.18 on 3714 degrees of freedom
Multiple R-squared:  0.09961,   Adjusted R-squared:  0.09936 
F-statistic: 410.9 on 1 and 3714 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
5b239b1 brimittleman 2019-06-13
7aeba54 brimittleman 2019-05-17
summary(lm(data=totalandtranscription, MeanIntronoverAll~Transcription))

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.19577 -0.13018 -0.06396  0.06526  0.80910 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.17703    0.01667  10.617   <2e-16 ***
Transcription  0.04765    0.03002   1.588    0.112    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1897 on 3714 degrees of freedom
Multiple R-squared:  0.0006781, Adjusted R-squared:  0.0004091 
F-statistic:  2.52 on 1 and 3714 DF,  p-value: 0.1125

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()

Version Author Date
5b239b1 brimittleman 2019-06-13
summary(lm(data=BothlocPhenoandtranscription, log10(fullAPA)~Transcription))

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.99008 -0.15847 -0.00751  0.15050  0.83773 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.39298    0.02119  18.546  < 2e-16 ***
Transcription -0.25482    0.03814  -6.681 2.73e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.241 on 3714 degrees of freedom
Multiple R-squared:  0.01187,   Adjusted R-squared:  0.01161 
F-statistic: 44.63 on 1 and 3714 DF,  p-value: 2.733e-11

Does number of PAS correlate with nascent transcription

I want to look at number of PAS in the nuclear fraction against 4su/4su+RNA. This is the transcription phenotype in this analysis

nPASnuc=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.fc", header = F, stringsAsFactors = F, col.names = c("chr", "start", "end","gene", "loc","strand", "peakNum", "meanusage")) %>% group_by(gene) %>% summarise(nPASN=n())

transcriptionPAS=Transcription %>%inner_join(nPASnuc, by="gene")


transcriptionPAS$density <- get_density(transcriptionPAS$nPASN, transcriptionPAS$Transcription, n = 100)



ggplot(transcriptionPAS, aes(x=Transcription, y=nPASN)) + geom_point(aes(color=density)) + scale_color_viridis() + geom_smooth(method = "lm") + labs(y="Number of PAS", title="Number of Nuclear PAS increases as nascent transcription increases") 

Version Author Date
9dd4b6e brimittleman 2019-06-24
summary(lm(data=transcriptionPAS, nPASN~Transcription))

Call:
lm(formula = nPASN ~ Transcription, data = transcriptionPAS)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8788 -1.2951 -0.3395  1.0042  8.4575 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.38595    0.09275   4.161  3.2e-05 ***
Transcription  4.96412    0.17301  28.692  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.709 on 7885 degrees of freedom
Multiple R-squared:  0.09454,   Adjusted R-squared:  0.09442 
F-statistic: 823.2 on 1 and 7885 DF,  p-value: < 2.2e-16

Try for total fraction:

nPAStot=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Total.5perc.fc", header = F, stringsAsFactors = F, col.names = c("chr", "start", "end","gene", "loc","strand", "peakNum", "meanusage")) %>% group_by(gene) %>% summarise(nPAST=n())

transcriptionTOTPAS=Transcription %>%inner_join(nPAStot, by="gene")


transcriptionTOTPAS$density <- get_density(transcriptionTOTPAS$nPAST, transcriptionTOTPAS$Transcription, n = 100)



ggplot(transcriptionTOTPAS, aes(x=Transcription, y=nPAST)) + geom_point(aes(color=density)) + scale_color_viridis() + geom_smooth(method = "lm") + labs(y="Number of PAS", title="Number of Total PAS increases as nascent transcription increases") 

Version Author Date
9dd4b6e brimittleman 2019-06-24
summary(lm(data=transcriptionTOTPAS, nPAST~Transcription))

Call:
lm(formula = nPAST ~ Transcription, data = transcriptionTOTPAS)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3359 -1.0608 -0.2954  0.8024  6.7777 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.15080    0.07847   1.922   0.0547 .  
Transcription  4.62411    0.14637  31.591   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.446 on 7885 degrees of freedom
Multiple R-squared:  0.1124,    Adjusted R-squared:  0.1122 
F-statistic:   998 on 1 and 7885 DF,  p-value: < 2.2e-16

Look at the difference in PAS number:

nPASall= nPAStot %>% inner_join(nPASnuc, by="gene") %>% mutate(Difference= nPASN-nPAST) 

transcriptionallPAS=Transcription %>%inner_join(nPASall, by="gene")

transcriptionallPAS$density <- get_density(transcriptionallPAS$Difference, transcriptionallPAS$Transcription, n = 100)

ggplot(transcriptionallPAS, aes(x=Transcription, y=Difference)) + geom_point(aes(color=density)) + scale_color_viridis() + geom_smooth(method = "lm") + labs(y="Diffference in number of PAS", title="Number of Total PAS increases as nascent transcription increases") 

Version Author Date
9dd4b6e brimittleman 2019-06-24
summary(lm(data=transcriptionallPAS, Difference~Transcription))

Call:
lm(formula = Difference ~ Transcription, data = transcriptionallPAS)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4613 -0.4299 -0.3865  0.5733  6.5790 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.23515    0.05423   4.336 1.47e-05 ***
Transcription  0.34001    0.10116   3.361  0.00078 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9991 on 7885 degrees of freedom
Multiple R-squared:  0.001431,  Adjusted R-squared:  0.001304 
F-statistic:  11.3 on 1 and 7885 DF,  p-value: 0.0007798

This has a small positive correlation.


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_1.4.0  
[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