Last updated: 2019-09-06
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/Readdistagainstfeatures.Rmd
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html | 272b0b4 | brimittleman | 2019-07-08 | Build site. |
Rmd | b1e6dd1 | brimittleman | 2019-07-08 | update ptt analysis |
html | 429432a | brimittleman | 2019-07-02 | Build site. |
Rmd | fe7b5dc | brimittleman | 2019-07-02 | add eQTL overlap |
html | dad7bd8 | brimittleman | 2019-07-02 | Build site. |
Rmd | fe41a93 | brimittleman | 2019-07-02 | add prop of tested genes |
html | 2a63cde | brimittleman | 2019-07-01 | Build site. |
Rmd | 8d36f9b | brimittleman | 2019-07-01 | add res |
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Rmd | 6db6003 | brimittleman | 2019-07-01 | add qtl code |
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Rmd | 75b84f4 | brimittleman | 2019-07-01 | add code premature term |
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 ──
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✖ dplyr::filter() masks stats::filter()
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Many papers have started to talk about premature termination. Premature terminated isoforms may be truncated protein or may be degraded. I am going to create a measure for this and test for genetic variation associated with it in my data. The measure will be sum of the reads in intronic PAS and the sum of the UTR reads. I will use leafcutter to put the ratios onto a normal distribution. I will then test for QTLs these ratios.
mkdir ../data/PreTerm_pheno
gene start and end
genes=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/FullTranscriptByName.bed", col.names = c("chr", "Gene_start", "Gene_end", "gene", "score", "strand"),stringsAsFactors = F) %>% select(chr,Gene_start, Gene_end, gene)
totalPAS=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz",stringsAsFactors = F,header = T)
totalPASPheno=totalPAS %>% melt(id.vars="chrom", variable.name="Ind", value.name = "ratio") %>% separate(ratio, into=c("count", "geneCount"), sep="/") %>% separate(chrom, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3" | loc=="intron") %>% inner_join(genes,by=c("chr", "gene"))%>% mutate(gene=paste(chr,Gene_start, Gene_end, gene,sep=":")) %>% group_by(Ind,gene,loc) %>% summarise(SumCount=sum(as.integer(count))) %>% ungroup() %>% group_by(Ind,gene) %>% mutate(nType=n()) %>% filter(nType==2) %>% spread(loc, SumCount) %>% mutate(total=intron+utr3,PreTermInt=paste(intron,total, sep="/"),PreTermUTR=paste(utr3,total, sep="/")) %>% select(-nType, -intron,-utr3,-total)
totalPASPheno_melt= totalPASPheno %>% melt(id.vars=c("Ind", "gene"), variable.name="Type", value.name = "Value") %>% mutate(chrom=paste(gene, Type, sep="_")) %>% spread(Ind, Value) %>% select(-gene, -Type)
write.table(totalPASPheno_melt,"../data/PreTerm_pheno/Total_preterminationPheno.txt",quote=F, row.names=F,col.names=T, sep=" ")
#python2
gzip ../data/PreTerm_pheno/Total_preterminationPheno.txt
python prepare_phenotype_table.py ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz
#activate env
sh ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz_prepare.sh
#top 2 pcs
head -n 3 ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz.PCs > ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz.2PCs
nuclearPAS=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz",stringsAsFactors = F,header = T)
nuclearPASPheno=nuclearPAS %>% melt(id.vars="chrom", variable.name="Ind", value.name = "ratio") %>% separate(ratio, into=c("count", "geneCount"), sep="/") %>% separate(chrom, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3" | loc=="intron") %>% inner_join(genes,by=c("chr", "gene"))%>% mutate(gene=paste(chr,Gene_start, Gene_end, gene,sep=":")) %>% group_by(Ind,gene,loc) %>% summarise(SumCount=sum(as.integer(count))) %>% ungroup() %>% group_by(Ind,gene) %>% mutate(nType=n()) %>% filter(nType==2) %>% spread(loc, SumCount) %>% mutate(total=intron+utr3,PreTermInt=paste(intron,total, sep="/"),PreTermUTR=paste(utr3,total, sep="/")) %>% select(-nType, -intron,-utr3,-total)
nuclearPASPheno_melt= nuclearPASPheno %>% melt(id.vars=c("Ind", "gene"), variable.name="Type", value.name = "Value") %>% mutate(chrom=paste(gene, Type, sep="_")) %>% spread(Ind, Value) %>% select(-gene, -Type)
write.table(nuclearPASPheno_melt,"../data/PreTerm_pheno/Nuclear_preterminationPheno.txt",quote=F, row.names=F,col.names=T, sep=" ")
#python2
gzip ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt
python prepare_phenotype_table.py ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz
#env
sh ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz_prepare.sh
#top 2 pcs
head -n 3 ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz.PCs > ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz.2PCs
Sample list from previous work
mkdir ../data/PrematureQTLNominal
mkdir ../data/PrematureQTLPermuted
sbatch PrematureQTLNominal.sh
sbatch PrematureQTLPermuted.sh
May want to only test one number per gene but do this for now because I want to take advantage of the leafcutter normalization software.
cat ../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_chr* > ../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChr.txt
cat ../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_chr* > ../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChr.txt
totRes=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChr.txt", stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
totRes$bh=p.adjust(totRes$bpval, method="fdr")
totRes_sig=totRes %>% filter(-log10(bh)>1)
totRes_sig_genes=totRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
write.table(totRes, file = "../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", col.names = T, row.names = F, quote = F)
nrow(totRes_sig_genes)
[1] 24
Proportion of genes tested:
tottested_genes=totRes %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
nrow(totRes_sig_genes)/nrow(tottested_genes)
[1] 0.007418856
qqplot:
qqplot(-log10(runif(nrow(totRes))), -log10(totRes$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total premature termination")
abline(0,1)
ggplot(totRes, aes(x=dist)) + geom_histogram(bins=100)
Warning: Removed 332 rows containing non-finite values (stat_bin).
Version | Author | Date |
---|---|---|
272b0b4 | brimittleman | 2019-07-08 |
nucRes=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChr.txt", stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nucRes$bh=p.adjust(nucRes$bpval, method="fdr")
nucRes_sig=nucRes %>% filter(-log10(bh)>1)
nucRes_sig_genes=nucRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
write.table(nucRes, file = "../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", col.names = T, row.names = F, quote = F)
nrow(nucRes_sig_genes)
[1] 69
Proportion of genes tested:
nuctested_genes=nucRes %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
nrow(nucRes_sig_genes)/nrow(nuctested_genes)
[1] 0.01431535
qqplot:
qqplot(-log10(runif(nrow(nucRes))), -log10(nucRes$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear premature termination")
abline(0,1)
Version | Author | Date |
---|---|---|
272b0b4 | brimittleman | 2019-07-08 |
More likely in nuclear:
prop.test(x=c(nrow(nucRes_sig_genes),nrow(totRes_sig_genes)), n=c(nrow(nuctested_genes),nrow(tottested_genes)),alternative = "greater")
2-sample test for equality of proportions with continuity
correction
data: c(nrow(nucRes_sig_genes), nrow(totRes_sig_genes)) out of c(nrow(nuctested_genes), nrow(tottested_genes))
X-squared = 7.4745, df = 1, p-value = 0.003129
alternative hypothesis: greater
95 percent confidence interval:
0.002886 1.000000
sample estimates:
prop 1 prop 2
0.014315353 0.007418856
I next want to look at the proportion of eGenes.
explainedEgenes=read.table("../data/Li_eQTLs/explainedEgenes.txt", col.names = c("gene"), stringsAsFactors = F)
unexplainedEgenes=read.table("../data/Li_eQTLs/UnexplainedEgenes.txt", col.names = c("gene"), stringsAsFactors = F)
allEgenes=bind_rows(explainedEgenes, unexplainedEgenes)
I want to test the proportion of overlap.
TotPre_uneGene=totRes_sig_genes %>% inner_join(unexplainedEgenes,by="gene")
NucPre_uneGene=nucRes_sig_genes %>% inner_join(unexplainedEgenes,by="gene")
TotPre_exeGene=totRes_sig_genes %>% inner_join(explainedEgenes,by="gene")
NucPre_exeGene=nucRes_sig_genes %>% inner_join(explainedEgenes,by="gene")
TotPre_alleGene=totRes_sig_genes %>% inner_join(allEgenes,by="gene")
NucPre_alleGene=nucRes_sig_genes %>% inner_join(allEgenes,by="gene")
Proportion of eGenes explaiend by this:
#total
nrow(TotPre_uneGene)/nrow(unexplainedEgenes)
[1] 0.006578947
nrow(TotPre_exeGene)/nrow(explainedEgenes)
[1] 0.004699248
nrow(TotPre_alleGene)/nrow(allEgenes)
[1] 0.005482456
#nuclear
nrow(NucPre_uneGene)/nrow(unexplainedEgenes)
[1] 0.007894737
nrow(NucPre_exeGene)/nrow(explainedEgenes)
[1] 0.01315789
nrow(NucPre_alleGene)/nrow(allEgenes)
[1] 0.01096491
prop.test(x=c(nrow(NucPre_uneGene),nrow(TotPre_uneGene)), n=c(nrow(unexplainedEgenes),nrow(unexplainedEgenes)))
2-sample test for equality of proportions with continuity
correction
data: c(nrow(NucPre_uneGene), nrow(TotPre_uneGene)) out of c(nrow(unexplainedEgenes), nrow(unexplainedEgenes))
X-squared = 0, df = 1, p-value = 1
alternative hypothesis: two.sided
95 percent confidence interval:
-0.008521981 0.011153560
sample estimates:
prop 1 prop 2
0.007894737 0.006578947
prop.test(x=c(nrow(NucPre_exeGene),nrow(TotPre_exeGene)), n=c(nrow(explainedEgenes),nrow(explainedEgenes)))
2-sample test for equality of proportions with continuity
correction
data: c(nrow(NucPre_exeGene), nrow(TotPre_exeGene)) out of c(nrow(explainedEgenes), nrow(explainedEgenes))
X-squared = 3.3988, df = 1, p-value = 0.06525
alternative hypothesis: two.sided
95 percent confidence interval:
-0.0004665971 0.0173838903
sample estimates:
prop 1 prop 2
0.013157895 0.004699248
Conclusion:
Total- 13 overlaps with all eGenes, 7 ex, 6 unexplained Nuclear- 24 overlaps with all eGenes, 13 ex, 11 unexpained
All eGenes=1824 Unexplained=760 Explained=1064
Are the total in the nuclear:
totInuc=totRes_sig_genes %>% anti_join(nucRes_sig_genes,by="gene")
nrow(totRes_sig_genes)-nrow(totInuc)
[1] 14
#did we test all of the
totInucTESTEDnuc=totInuc %>% anti_join(nuctested_genes, by="gene")
nrow(totInucTESTEDnuc)
[1] 2
totInucTESTEDnuc
gene
1 IPO5P1
2 ZNF718
#all
totInuc %>% inner_join(allEgenes,by="gene")
gene
1 MTHFSD
2 IPO5P1
3 ANKRD44
4 ERAP2
#explained
totInuc %>% inner_join(explainedEgenes,by="gene")
gene
1 IPO5P1
2 ERAP2
#unexplained
totInuc %>% inner_join(unexplainedEgenes,by="gene")
gene
1 MTHFSD
2 ANKRD44
nucRes_sig_dir= nucRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_")
ggplot(nucRes_sig_dir, aes(x=Frac, y=slope)) + geom_boxplot() + geom_jitter(aes(col=Frac)) + labs(title="Nuclear premature APA QTL effect sizes", y="Effect size",x="") + scale_x_discrete(labels=c( "Intronic Usage","3' UTR Usage"))+ scale_color_discrete(name ="", labels=c( "Intronic Usage","3' UTR Usage"))
Version | Author | Date |
---|---|---|
272b0b4 | brimittleman | 2019-07-08 |
totRes_sig_dir= totRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_")
ggplot(totRes_sig_dir, aes(x=Frac, y=slope)) + geom_boxplot() + geom_jitter(aes(col=Frac))+ labs(title="Total premature APA QTL effect sizes", y="Effect size",x="") + scale_x_discrete(labels=c( "Intronic Usage","3' UTR Usage"))+ scale_color_discrete(name ="", labels=c( "Intronic Usage","3' UTR Usage"))
Version | Author | Date |
---|---|---|
272b0b4 | brimittleman | 2019-07-08 |
The difference may just be due to the numbers but most of the variants are associated with decreased utr usage and increase intronic usage.
Code that will plot the non normalized intronic ratio:
mkdir ../data/pttQTLplots
TotPretermPhen=read.table("../data/PreTerm_pheno/Total_preterminationPheno.txt.gz", header = T,stringsAsFactors = F) %>% separate(chrom, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into = c("gene", "loc"),sep="_") %>% filter(loc=="PreTermInt") %>% select(-start,-chr,-end,-loc)
TotPretermPhen_melt=melt(TotPretermPhen, id.vars = "gene", variable.name = "Individual") %>% separate(value, into=c("num", "den"),sep="/") %>% mutate(ratio=as.integer(num)/as.integer(den)) %>% select(-num,-den)
write.table(TotPretermPhen_melt,file="../data/pttQTLplots/TotalPhenotype.txt",col.names = T, row.names = F, quote=F, sep="\t")
totpttQTL=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% select(pid,sid )
head(totpttQTL)
pid sid
1 10:124690418:124713919:C10orf88_PreTermInt rs7904973
2 10:124690418:124713919:C10orf88_PreTermUTR rs7904973
3 14:74181824:74253961:ELMSAN1_PreTermUTR rs73297476
4 14:104095524:104167888:KLC1_PreTermInt rs4906340
5 14:104095524:104167888:KLC1_PreTermUTR rs4906340
6 16:10854775:10912651:TVP23A_PreTermInt rs2233541
less ../../li_genotypes/genotypesYRI.gen.proc.5MAF.vcf.gz | head -n 1 > ../data/pttQTLplots/genoHead.txt
less ../../li_genotypes/genotypesYRI.gen.proc.5MAF.chr10.vcf.gz | grep rs7091776 > ../data/pttQTLplots/rs7091776.txt
Remove #
geno_head=read.table("../data/pttQTLplots/genoHead.txt", header =T,stringsAsFactors = F)
rs7091776=read.table("../data/pttQTLplots/rs7091776.txt", col.names =colnames(geno_head),stringsAsFactors = F)%>% select(ID,contains("NA"))
lettersGeno=read.table("../data/pttQTLplots/rs7091776.txt", col.names =colnames(geno_head), colClasses = c("character")) %>% select(REF,ALT)
refAllele=as.character(lettersGeno$REF)
altAllele=as.character(lettersGeno$ALT)
genoMelt=melt(rs7091776, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% select(Individual, FullGeno) %>% mutate(genotype=ifelse(round(as.integer(FullGeno))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(FullGeno))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
TotPretermPhen_melt_C10orf88= TotPretermPhen_melt %>% filter(gene=="C10orf88") %>% inner_join(genoMelt, by="Individual")
Warning: Column `Individual` joining factors with different levels,
coercing to character vector
ggplot(TotPretermPhen_melt_C10orf88,aes(x=genotype, y=ratio, fill=genotype)) + geom_boxplot(width=.5)+ geom_jitter(alpha=1) + labs(y="Intronic PAS usage Ratio") + scale_fill_brewer(palette = "Dark2")
NucPretermPhen=read.table("../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz", header = T,stringsAsFactors = F) %>% separate(chrom, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into = c("gene", "loc"),sep="_") %>% filter(loc=="PreTermInt") %>% select(-start,-chr,-end,-loc)
NucPretermPhen_melt=melt(NucPretermPhen, id.vars = "gene", variable.name = "Individual") %>% separate(value, into=c("num", "den"),sep="/") %>% mutate(ratio=as.integer(num)/as.integer(den)) %>% select(-num,-den)
write.table(NucPretermPhen_melt,file="../data/pttQTLplots/NuclearPhenotype.txt",col.names = T, row.names = F, quote=F, sep="\t")
Code to run this for any example:
sbatch run_pttfacetboxplot.sh Total C10orf88 10 rs7091776
This works. I want to write a script that will make all of them.
python writePTTexamplecode.py Total
python writePTTexamplecode.py Nuclear
sbatch Script4TotalPTTqtlexamples.sh
sbatch Script4NuclearPTTqtlexamples.sh
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1 workflowr_1.4.0 reshape2_1.4.3
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 RColorBrewer_1.1-2 cellranger_1.1.0
[4] compiler_3.5.1 pillar_1.3.1 git2r_0.25.2
[7] plyr_1.8.4 highr_0.7 tools_3.5.1
[10] digest_0.6.18 lubridate_1.7.4 jsonlite_1.6
[13] evaluate_0.12 nlme_3.1-137 gtable_0.2.0
[16] lattice_0.20-38 pkgconfig_2.0.2 rlang_0.4.0
[19] cli_1.1.0 rstudioapi_0.10 yaml_2.2.0
[22] haven_1.1.2 withr_2.1.2 xml2_1.2.0
[25] httr_1.3.1 knitr_1.20 hms_0.4.2
[28] generics_0.0.2 fs_1.3.1 rprojroot_1.3-2
[31] grid_3.5.1 tidyselect_0.2.5 glue_1.3.0
[34] R6_2.3.0 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 stringi_1.2.4 lazyeval_0.2.1
[49] munsell_0.5.0 broom_0.5.1 crayon_1.3.4