Proportion eQTLs explained
Briana Mittleman
6/7/2019
Last updated: 2019-06-26
Checks: 7 0
Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/NuclearSpecAPAqtl.Rmd
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/overlapapaqtlsandeqtls.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
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c53925a | brimittleman | 2019-06-26 | add graph labels |
| html | 06de9df | brimittleman | 2019-06-26 | Build site. |
| Rmd | ec9c1d6 | brimittleman | 2019-06-26 | add direction concordance plots |
| html | ec8d7dc | brimittleman | 2019-06-26 | Build site. |
| Rmd | 52e46bc | brimittleman | 2019-06-26 | add example plot code |
| html | 0fae25e | brimittleman | 2019-06-20 | Build site. |
| Rmd | eb847c1 | brimittleman | 2019-06-20 | add analysis by pval |
| html | ca379ce | brimittleman | 2019-06-13 | Build site. |
| Rmd | 2fd2b27 | brimittleman | 2019-06-13 | fix bug |
| html | b907ac1 | brimittleman | 2019-06-12 | Build site. |
| Rmd | 178c5dc | brimittleman | 2019-06-12 | new geno |
| html | 6b164c8 | brimittleman | 2019-06-07 | Build site. |
| Rmd | b39620d | brimittleman | 2019-06-07 | add bonfor results |
| html | 458e494 | brimittleman | 2019-06-07 | Build site. |
| Rmd | 32091ee | brimittleman | 2019-06-07 | more prop explained to new analysis |
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(workflowr)This is workflowr version 1.4.0
Run ?workflowr for help getting startedlibrary(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithsI need to fix the explained_FDR10.sort.txt and unexplained_FDR10.sort.txt files because right now this file has multiple genes per snp.
python fixExandUnexeQTL.py ../data/Li_eQTLs/explained_FDR10.sort.txt ../data/Li_eQTLs/explained_FDR10.sort_FIXED.txt
python fixExandUnexeQTL.py ../data/Li_eQTLs/unexplained_FDR10.sort.txt ../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txtThere are 1195 explained and 814 unexplained eQTLs. I will next look at each of these in my apadata.
Convert nominal results to have snps rather than rsids:
python convertNominal2SNPLOC.py Total
python convertNominal2SNPLOC.py Nuclearmkdir ../data/overlapeQTL_try2
sbatch run_getapafromeQTL.sh
total
I can group the unexplained by gene and snp then I can ask if there is at least 1 significat peak for each of these.
I will use the bonforoni correction here and multiply the pvalue by the number of peaks in the gene:snp association.
nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
totalapaUnexplained=read.table("../data/overlapeQTL_try2/apaTotal_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames)
totalapaUnexplained=totalapaUnexplained %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>% mutate(nPeaks=n(), adjPval=pval* nPeaks)%>% dplyr::slice(which.min(adjPval))
totalapaUnexplained_sig= totalapaUnexplained %>% filter(adjPval<.05)Look at distribution of these pvals:
ggplot(totalapaUnexplained, aes(x=adjPval)) + geom_histogram(bins=50)
Proportion explained:
nrow(totalapaUnexplained_sig)/nrow(totalapaUnexplained)[1] 0.1632653I tested 588 unexplained eQTLs in the total fraction and 96 have a bonforoni corrected significant peak.
Compare to explained eQTLS:
totalapaexplained=read.table("../data/overlapeQTL_try2/apaTotal_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>% mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))
totalapaexplained_sig= totalapaexplained %>% filter(adjPval<.05)
nrow(totalapaexplained_sig)/nrow(totalapaexplained)[1] 0.1304878I am testing 820 explained eQTLs and of those 107 have a bonforoni corrected significant peak.
difference of proportions:
prop.test(x=c(nrow(totalapaUnexplained_sig),nrow(totalapaexplained_sig)), n=c(nrow(totalapaUnexplained),nrow(totalapaexplained)))
2-sample test for equality of proportions with continuity
correction
data: c(nrow(totalapaUnexplained_sig), nrow(totalapaexplained_sig)) out of c(nrow(totalapaUnexplained), nrow(totalapaexplained))
X-squared = 2.722, df = 1, p-value = 0.09898
alternative hypothesis: two.sided
95 percent confidence interval:
-0.00641871 0.07197371
sample estimates:
prop 1 prop 2
0.1632653 0.1304878 ggplot(totalapaUnexplained_sig,aes(x=loc)) + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(y="Proportion", title = "Total apaQTLs explaining eQTLs")Warning: Ignoring unknown parameters: binwidth, bins, pad
totalapaUnexplained_sig_loc= totalapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocTotalUn=n()) %>% mutate(propTotalUn=nLocTotalUn/nrow(totalapaUnexplained_sig))
totalapaexplained_sig_loc= totalapaexplained_sig %>% group_by(loc) %>% summarise(nLocTotalEx=n()) %>% mutate(propTotalEx=nLocTotalEx/nrow(totalapaexplained_sig))
BothTotalLoc=totalapaUnexplained_sig_loc %>% full_join(totalapaexplained_sig_loc,by="loc") %>% replace_na(list(propTotalUn = 0, nLocTotalUn = 0,propTotalEx=0,nLocTotalEx=0 ))
BothTotalLoc# A tibble: 5 x 5
loc nLocTotalUn propTotalUn nLocTotalEx propTotalEx
<chr> <dbl> <dbl> <dbl> <dbl>
1 cds 7 0.0729 8 0.0748
2 end 9 0.0938 7 0.0654
3 intron 17 0.177 20 0.187
4 utr3 59 0.615 70 0.654
5 utr5 4 0.0417 2 0.0187nuclear
nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>% mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))
nuclearapaUnexplained_sig= nuclearapaUnexplained %>% filter(adjPval<.05)
nrow(nuclearapaUnexplained_sig)/nrow(nuclearapaUnexplained)[1] 0.1649832I tested 594 unexplained eQTLs in the nuclear fraction and 98 have a bonforoni corrected significant peak.
nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>% mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))
nuclearapaexplained_sig= nuclearapaexplained %>% filter(adjPval<.05)
nrow(nuclearapaexplained_sig)/nrow(nuclearapaexplained)[1] 0.13269I tested 829 explained eQTLs in the nuclear fraction and 110 have a nominally significant peak. difference of proportions:
prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(nuclearapaexplained)))
2-sample test for equality of proportions with continuity
correction
data: c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(nuclearapaexplained))
X-squared = 2.6386, df = 1, p-value = 0.1043
alternative hypothesis: two.sided
95 percent confidence interval:
-0.006890426 0.071476780
sample estimates:
prop 1 prop 2
0.1649832 0.1326900 ggplot(nuclearapaUnexplained_sig,aes(x=loc)) + geom_histogram(stat="count",aes(y=..count../sum(..count..))) + labs(title = "Nuclear apaQTLs explaining eQTLs", y="Proportion")Warning: Ignoring unknown parameters: binwidth, bins, pad
nuclearapaUnexplained_sig_loc= nuclearapaUnexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearUn=n()) %>% mutate(propnuclearUn=nLocnuclearUn/nrow(nuclearapaUnexplained_sig))
nuclearapaexplained_sig_loc= nuclearapaexplained_sig %>% group_by(loc) %>% summarise(nLocnuclearEx=n()) %>% mutate(propnuclearEx=nLocnuclearEx/nrow(nuclearapaexplained_sig))
BothnuclearLoc=nuclearapaUnexplained_sig_loc %>% full_join(nuclearapaexplained_sig_loc,by="loc") %>% replace_na(list(propnuclearUn = 0, nLocnuclearUn = 0,propnuclearEx=0,nLocnuclearEx=0 ))
BothnuclearLoc# A tibble: 5 x 5
loc nLocnuclearUn propnuclearUn nLocnuclearEx propnuclearEx
<chr> <dbl> <dbl> <dbl> <dbl>
1 cds 4 0.0408 3 0.0273
2 end 10 0.102 9 0.0818
3 intron 18 0.184 33 0.3
4 utr3 66 0.673 63 0.573
5 utr5 0 0 2 0.0182prop.test(x=c(18,33), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))
2-sample test for equality of proportions with continuity
correction
data: c(18, 33) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 3.1869, df = 1, p-value = 0.07423
alternative hypothesis: two.sided
95 percent confidence interval:
-0.240913267 0.008260206
sample estimates:
prop 1 prop 2
0.1836735 0.3000000 prop.test(x=c(66,63), n=c(nrow(nuclearapaUnexplained_sig),nrow(nuclearapaexplained_sig)))
2-sample test for equality of proportions with continuity
correction
data: c(66, 63) out of c(nrow(nuclearapaUnexplained_sig), nrow(nuclearapaexplained_sig))
X-squared = 1.8258, df = 1, p-value = 0.1766
alternative hypothesis: two.sided
95 percent confidence interval:
-0.03992433 0.24140856
sample estimates:
prop 1 prop 2
0.6734694 0.5727273 total v nuclear
prop.test(x=c(nrow(nuclearapaUnexplained_sig),nrow(totalapaUnexplained_sig)), n=c(nrow(nuclearapaUnexplained),nrow(totalapaUnexplained)))
2-sample test for equality of proportions with continuity
correction
data: c(nrow(nuclearapaUnexplained_sig), nrow(totalapaUnexplained_sig)) out of c(nrow(nuclearapaUnexplained), nrow(totalapaUnexplained))
X-squared = 1.4301e-06, df = 1, p-value = 0.999
alternative hypothesis: two.sided
95 percent confidence interval:
-0.04220475 0.04564046
sample estimates:
prop 1 prop 2
0.1649832 0.1632653 Differences in proportion by location
allLocProp=BothnuclearLoc %>% full_join(BothTotalLoc, by="loc") %>% select(loc,propnuclearUn,propnuclearEx,propTotalUn,propTotalEx )
allLocPropmelt= melt(allLocProp, id.vars = "loc") %>% mutate(Fraction=ifelse(grepl("Total", variable), "Total", "Nuclear"),eQTL=ifelse(grepl("Un", variable), "Unexplained", "Explained"))
ggplot(allLocPropmelt,aes(x=loc, fill=eQTL, y=value)) + geom_histogram(stat="identity", position = "dodge") + facet_grid(~Fraction)+ labs(y="Proportion of PAS", title="apaQTLs overlaping eQTLs by PAS location")Warning: Ignoring unknown parameters: binwidth, bins, pad
This is a very stringent test. A less stringent way to get an upper bound would be to make an informed decision about which peak to use. This will make it so I am only testing one PAS per gene.
Vary the pvalue cuttoff
To test if .05 is a good cuttoff for this analysis I will create a function that computes the overlap at different cutoffs. I will go from .01 to .5 by .05
totalapaUnexplained totalapaexplained
nuclearapaUnexplained nuclearapaexplained
prop_overlap=function(status, fraction, cutoff){
if (fraction=="Total"){
if (status=="Explained"){
file=totalapaexplained
sig=file %>% filter(adjPval<=cutoff)
proportion=round(nrow(sig)/nrow(file),digits=2)
}else {
file=totalapaUnexplained
sig=file %>% filter(adjPval<=cutoff)
proportion=round(nrow(sig)/nrow(file),digits=2)
}
} else{
if (status=="Explained"){
file=nuclearapaexplained
sig=file %>% filter(adjPval<=cutoff)
proportion=round(nrow(sig)/nrow(file),digits=2)
}else {
file=nuclearapaUnexplained
sig=file %>% filter(adjPval<=cutoff)
proportion=round(nrow(sig)/nrow(file),digits=2)
}
}
return(proportion)
}cutoffs=c(0.001,0.01,0.02,0.03,0.04,0.05,0.1,0.2,0.3,0.4,0.5)
TotalExplained_Proportions=c()
for(i in cutoffs){
TotalExplained_Proportions=c( TotalExplained_Proportions, prop_overlap("Explained", "Total", i))
}
TotalExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Total", 11)))
TotalUnexplained_Proportions=c()
for(i in cutoffs){
TotalUnexplained_Proportions=c(TotalUnexplained_Proportions, prop_overlap("Unexplained", "Total", i))
}
TotalUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Total", 11)))
NuclearExplained_Proportions=c()
for(i in cutoffs){
NuclearExplained_Proportions=c( NuclearExplained_Proportions, prop_overlap("Explained", "Nuclear", i))
}
NuclearExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Nuclear", 11)))
NuclearUnexplained_Proportions=c()
for(i in cutoffs){
NuclearUnexplained_Proportions=c( NuclearUnexplained_Proportions, prop_overlap("Unexplained", "Nuclear", i))
}
NuclearUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Nuclear", 11)))
AllPropDF=bind_rows(TotalExplained_ProportionsDF,TotalUnexplained_ProportionsDF,NuclearExplained_ProportionsDF,NuclearUnexplained_ProportionsDF)Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characterWarning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vectorWarning in bind_rows_(x, .id): Unequal factor levels: coercing to characterWarning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vectorWarning in bind_rows_(x, .id): Unequal factor levels: coercing to characterWarning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vectorAllPropDF$Prop=as.numeric(AllPropDF$Prop)Plot this:
ggplot(AllPropDF, aes(x=cutoffs, y=Prop, fill=Status)) + geom_bar(position = "dodge", stat="identity") + facet_grid(~Fraction) + geom_text(aes(label=Prop))
Concordance of directions for intronic pas usage and eQTL
I want to look at the intronic pas and the eQTLs. To do this I want to look at a correaltion of effect sizes for the eQTLs and and intronic PAS.
The eQTL information is in ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName.txt. I need to converte the RSID into snp loc.
python eQTL_switch2snploc.pyprepare eQTL:
eQTLeffect=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName_snploc.txt", stringsAsFactors = F, col.names = c("gene","snp","dist", "pval", "eQTL_es")) %>% select(gene, snp, eQTL_es)total:
alleQTLS_total=bind_rows(totalapaUnexplained, totalapaexplained) %>% filter(loc=="intron") %>% inner_join(eQTLeffect, by=c("gene","snp"))
ggplot(alleQTLS_total,aes(x=eQTL_es, y=slope)) + geom_point() + geom_smooth(method = "lm") +geom_text(y=-1, x=-1.5, label="slope: -0.21 p-value: .01, r2=0.06") + labs(title="Total apa effect sizes vs eQTL eqtl effect sizes", y="Total apaQTL effect size",x="eQTL effect size")
| Version | Author | Date |
|---|---|---|
| 06de9df | brimittleman | 2019-06-26 |
summary(lm(alleQTLS_total$slope ~alleQTLS_total$eQTL_es))
Call:
lm(formula = alleQTLS_total$slope ~ alleQTLS_total$eQTL_es)
Residuals:
Min 1Q Median 3Q Max
-1.12882 -0.29223 -0.00704 0.29545 1.11825
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04771 0.04766 1.001 0.320
alleQTLS_total$eQTL_es -0.20567 0.07919 -2.597 0.011 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.447 on 87 degrees of freedom
Multiple R-squared: 0.07196, Adjusted R-squared: 0.0613
F-statistic: 6.746 on 1 and 87 DF, p-value: 0.01103Nuclear:
alleQTLS_nuclear=bind_rows(nuclearapaUnexplained,nuclearapaexplained) %>% filter(loc=="intron") %>% inner_join(eQTLeffect, by=c("gene","snp"))
ggplot(alleQTLS_nuclear,aes(x=eQTL_es, y=slope)) + geom_point() + geom_smooth(method = "lm") +geom_text(y=-1, x=-1.5, label="slope: -0.18 p-value: .005, r2=0.05") + labs(title="Nuclear apa effect sizes vs eQTL eqtl effect sizes", y="Nuclear apaQTL effect size",x="eQTL effect size")
| Version | Author | Date |
|---|---|---|
| 06de9df | brimittleman | 2019-06-26 |
summary(lm(alleQTLS_nuclear$slope ~alleQTLS_nuclear$eQTL_es))
Call:
lm(formula = alleQTLS_nuclear$slope ~ alleQTLS_nuclear$eQTL_es)
Residuals:
Min 1Q Median 3Q Max
-1.18775 -0.26653 -0.03384 0.29319 1.16355
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.009364 0.036972 -0.253 0.80041
alleQTLS_nuclear$eQTL_es -0.178352 0.062232 -2.866 0.00478 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4455 on 144 degrees of freedom
Multiple R-squared: 0.05396, Adjusted R-squared: 0.04739
F-statistic: 8.213 on 1 and 144 DF, p-value: 0.004782Examples for overlap:
unexplained_snps=read.table("../data/Li_eQTLs/unexplained_FDR10.sort_FIXED.txt", col.names = c("chr", "loc", "gene"),stringsAsFactors = F)
totQTL=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.bed", header = T, stringsAsFactors = F, col.names = c("chr", "bedstart","loc","ID", "score", "strand"))
nucQTL=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.bed", stringsAsFactors = F, header = T, col.names = c("chr", "bedstart","loc","ID", "score", "strand"))Overlap:
totQTL_unex=totQTL %>% inner_join(unexplained_snps, by=c("chr", "loc"))
nucQTL_unex=nucQTL %>% inner_join(unexplained_snps, by=c("chr", "loc"))totQTL_unex chr bedstart loc ID score strand
1 19 57706377 57706378 ZNF264:peak67836:intron -0.732823 .
2 20 1350708 1350709 FKBP1A:peak80048:utr3 -0.565488 .
3 2 197855151 197855152 ANKRD44:peak77452:intron -0.976492 .
4 7 6497500 6497501 KDELR2:peak119697:utr3 0.962082 .
5 7 6497500 6497501 KDELR2:peak119699:utr3 -1.013800 .
gene
1 ZNF264
2 FKBP1A
3 ANKRD44
4 KDELR2
5 KDELR2nucQTL_unex chr bedstart loc ID score strand
1 10 124693586 124693587 C10orf88:peak19881:intron 1.314860 .
2 19 57706377 57706378 ZNF264:peak67836:intron -0.541008 .
3 4 44702719 44702720 GUF1:peak98095:utr3 0.837665 .
4 4 44702719 44702720 GUF1:peak98096:utr3 -1.361180 .
5 7 156760698 156760699 NOM1:peak127488:utr3 0.972826 .
gene
1 C10orf88
2 ZNF264
3 GNPDA2
4 GNPDA2
5 NOM1Make a plot for KDELR2 7:6497501
genohead=as.data.frame(read.table("../data/ExampleQTLPlots/genotypeHeader.txt", stringsAsFactors = F, header = F)[,10:128] %>% t())
colnames(genohead)=c("header")
genotype=as.data.frame(read.table("../data/ExampleQTLPlots/KDELR2_TotalPeaksGenotype.txt", stringsAsFactors = F, header = F) [,10:128] %>% t())
full_geno=bind_cols(Ind=genohead$header, dose=genotype$V1) %>% mutate(numdose=round(dose), genotype=ifelse(numdose==0, "TT", ifelse(numdose==1, "TG", "GG")))
RNAhead=as.data.frame(read.table("../data/molPhenos/RNAhead.txt", stringsAsFactors = F, header = F)[,5:73] %>% t())
RNApheno=as.data.frame(read.table("../data/molPhenos/RNA_KDELr2.txt", stringsAsFactors = F, header = F) [,5:73] %>% t())
full_pheno=bind_cols(Ind=RNAhead$V1, Expression=RNApheno$V1)
allRNA=full_geno %>% inner_join(full_pheno, by="Ind")Warning: Column `Ind` joining factors with different levels, coercing to
character vectorallRNA$genotype=as.factor(allRNA$genotype)Ref,T Alt= G
ggplot(allRNA, aes(x=genotype, y=Expression,group=genotype, fill=genotype)) + geom_boxplot() + geom_jitter()+scale_fill_brewer(palette = "YlOrRd") + labs(title="Unexplained eQTL: KDELR2 - rs6962012")
| Version | Author | Date |
|---|---|---|
| 06de9df | brimittleman | 2019-06-26 |
Make locus zoom
mkdir ../data/locusZoompeak119699 KDELR2 ENSG00000136240.5
grep peak119699 ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt > ../data/locusZoom/TotalAPA.peak119699.KDELR2.nomNuc.txt
grep ENSG00000136240.5 ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.txt > ../data/locusZoom/RNA.KDELR2.txt
APATotal_KDELR2_LZ=read.table("../data/locusZoom/TotalAPA.peak119699.KDELR2.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(APATotal_KDELR2_LZ,"../data/locusZoom/apaTotalKDELR2_LZ.txt", col.names = T, row.names = F, quote = F)
RNA_KDELR2_LZ=read.table("../data/locusZoom/RNA.KDELR2.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(RNA_KDELR2_LZ,"../data/locusZoom/RNAKDELR2_LZ.txt", col.names = T, row.names = F, quote = F)Use locuszoom.org
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] reshape2_1.4.3 workflowr_1.4.0 forcats_0.3.0 stringr_1.3.1
[5] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3
[9] tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 RColorBrewer_1.1-2 cellranger_1.1.0
[4] pillar_1.3.1 compiler_3.5.1 git2r_0.25.2
[7] plyr_1.8.4 tools_3.5.1 digest_0.6.18
[10] lubridate_1.7.4 jsonlite_1.6 evaluate_0.12
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-38
[16] pkgconfig_2.0.2 rlang_0.3.1 cli_1.0.1
[19] rstudioapi_0.10 yaml_2.2.0 haven_1.1.2
[22] withr_2.1.2 xml2_1.2.0 httr_1.3.1
[25] knitr_1.20 hms_0.4.2 generics_0.0.2
[28] fs_1.2.6 rprojroot_1.3-2 grid_3.5.1
[31] tidyselect_0.2.5 glue_1.3.0 R6_2.3.0
[34] fansi_0.4.0 readxl_1.1.0 rmarkdown_1.10
[37] modelr_0.1.2 magrittr_1.5 whisker_0.3-2
[40] backports_1.1.2 scales_1.0.0 htmltools_0.3.6
[43] rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2
[46] labeling_0.3 utf8_1.1.4 stringi_1.2.4
[49] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1
[52] crayon_1.3.4