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
    Modified:   docs/figure/DiffIsoAnalysis.Rmd/figure1Emain-1.pdf
    Modified:   docs/figure/DiffIsoAnalysis.Rmd/figure1Esubset-1.pdf
    Deleted:    reads_graphs.Rmd

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
html 00fe2b4 brimittleman 2019-07-26 Build site.
Rmd cee6ce0 brimittleman 2019-07-26 get pvalues form <-16 tests
html 1fea2ed brimittleman 2019-06-25 Build site.
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 
-0.36495 -0.10143  0.00799  0.10823  0.38633 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)   0.391266   0.007299   53.61   <2e-16 ***
Transcription 0.268111   0.013614   19.69   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1343 on 7879 degrees of freedom
Multiple R-squared:  0.04691,   Adjusted R-squared:  0.04679 
F-statistic: 387.8 on 1 and 7879 DF,  p-value: < 2.2e-16
cor.test(x=APAandTranscrption$Transcription,y=APAandTranscrption$APAVal)$p.value
[1] 2.560137e-84

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
1fea2ed brimittleman 2019-06-25
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 
-0.3265 -0.1894 -0.0686  0.1418  0.7199 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.13634    0.02166   6.294 3.49e-10 ***
Transcription  0.30874    0.03889   7.939 2.74e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2353 on 3401 degrees of freedom
Multiple R-squared:  0.0182,    Adjusted R-squared:  0.01791 
F-statistic: 63.03 on 1 and 3401 DF,  p-value: 2.743e-15

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 
-1.56425 -0.42530 -0.04272  0.37494  2.55395 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -0.51548    0.01116 -46.208   <2e-16 ***
log10(Transcription)  0.49074    0.05312   9.238   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5932 on 3401 degrees of freedom
Multiple R-squared:  0.02448,   Adjusted R-squared:  0.02419 
F-statistic: 85.34 on 1 and 3401 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 
-0.70017 -0.18961 -0.00315  0.19567  0.91050 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.152115   0.009048   16.81   <2e-16 ***
log10(Transcription) 0.486492   0.029505   16.49   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2518 on 7709 degrees of freedom
Multiple R-squared:  0.03407,   Adjusted R-squared:  0.03394 
F-statistic: 271.9 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 
-0.45859 -0.08654  0.01628  0.10173  0.37084 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -0.235769   0.004561   -51.7   <2e-16 ***
log10(Transcription)  0.269145   0.014872    18.1   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1269 on 7709 degrees of freedom
Multiple R-squared:  0.04075,   Adjusted R-squared:  0.04063 
F-statistic: 327.5 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
1fea2ed brimittleman 2019-06-25
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 
-0.34199 -0.15170 -0.05155  0.11068  0.80839 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -0.234585   0.033994  -6.901 6.11e-12 ***
log10(Mean_RNA) -0.125270   0.007938 -15.780  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.203 on 3489 degrees of freedom
Multiple R-squared:  0.06662,   Adjusted R-squared:  0.06635 
F-statistic:   249 on 1 and 3489 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
1fea2ed brimittleman 2019-06-25
5b239b1 brimittleman 2019-06-13
summary(lm(data=nuclearandtranscription, MeanIntronoverAll~Transcription))

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.28328 -0.16653 -0.05979  0.11668  0.70545 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.29220    0.01902  15.366   <2e-16 ***
Transcription  0.01262    0.03416   0.369    0.712    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2101 on 3489 degrees of freedom
Multiple R-squared:  3.91e-05,  Adjusted R-squared:  -0.0002475 
F-statistic: 0.1364 on 1 and 3489 DF,  p-value: 0.7119

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
1fea2ed brimittleman 2019-06-25
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 
-0.31597 -0.11142 -0.05084  0.05668  0.91908 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     -0.387446   0.029256  -13.24   <2e-16 ***
log10(Mean_RNA) -0.137665   0.006832  -20.15   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1747 on 3489 degrees of freedom
Multiple R-squared:  0.1042,    Adjusted R-squared:  0.104 
F-statistic:   406 on 1 and 3489 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
1fea2ed brimittleman 2019-06-25
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 
-0.19479 -0.12577 -0.06159  0.06326  0.80891 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.18698    0.01671  11.193   <2e-16 ***
Transcription  0.02206    0.03001   0.735    0.462    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1846 on 3489 degrees of freedom
Multiple R-squared:  0.0001549, Adjusted R-squared:  -0.0001316 
F-statistic: 0.5407 on 1 and 3489 DF,  p-value: 0.4622

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
1fea2ed brimittleman 2019-06-25
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 
-1.04512 -0.15891 -0.00646  0.15119  0.97338 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.36597    0.02186  16.741  < 2e-16 ***
Transcription -0.23538    0.03927  -5.995 2.25e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2416 on 3489 degrees of freedom
Multiple R-squared:  0.01019,   Adjusted R-squared:  0.009911 
F-statistic: 35.94 on 1 and 3489 DF,  p-value: 2.248e-09

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 
-3.8031 -1.2709 -0.3399  0.9833  8.3229 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.30831    0.09171   3.362 0.000779 ***
Transcription  4.96629    0.17108  29.030  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.687 on 7879 degrees of freedom
Multiple R-squared:  0.09662,   Adjusted R-squared:  0.09651 
F-statistic: 842.7 on 1 and 7879 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 
-3.2809 -1.0575 -0.2933  0.7959  7.0142 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.14210    0.07792   1.824   0.0682 .  
Transcription  4.57287    0.14534  31.462   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.434 on 7879 degrees of freedom
Multiple R-squared:  0.1116,    Adjusted R-squared:  0.1115 
F-statistic: 989.9 on 1 and 7879 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 
-5.3758 -0.3925 -0.3446  0.5983  5.6461 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.16621    0.05165   3.218   0.0013 ** 
Transcription  0.39342    0.09634   4.084 4.48e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9503 on 7879 degrees of freedom
Multiple R-squared:  0.002112,  Adjusted R-squared:  0.001985 
F-statistic: 16.68 on 1 and 7879 DF,  p-value: 4.476e-05

This has a small positive correlation.

RNA decay

Do this with Athma’s decay.

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

decay_geneNames=decay %>% inner_join(geneNames, by="gene_id") %>% dplyr::select(GeneName, contains("RNAdecay"))

decay_geneNames_long=melt(decay_geneNames,id.vars = "GeneName", value.name = "RNA_Decay", variable.name = "Decay_Ind") %>% separate(Decay_Ind, into=c("type", "ind"), sep="_") %>% mutate(line=paste("NA" , ind, sep="")) %>% dplyr::select(GeneName, line, RNA_Decay) %>% dplyr::rename( "gene"=GeneName)
APAandDecay=decay_geneNames_long %>% inner_join(ApaBothFrac_melt, by=c('gene', 'line'))
ngenes=APAandDecay %>% dplyr::select(gene) %>% unique() %>% nrow()
ngenes
[1] 7881
summary(lm(data=APAandDecay, APA_val~RNA_Decay))

Call:
lm(formula = APA_val ~ RNA_Decay, data = APAandDecay)

Residuals:
   Min     1Q Median     3Q    Max 
  -849   -454   -356   -145 809821 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  512.257      4.058  126.24   <2e-16 ***
RNA_Decay    388.772     24.162   16.09   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3492 on 756574 degrees of freedom
Multiple R-squared:  0.0003421, Adjusted R-squared:  0.0003407 
F-statistic: 258.9 on 1 and 756574 DF,  p-value: < 2.2e-16
ngenes=APAandDecay %>% dplyr::select(gene) %>% unique() %>% nrow()
ngenes
[1] 7881
decay_byGene= decay_geneNames_long %>% group_by(gene) %>% summarise(MeanDecay=mean(RNA_Decay))
APAandDecayMean=decay_byGene %>% inner_join(ApaBothFracStand_geneMean_spread, by=c('gene'))



APAandDecayMean$density <- get_density(APAandDecayMean$APAVal, APAandDecayMean$MeanDecay, n = 100)

ggplot(APAandDecayMean, aes(x=MeanDecay, y=APAVal)) + geom_point(aes(color=density)) + geom_smooth(method="lm") + labs(x="relative Decay", y="Nuclear/(Total+Nuclear)", title="Relationship between Nuclear proportion and RNA decay")+ scale_color_viridis()

summary(lm(data=APAandDecayMean, APAVal~MeanDecay))

Call:
lm(formula = APAVal ~ MeanDecay, data = APAandDecayMean)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.36888 -0.10414  0.00977  0.11337  0.35047 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.530569   0.001577 336.413  < 2e-16 ***
MeanDecay   -0.054487   0.012607  -4.322 1.56e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1374 on 7879 degrees of freedom
Multiple R-squared:  0.002365,  Adjusted R-squared:  0.002239 
F-statistic: 18.68 on 1 and 7879 DF,  p-value: 1.564e-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_1.4.0  
[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_0.4.0     
 [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       highr_0.7        broom_0.5.1      Rcpp_1.0.0      
[25] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[29] gridExtra_2.3    hms_0.4.2        digest_0.6.18    stringi_1.2.4   
[33] grid_3.5.1       rprojroot_1.3-2  cli_1.1.0        tools_3.5.1     
[37] magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[49] R6_2.3.0         nlme_3.1-137     git2r_0.25.2     compiler_3.5.1