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)
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
-0.36850 -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 |
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$Transcription, n = 100)
ggplot(APAlocationandTranscrption, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="NuclearIntron/TotalUTR + IntronNuclear", title="Relationship between APA fraction and transcription") + scale_color_viridis()
Version | Author | Date |
---|---|---|
5b239b1 | brimittleman | 2019-06-13 |
summary(lm(data=APAlocationandTranscrption, APAValLoc~Transcription))
Call:
lm(formula = APAValLoc ~ Transcription, data = APAlocationandTranscrption)
Residuals:
Min 1Q Median 3Q Max
-0.37748 -0.19928 -0.06962 0.15961 0.73348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.13859 0.02161 6.414 1.6e-10 ***
Transcription 0.33661 0.03889 8.656 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2425 on 3618 degrees of freedom
Multiple R-squared: 0.02029, Adjusted R-squared: 0.02002
F-statistic: 74.93 on 1 and 3618 DF, p-value: < 2.2e-16
Just the ratio:
APAlocationandTranscrption2= Transcription2 %>% inner_join(ApaBothFracLocStand_geneMean_spread2, by="gene")
APAlocationandTranscrption2$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 |
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$Transcription, n = 100)
summary(lm(data=APAUTRandTranscrption, log10(APAValLoc)~log10(Transcription)))
Call:
lm(formula = log10(APAValLoc) ~ log10(Transcription), data = APAUTRandTranscrption)
Residuals:
Min 1Q Median 3Q Max
-0.69129 -0.18916 -0.00344 0.19699 0.90698
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.153728 0.008935 17.20 <2e-16 ***
log10(Transcription) 0.481833 0.029049 16.59 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2526 on 7709 degrees of freedom
Multiple R-squared: 0.03446, Adjusted R-squared: 0.03433
F-statistic: 275.1 on 1 and 7709 DF, p-value: < 2.2e-16
ggplot(APAUTRandTranscrption, aes(x=Transcription, y=APAValLoc))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/RNA", y="NuclearUTR/TotalUTR", title="Relationship between APA fraction and transcription") + scale_color_viridis()
APAUTRandTranscrption2= Transcription %>% inner_join(ApaBothFracUTRStand_geneMean_spread2, by="gene")
APAUTRandTranscrption2$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 |
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$Mean_RNA, n = 100)
Plot:
ggplot(nuclearandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="NuclearIntron/NuclearAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()
Version | Author | Date |
---|---|---|
5b239b1 | brimittleman | 2019-06-13 |
summary(lm(data=nuclearandRNA, MeanIntronoverAll~log10(Mean_RNA)))
Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = nuclearandRNA)
Residuals:
Min 1Q Median 3Q Max
-0.35450 -0.15791 -0.05443 0.11943 0.79506
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.219268 0.033634 -6.519 8.02e-11 ***
log10(Mean_RNA) -0.124686 0.007854 -15.876 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2087 on 3714 degrees of freedom
Multiple R-squared: 0.06355, Adjusted R-squared: 0.0633
F-statistic: 252 on 1 and 3714 DF, p-value: < 2.2e-16
ApaNuclear_byloc_rename=ApaNuclear_byloc %>% dplyr::rename("gene"=GeneName)
nuclearandtranscription=ApaNuclear_byloc_rename %>% inner_join(Transcription, by="gene")
nuclearandtranscription$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
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$Mean_RNA, n = 100)
Plot:
ggplot(totalandRNA, aes(x=log10(Mean_RNA), y=MeanIntronoverAll))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="log10(RNA)", y="TotalIntron/TotalAll", title="Relationship between APA fraction and transcription") + scale_color_viridis()
Version | Author | Date |
---|---|---|
5b239b1 | brimittleman | 2019-06-13 |
summary(lm(data=totalandRNA, MeanIntronoverAll~log10(Mean_RNA)))
Call:
lm(formula = MeanIntronoverAll ~ log10(Mean_RNA), data = totalandRNA)
Residuals:
Min 1Q Median 3Q Max
-0.31993 -0.11685 -0.05411 0.05877 0.91482
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.382135 0.029020 -13.17 <2e-16 ***
log10(Mean_RNA) -0.137356 0.006776 -20.27 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.18 on 3714 degrees of freedom
Multiple R-squared: 0.09961, Adjusted R-squared: 0.09936
F-statistic: 410.9 on 1 and 3714 DF, p-value: < 2.2e-16
ApaTotal_byloc_rename=ApaTotal_byloc %>% dplyr::rename("gene"=GeneName)
totalandtranscription=ApaTotal_byloc_rename %>% inner_join(Transcription, by="gene")
totalandtranscription$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()
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
(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$Transcription, n = 100)
ggplot(BothlocPhenoandtranscription, aes(x=Transcription, y=fullAPA))+ geom_point(aes(color=density)) + geom_smooth(method = "lm") + labs(x="4su/4su+RNA", y="log10(NuclearIntron/Nuclearall)/(TotalIntron/TotalAll)", title="Relationship between APA fraction and transcription") + scale_color_viridis()
Version | Author | Date |
---|---|---|
5b239b1 | brimittleman | 2019-06-13 |
summary(lm(data=BothlocPhenoandtranscription, log10(fullAPA)~Transcription))
Call:
lm(formula = log10(fullAPA) ~ Transcription, data = BothlocPhenoandtranscription)
Residuals:
Min 1Q Median 3Q Max
-0.99008 -0.15847 -0.00751 0.15050 0.83773
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.39298 0.02119 18.546 < 2e-16 ***
Transcription -0.25482 0.03814 -6.681 2.73e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.241 on 3714 degrees of freedom
Multiple R-squared: 0.01187, Adjusted R-squared: 0.01161
F-statistic: 44.63 on 1 and 3714 DF, p-value: 2.733e-11
I want to look at number of PAS in the nuclear fraction against 4su/4su+RNA. This is the transcription phenotype in this analysis
nPASnuc=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.fc", header = F, stringsAsFactors = F, col.names = c("chr", "start", "end","gene", "loc","strand", "peakNum", "meanusage")) %>% group_by(gene) %>% summarise(nPASN=n())
transcriptionPAS=Transcription %>%inner_join(nPASnuc, by="gene")
transcriptionPAS$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