Last updated: 2019-05-01
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Rmd | f587970 | brimittleman | 2019-05-01 | fix join |
html | 9fba469 | brimittleman | 2019-05-01 | Build site. |
Rmd | df3b22e | brimittleman | 2019-05-01 | add heatmap all qtls |
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Rmd | a1e3a43 | brimittleman | 2019-04-30 | add example boxplots |
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Rmd | cf09985 | brimittleman | 2019-04-30 | add beta corr plots |
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Rmd | 39a6572 | brimittleman | 2019-04-29 | add correlation genotype heatmap |
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I have about double the QTLs hear compared to before resequencing batch 4. I will look at the new QTL to see if there is evidence for them being false positives. I am going to see if there is structure in the genotypes for these QTLs.
The old QTLs are from the threeprimeseq repository.
Import old QTLs
oldtot=read.table("../../threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", header=T,stringsAsFactors = F) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Strand","Peak"), sep="_")
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].
OldTotQTLs= oldtot %>% filter(-log10(bh)>=1)
nrow(OldTotQTLs)
[1] 291
Import new QTLs:
newTotQTLs=read.table("../data/apaQTLs/Total_apaQTLs_5fdr.txt", stringsAsFactors = F, header = T)
nrow(newTotQTLs)
[1] 502
Filter out those matching from the old:
UniqueNewTot=newTotQTLs %>% anti_join(OldTotQTLs, by=c("sid","Gene"))
Write these out to fetch the genotypes:
write.table(UniqueNewTot, file="../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt", quote = F, col.names = F, row.names = F)
oldnuc=read.table("../../threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", header=T,stringsAsFactors = F) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Strand","Peak"), sep="_")
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].
OldNucQTLs= oldnuc %>% filter(-log10(bh)>=1)
nrow(OldNucQTLs)
[1] 615
Import new QTLs:
newNucQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs_5fdr.txt", stringsAsFactors = F, header = T)
nrow(newNucQTLs)
[1] 1070
UniqueNewNuc=newNucQTLs %>% anti_join(OldNucQTLs, by=c("sid","Gene"))
write.table(UniqueNewNuc, file="../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt", quote = F, col.names = F, row.names = F)
I wrote a script to pull the doses from the vcf file. Run it with:
python extractGenotypes.py ../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt ../data/QTLGenotypes/Genotypes_NuclearapaQTLS_newunique.txt
python extractGenotypes.py ../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt ../data/QTLGenotypes/Genotypes_TotalapaQTLS_newunique.txt
I also need the header from the VCF to have the individuals:
head -n14 /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf | tail -n1 > ../data/QTLGenotypes/vcfheader.txt
#manually remove # and unneaded columns, keep snp and ind.
vcfhead=read.table("../data/QTLGenotypes/vcfheader.txt", header = T)
input sample list:
samples=read.table("../data/phenotype/SAMPLE.txt")
samplist=as.vector(samples$V1)
totgeno=read.table("../data/QTLGenotypes/Genotypes_TotalapaQTLS_newunique.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()
Correlation:
totgenosm=totgeno[,1:250]
totgeneCorr=round(cor(totgenosm),2)
heatmap.2(as.matrix(totgeneCorr),trace="none", dendrogram =c("none"), main="Genotype correlation\n for new Total QTL snps")
Version | Author | Date |
---|---|---|
e3bdc3a | brimittleman | 2019-04-29 |
nucgeno=read.table("../data/QTLGenotypes/Genotypes_NuclearapaQTLS_newunique.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()
Correlation:
nucgenosm=nucgeno[,1:600]
nucgeneCorr=round(cor(nucgenosm),2)
heatmap.2(as.matrix(nucgeneCorr),trace="none", dendrogram =c("none"),main="Genotype correlation \n for new Nuclear QTL snps")
Version | Author | Date |
---|---|---|
e3bdc3a | brimittleman | 2019-04-29 |
This was for the new QTLs. I want to see if there is structure more generally.
python extractGenotypes.py ../data/apaQTLs/Nuclear_apaQTLs_5fdr.txt ../data/QTLGenotypes/Genotypes_NuclearapaQTLS_ALLQTLs.txt
python extractGenotypes.py ../data/apaQTLs/Total_apaQTLs_5fdr.txt ../data/QTLGenotypes/Genotypes_TotalapaQTLS_ALLQTLs.txt
totgenoall=read.table("../data/QTLGenotypes/Genotypes_TotalapaQTLS_ALLQTLs.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()
totgenoallsm=totgenoall[,1:300]
totgeneallCorr=round(cor(totgenoallsm),2)
heatmap.2(as.matrix(totgeneallCorr),trace="none", dendrogram =c("none"),main="Genotype correlation \n for first 300 Total QTL snps")
Version | Author | Date |
---|---|---|
9fba469 | brimittleman | 2019-05-01 |
nucgenoall=read.table("../data/QTLGenotypes/Genotypes_NuclearapaQTLS_ALLQTLs.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()
nucgenoallsm=nucgenoall[,1:400]
nucgeneallCorr=round(cor(nucgenoallsm),2)
heatmap.2(as.matrix(nucgeneallCorr),trace="none", dendrogram =c("none"),main="Genotype correlation \n for first 400 Nuclear QTL snps")
Version | Author | Date |
---|---|---|
9fba469 | brimittleman | 2019-05-01 |
I want to make a scatter plot comaparring the new QTL associations in the 55 vs 39 individauls. If the qtls are real we expect a high correlation.
To do this I can recall the qtls with a smaller sample list excluding the 15 new individuals.
I need to make a list of the individuals not in the 4th batch.
batch1.2.3=read.table("../data/MetaDataSequencing.txt", header=T,stringsAsFactors = F)%>% filter(fraction=="total") %>% select(line, batch) %>% filter(batch != 4)
samplelist=read.table("../data/phenotype/SAMPLE.txt", col.names = c("line"),stringsAsFactors = F)
Make a new directory for the 39ind qtls:
mkdir ../data/ThirtyNineIndQtl_nominal
Filter the sample list
samplelist_39= samplelist %>% semi_join(batch1.2.3, by="line")
write.table(samplelist_39, file="../data/ThirtyNineIndQtl_nominal/samplelist39.txt", col.names = F, row.names = F, quote = F)
Run the QTL code with this sample list
sbatch aAPAqtl_nominal39ind.sh
Concatinate results:
cat APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_chr* > APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChr.txt
cat APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr* > APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChr.txt
I want to filter the results for the new snps in the uniquenewtot. These results are in data/apaQTLs
I need to write a script that makes a dictionary with each of the new QTLs in the format above. Then I can run throguh the nominal values and keep only the values in the dictionary.
I can run this on the 55 and 39 nominal files then combine the files to create the scatterplot.
python selectNominalPvalues.py ../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt ../data/ThirtyNineIndQtl_nominal/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChr.txt ../data/ThirtyNineIndQtl_nominal/Total_apaQTLs_NewUniqNom_37ing.txt
python selectNominalPvalues.py ../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt ../data/apaQTLNominal/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChr.txt ../data/ThirtyNineIndQtl_nominal/Total_apaQTLs_NewUniqNom_55ind.txt
Import files:
newin37_tot=read.table("../data/ThirtyNineIndQtl_nominal/Total_apaQTLs_NewUniqNom_37ing.txt",col.names=c("peakID", "snp", "dist", "Nompval39","Beta39"), stringsAsFactors = F)
newin54_tot=read.table("../data/ThirtyNineIndQtl_nominal/Total_apaQTLs_NewUniqNom_55ind.txt",col.names=c("peakID", "snp", "dist", "Nompval54","Beta54"), stringsAsFactors = F)
Join these:
newinboth=newin54_tot %>% inner_join(newin37_tot, by=c("peakID", "snp"))
total_qtlind=ggplot(newinboth,aes(x=Beta54, y=Beta39)) + geom_point() + labs(title="New Total apaQTLs \nin different ind. sets", ylab="Beta 39 ind", xlab="Beta 55 ind")
total_qtlind
python selectNominalPvalues.py ../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt ../data/ThirtyNineIndQtl_nominal/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChr.txt ../data/ThirtyNineIndQtl_nominal/Nuclear_apaQTLs_NewUniqNom_37ing.txt
python selectNominalPvalues.py ../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt ../data/apaQTLNominal/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChr.txt ../data/ThirtyNineIndQtl_nominal/Nuclear_apaQTLs_NewUniqNom_55ind.txt
newin37_nuc=read.table("../data/ThirtyNineIndQtl_nominal/Nuclear_apaQTLs_NewUniqNom_37ing.txt",col.names=c("peakID", "snp", "dist", "Nompval39","Beta39"), stringsAsFactors = F)
newin54_nuc=read.table("../data/ThirtyNineIndQtl_nominal/Nuclear_apaQTLs_NewUniqNom_55ind.txt",col.names=c("peakID", "snp", "dist", "Nompval54","Beta54"), stringsAsFactors = F)
Join these:
newinboth_nuc=newin54_nuc %>% inner_join(newin37_nuc, by=c("peakID", "snp"))
nuclear_qtlind=ggplot(newinboth_nuc,aes(x=Beta54, y=Beta39)) + geom_point() + labs(title="New Nuclear apaQTLs\n in different ind. sets", ylab="Beta 39 ind", xlab="Beta 55 ind")
plot both:
plot_grid(total_qtlind, nuclear_qtlind)
Plot -log10pvalue
pvalplot_tot=ggplot(newinboth,aes(x=-log10(Nompval54), y=-log10(Nompval39))) + geom_point() + labs(title="New Total apaQTLs \nin different ind. sets", y="-log10 pval 39 ind", x="-log10 pval 55 ind")
pvalplot_tot
pvalplot_nuc=ggplot(newinboth_nuc,aes(x=-log10(Nompval54), y=-log10(Nompval39))) + geom_point() + labs(title="New Nuclear apaQTLs \nin different ind. sets", y="-log10 pval 39 ind", x="-log10 pval 55 ind")
plot_grid(pvalplot_tot,pvalplot_nuc)
Version | Author | Date |
---|---|---|
9fba469 | brimittleman | 2019-05-01 |
Confirm these QTLs are in new phenotype peaks:
newvold=read.table("../data/peaks_5perc/NewVOldPeaks.txt", header = T, stringsAsFactors = F) %>% select(peak, New)
colnames(newvold)=c("Peak", "Set")
Are the peaks in UniqueNewTot and UniqueNewNuc
UniqueNewTot_set= UniqueNewTot %>% inner_join(newvold, by="Peak")
UniqueNewTot_set$Set=as.factor(UniqueNewTot_set$Set)
summary(UniqueNewTot_set$Set)
new original
209 188
UniqueNewNuc_set= UniqueNewNuc %>% inner_join(newvold, by="Peak")
UniqueNewNuc_set$Set=as.factor(UniqueNewNuc_set$Set)
summary(UniqueNewNuc_set$Set)
new original
521 349
I want to plot these qtls as boxplots. I will plot usage vs. genotype. I want to color the new 15 individuals differently.
peak15390 CREM 10:35411246
less /project2/gilad/briana/YRI_geno_hg19/chr10.dose.filt.vcf.gz | grep 10:35411246 > ../data/exampleQTLs/Geno_10:35411246.txt
#get pheno from pheno_5perc
less ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz | grep peak15390 > ../data/exampleQTLs/Pheno_peak15390.txt
col.names = colnames(vcfhead))
head -n14 /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf | tail -n1 > ../data/exampleQTLs/vcfheader.txt
#get rid of # manually
#pheno head
less APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz | head -n1 > ../exampleQTLs/phenoHead.txt
vcfheadfull=read.table("../data/exampleQTLs/vcfheader.txt", header = T)
phenohead=read.table("../data/exampleQTLs/phenoHead.txt", header = T)
Try first
geno_peak15390=read.table("../data/exampleQTLs/Geno_10:35411246.txt", col.names = colnames(vcfheadfull), stringsAsFactors = F) %>% select(-CHROM, -POS, -REF, -ALT, -QUAL, -FILTER, -INFO, -FORMAT)
geno_peak15390M=melt(geno_peak15390, id.vars = "ID") %>% separate(value, into=c("genotype", "extra", "extra2"), sep=":") %>% mutate(dose=round(as.integer(extra), digits = 1)) %>% select(-genotype, -extra, -extra2)
geno_peak15390M$variable=as.character(geno_peak15390M$variable)
pheno_peak15390=read.table("../data/exampleQTLs/Pheno_peak15390.txt", col.names = colnames(phenohead), stringsAsFactors = F)
pheno_peak15390M=melt(pheno_peak15390,id.vars = "chrom") %>% separate(value, into=c("num", "dom"), sep="/") %>% mutate(usage=as.integer(num)/as.integer(dom))
pheno_peak15390M$variable=as.character(pheno_peak15390M$variable)
Join these
genoPhenopeak15390=pheno_peak15390M %>% inner_join(geno_peak15390M, by="variable") %>% mutate(set=ifelse(variable %in% samplelist_39$line, "old39", "new15"))
genoPhenopeak15390$set=as.factor(genoPhenopeak15390$set)
genoPhenopeak15390$dose=as.factor(genoPhenopeak15390$dose)
ggplot(genoPhenopeak15390, aes(x=dose, y=usage)) + geom_boxplot()+ geom_jitter(aes(col=set))
Version | Author | Date |
---|---|---|
9fba469 | brimittleman | 2019-05-01 |
Try 1 more: PAFAH1B2 peak25812 11:117048417
#get genotype from vcf
less /project2/gilad/briana/YRI_geno_hg19/chr11.dose.filt.vcf.gz | grep 11:117048417 > ../data/exampleQTLs/Geno_11:117048417.txt
#get pheno from pheno_5perc
less ../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz | grep peak25812 > ../data/exampleQTLs/Pheno_peak25812.txt
geno_peak25812=read.table("../data/exampleQTLs/Geno_11:117048417.txt", col.names = colnames(vcfheadfull), stringsAsFactors = F) %>% select(-CHROM, -POS, -REF, -ALT, -QUAL, -FILTER, -INFO, -FORMAT)
geno_peak25812M=melt(geno_peak25812, id.vars = "ID") %>% separate(value, into=c("genotype", "extra", "extra2"), sep=":") %>% mutate(dose=round(as.integer(extra), digits = 1)) %>% select(-genotype, -extra, -extra2)
geno_peak25812M$variable=as.character(geno_peak25812M$variable)
pheno_peak25812=read.table("../data/exampleQTLs/Pheno_peak25812.txt", col.names = colnames(phenohead), stringsAsFactors = F)
pheno_peak25812M=melt(pheno_peak25812,id.vars = "chrom") %>% separate(value, into=c("num", "dom"), sep="/") %>% mutate(usage=as.integer(num)/as.integer(dom))
geno_peak25812M$variable=as.character(geno_peak25812M$variable)
Join these
genoPhenopeak25812=pheno_peak25812M %>% inner_join(geno_peak25812M, by="variable") %>% mutate(set=ifelse(variable %in% samplelist_39$line, "old39", "new15"))
Warning: Column `variable` joining factor and character vector, coercing
into character vector
genoPhenopeak25812$set=as.factor(genoPhenopeak25812$set)
genoPhenopeak25812$dose=as.factor(genoPhenopeak25812$dose)
ggplot(genoPhenopeak25812, aes(x=dose, y=usage)) + geom_boxplot()+ geom_jitter(aes(col=set))
Version | Author | Date |
---|---|---|
9fba469 | brimittleman | 2019-05-01 |
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] cowplot_0.9.4 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.0 tidyverse_1.2.1 gplots_3.0.1 workflowr_1.3.0
[13] gdata_2.18.0 reshape2_1.4.3
loaded via a namespace (and not attached):
[1] gtools_3.8.1 tidyselect_0.2.5 haven_1.1.2
[4] lattice_0.20-38 colorspace_1.3-2 generics_0.0.2
[7] htmltools_0.3.6 yaml_2.2.0 rlang_0.3.1
[10] pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[16] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0
[19] rvest_0.3.2 caTools_1.17.1.1 evaluate_0.12
[22] labeling_0.3 knitr_1.20 broom_0.5.1
[25] Rcpp_1.0.0 KernSmooth_2.23-15 backports_1.1.2
[28] scales_1.0.0 jsonlite_1.6 fs_1.2.6
[31] hms_0.4.2 digest_0.6.18 stringi_1.2.4
[34] grid_3.5.1 rprojroot_1.3-2 cli_1.0.1
[37] tools_3.5.1 bitops_1.0-6 magrittr_1.5
[40] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[43] pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4
[46] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137
[52] git2r_0.23.0 compiler_3.5.1