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
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Modified: code/apaQTLCorrectPvalMakeQQ.R
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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
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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)
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)
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))
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()
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))
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()
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()
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$Mean_RNA, n = 100)
Plot:
ggplot(nuclearandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="NuclearIntron/NuclearAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()
summary(lm(data=nuclearandRNA, MeanIntronoverAll~log10(Mean_RNA)))
Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = nuclearandRNA)
Residuals:
Min 1Q Median 3Q Max
-0.47913 -0.12703 -0.01765 0.12618 0.55620
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.18933 0.06799 2.784 0.00549 **
log10(Mean_RNA) -0.06897 0.01559 -4.425 1.1e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1865 on 811 degrees of freedom
Multiple R-squared: 0.02357, Adjusted R-squared: 0.02237
F-statistic: 19.58 on 1 and 811 DF, p-value: 1.098e-05
ApaNuclear_byloc_rename=ApaNuclear_byloc %>% dplyr::rename("gene"=GeneName)
nuclearandtranscription=ApaNuclear_byloc_rename %>% inner_join(Transcription, by="gene")
nuclearandtranscription$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
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$Mean_RNA, n = 100)
Plot:
ggplot(totalandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="TotalIntron/TotalAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()
summary(lm(data=totalandRNA, MeanIntronoverAll~log10(Mean_RNA)))
Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = totalandRNA)
Residuals:
Min 1Q Median 3Q Max
-0.27714 -0.12468 -0.04078 0.06442 0.82065
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.32230 0.06483 -4.972 8.1e-07 ***
log10(Mean_RNA) -0.13596 0.01486 -9.149 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1779 on 811 degrees of freedom
Multiple R-squared: 0.09355, Adjusted R-squared: 0.09243
F-statistic: 83.7 on 1 and 811 DF, p-value: < 2.2e-16
ApaTotal_byloc_rename=ApaTotal_byloc %>% dplyr::rename("gene"=GeneName)
totalandtranscription=ApaTotal_byloc_rename %>% inner_join(Transcription, by="gene")
totalandtranscription$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
(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