Explaining pQTLS
Briana Mittleman
11/29/2018
Last updated: 2018-12-04
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Expand here to see past versions:
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a41224c | Briana Mittleman | 2018-12-04 | phenotype data in model |
| html | 36e39f3 | Briana Mittleman | 2018-12-03 | Build site. |
| Rmd | 17b336b | Briana Mittleman | 2018-12-03 | add random |
| html | 8bb97f6 | Briana Mittleman | 2018-12-03 | Build site. |
| Rmd | 8b45f35 | Briana Mittleman | 2018-12-03 | add anova |
| html | dd06ccc | Briana Mittleman | 2018-12-03 | Build site. |
| Rmd | 37ef750 | Briana Mittleman | 2018-12-03 | add graph for top 5 qtls |
| html | 210b58b | Briana Mittleman | 2018-11-29 | Build site. |
| Rmd | 413c8fd | Briana Mittleman | 2018-11-29 | add filter QTL analysis and start explain pqtl |
In this analysis I want to look at how well the eQTLs and apaQTLs an explain pQTLs. I will use linear models on the effect sizes.
Libraries
library(workflowr)This is workflowr version 1.1.1
Run ?workflowr for help getting startedlibrary(tidyverse)── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.0.0 ✔ purrr 0.2.5
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✔ tidyr 0.8.1 ✔ stringr 1.3.1
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✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithslibrary(broom)
library(cowplot)
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsaveInput the pQTLs (10% FDR) and gene names
geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F)
pQTL=read.table("../data/other_qtls/fastqtl_qqnorm_prot.fixed.perm.out", col.names = c("Gene.stable.ID", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"), header = F, stringsAsFactors = F) %>% inner_join(geneNames, by="Gene.stable.ID") %>% dplyr::select("Gene.name","Gene.stable.ID", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval")
pQTL$bh=p.adjust(pQTL$bpval, method="fdr")
pQTL_sig=pQTL %>% filter(-log10(bh)> 1) Example CCDC51
Start with the exanple with the highest effect size:
CCDC51- ENSG00000164051 3:48476431
I need all of the results for this snp gene pair from the total, nuclear, and RNA nominal files. I can make a python script that will take the gene name and snp and create the relevent dataframe.
files: * /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt
* /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt
* /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out (need the ENSG ID)
APAandRNAfromProtQTLs.py
#use this by inserting a gene, gene_ensg (from prot), and snp for the protien QTLs
def main(gene, gene_ensg,snp):
out_prot=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Protres.txt"%(gene, snp), "w")
out_RNA=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_RNAres.txt"%(gene, snp), "w")
out_Total=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Totalres.txt"%(gene, snp), "w")
out_Nuclear=open("/project2/gilad/briana/threeprimeseq/data/protQTL_otherphen/%s_%s_Nuclearres.txt"%(gene, snp), "w")
for ln in open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt", "r"):
s=ln.split()[1]
g=ln.split()[0].split(":")[3].split("_")[0]
if g==gene:
out_Nuclear.write(ln)
for ln in open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt", "r"):
s=ln.split()[1]
g=ln.split()[0].split(":")[3].split("_")[0]
if g==gene:
out_Total.write(ln)
for ln in open("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out", "r"):
s=ln.split()[1]
g=ln.split()[0]
if gene_ensg in g:
out_RNA.write(ln)
for ln in open("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out", "r"):
s=ln.split()[1]
g=ln.split()[0]
if gene_ensg in g:
out_prot.write(ln)
out_Total.close()
out_Nuclear.close()
out_RNA.close()
out_prot.close()
if __name__ == "__main__":
import sys
gene=sys.argv[1]
gene_ensg=sys.argv[2]
snp=sys.argv[3]
main(gene, gene_ensg, snp)
CCDC51_APAandRNAfromProtQTLs.sh
#!/bin/bash
#SBATCH --job-name=CCDC51_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=CCDC51_APAandRNAfromProtQTLs.out
#SBATCH --error=CCDC51_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load python
python APAandRNAfromProtQTLs.py CCDC51 ENSG00000164051 3:48476431 I first want to look at the effect sizes. I am interested in understanding the directions of the effect sizes.
3:48728204. This genotype is in the genotype file twice. I am gonig to remove this snp from the analysis.
nom_names=c("gene", "snp", "dist", "pval", "slope")
CCDC51_apaTot=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope)
CCDC51_apaTot=filter(CCDC51_apaTot, !grepl("3:48728204",snp))
CCDC51_apaNuc=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope)
CCDC51_apaNuc=filter(CCDC51_apaNuc, !grepl("3:48728204",snp))
CCDC51_RNA=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope)
CCDC51_RNA=filter(CCDC51_RNA, !grepl("3:48728204",snp))
CCDC51_Prot=read.table("../data/pQTL_otherphen/CCDC51_3:48476431_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>% select(snp, slope)
CCDC51_Prot=filter(CCDC51_Prot, !grepl("3:48728204",snp))Start with RNA and protein because they are the most simple
CCDC51_ProtRNA=CCDC51_RNA %>% left_join(CCDC51_Prot, by="snp")
colnames(CCDC51_ProtRNA)=c("snp", "RNA", "Protein")Plot this:
plot(CCDC51_ProtRNA$Protein ~ CCDC51_ProtRNA$RNA)
abline(lm(CCDC51_ProtRNA$Protein ~ CCDC51_ProtRNA$RNA))
Expand here to see past versions of unnamed-chunk-7-1.png:
| Version | Author | Date |
|---|---|---|
| 8bb97f6 | Briana Mittleman | 2018-12-03 |
For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.
CCDC51_apaTot_s=spread(CCDC51_apaTot, "peak", "slope",drop = F)CCDC51_apaNuc_s=spread(CCDC51_apaNuc, "peak", "slope",drop = F)I can look at the protein ~ apaTotal.
CCDC51_apaTotProt=CCDC51_apaTot_s %>% left_join(CCDC51_Prot, by="snp")
colnames(CCDC51_apaTotProt)=c("snp", "peak216857", "peak216858", "peak216859", "peak216860", "peak216867","Protein")Try with all of it:
CCDC51_apaTotProtRNA=CCDC51_apaTot_s %>% left_join(CCDC51_ProtRNA, by="snp")
colnames(CCDC51_apaTotProtRNA)=c("snp", "peak216857", "peak216858", "peak216859", "peak216860", "peak216867","RNA","Protein")I want to use the same data for each model:
CCDC51_lmProtRNA=lm(Protein ~ RNA, data=CCDC51_apaTotProtRNA)
CCDC51_lmProtRNA_sum=glance(CCDC51_lmProtRNA)
CCDC51_lmProtAPA=lm(Protein ~peak216857 + peak216858 + peak216859 + peak216860 + peak216867, data=CCDC51_apaTotProtRNA)
CCDC51_lmProtAPA_sum=glance(CCDC51_lmProtAPA)
CCDC51_lmProtAPARNA=lm(Protein ~RNA +peak216857 + peak216858 + peak216859 + peak216860 + peak216867, data=CCDC51_apaTotProtRNA)
CCDC51_lmProtAPARNA_sum=glance(CCDC51_lmProtAPARNA)
adjR2_CCDC51=cbind("CCDC51", CCDC51_lmProtRNA_sum[1,2], CCDC51_lmProtAPA_sum[1,2], CCDC51_lmProtAPARNA_sum[1,2])
colnames(adjR2_CCDC51)=c("Gene", "RNA", "APA", "RNAandAPA")Example DTD1
DTD1- ENSG00000125821 20:18607243
DTD1_APAandRNAfromProtQTLs.sh
#!/bin/bash
#SBATCH --job-name=DTD1_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=DTD1_APAandRNAfromProtQTLs.out
#SBATCH --error=DTD1_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load python
python APAandRNAfromProtQTLs.py DTD1 ENSG00000125821 20:18607243nom_names=c("gene", "snp", "dist", "pval", "slope")
DTD1_RNA=read.table("../data/pQTL_otherphen/DTD1_20:18607243_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>% filter(!duplicated(snp))
DTD1_Prot=read.table("../data/pQTL_otherphen/DTD1_20:18607243_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>% select(snp, slope) %>% filter(!duplicated(snp))
#FILTER snps in the list from Rna and prot
DTD1_apaTot=read.table("../data/pQTL_otherphen/DTD1_20:18607243_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp))
DTD1_apaNuc=read.table("../data/pQTL_otherphen/DTD1_20:18607243_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp)) Start with RNA and protein because they are the most simple
DTD1_ProtRNA=DTD1_RNA %>% left_join(DTD1_Prot, by="snp")
colnames(DTD1_ProtRNA)=c("snp", "RNA", "Protein")DTD1_apaTot_s=spread(DTD1_apaTot, "peak", "slope",drop = F)DTD1_apaNuc_s=spread(DTD1_apaNuc, "peak", "slope",drop = F)DTD1_apaTotProt=DTD1_apaTot_s %>% inner_join(DTD1_Prot, by="snp")
colnames(DTD1_apaTotProt)=c("snp", "peak195416", "peak195418" ,"peak195419", "peak195420", "peak195423", "peak195424","peak195425" ,"peak195426", "peak195427", "peak195428", "peak195429", "peak195430", "peak195431","peak195432", "peak195433" ,"peak195434" ,"peak195436", "peak195438", "peak195443", "peak195444" ,"peak195445", "peak195446", "peak195447", "peak195449" ,"peak195450", "peak195451", "peak195452","peak195453", "peak195454", "peak195455" ,"protein")`
Try with all:
DTD1_apaTotProtRNA=DTD1_apaTot_s %>% inner_join(DTD1_ProtRNA, by="snp")
colnames(DTD1_apaTotProtRNA)=c("snp", "peak195416", "peak195418" ,"peak195419", "peak195420", "peak195423", "peak195424","peak195425" ,"peak195426", "peak195427", "peak195428", "peak195429", "peak195430", "peak195431","peak195432", "peak195433" ,"peak195434" ,"peak195436", "peak195438", "peak195443", "peak195444" ,"peak195445", "peak195446", "peak195447", "peak195449" ,"peak195450", "peak195451", "peak195452","peak195453", "peak195454", "peak195455" ,"RNA", "protein")Model:
DTD1_lmProtRNA=lm(protein ~ RNA, data=DTD1_apaTotProtRNA)
DTD1_lmProtRNA_sum=glance(DTD1_lmProtRNA)
DTD1_lmProtAPA=lm(protein ~ peak195416+ peak195418 +peak195419+peak195420+ peak195423+ peak195424+peak195425 +peak195426+ peak195427+ peak195428+ peak195429+ peak195430 + peak195431+peak195432 + peak195433+peak195434+peak195436+peak195438+peak195443+peak195444+peak195445+peak195446+peak195447+peak195449+peak195450+peak195451+peak195452+peak195453+ peak195454 +peak195455, data=DTD1_apaTotProtRNA)
DTD1_lmProtAPA_sum=glance(DTD1_lmProtAPA)
DTD1_lmProtAPARNA=lm(protein ~RNA+ peak195416+ peak195418 +peak195419+peak195420+ peak195423+ peak195424+peak195425 +peak195426+ peak195427+ peak195428+ peak195429+ peak195430 + peak195431+peak195432 + peak195433+peak195434+peak195436+peak195438+peak195443+peak195444+peak195445+peak195446+peak195447+peak195449+peak195450+peak195451+peak195452+peak195453+ peak195454 +peak195455, data=DTD1_apaTotProtRNA)
DTD1_lmProtAPARNA_sum=glance(DTD1_lmProtAPARNA)
adjR2_DTD1=cbind("DTD1",DTD1_lmProtRNA_sum[1,2], DTD1_lmProtAPA_sum[1,2], DTD1_lmProtAPARNA_sum[1,2])
colnames(adjR2_DTD1)=c("Gene", "RNA", "APA", "RNAandAPA")Example DHRS7B
DHRS7B ENSG00000109016 17:21036822 DHRS7B_APAandRNAfromProtQTLs.sh
#!/bin/bash
#SBATCH --job-name=DHRS7B_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=DHRS7B_APAandRNAfromProtQTLs.out
#SBATCH --error=DHRS7B_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load python
python APAandRNAfromProtQTLs.py DHRS7B ENSG00000109016 17:21036822nom_names=c("gene", "snp", "dist", "pval", "slope")
DHRS7B_RNA=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>% filter(!duplicated(snp))
DHRS7B_Prot=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>% select(snp, slope) %>% filter(!duplicated(snp))
#FILTER snps in the list from Rna and prot
DHRS7B_apaTot=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp))
DHRS7B_apaNuc=read.table("../data/pQTL_otherphen/DHRS7B_17:21036822_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp)) Start with RNA and protein because they are the most simple
DHRS7B_ProtRNA=DHRS7B_RNA %>% left_join(DHRS7B_Prot, by="snp")
colnames(DHRS7B_ProtRNA)=c("snp", "RNA", "Protein")For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.
DHRS7B_apaTot_s=spread(DHRS7B_apaTot, "peak", "slope",drop = F)DHRS7B_apaNuc_s=spread(DHRS7B_apaNuc, "peak", "slope",drop = F)I can look at the protein ~ apaTotal.
DHRS7B_apaTotProt=DHRS7B_apaTot_s %>% left_join(DHRS7B_Prot, by="snp")
colnames(DHRS7B_apaTotProt)=c(names(DHRS7B_apaTot_s),"Protein")Try with all:
DHRS7B_apaTotProtRNA=DHRS7B_apaTot_s %>% inner_join(DHRS7B_ProtRNA, by="snp")
colnames(DHRS7B_apaTotProtRNA)=c(names(DHRS7B_apaTot_s),"RNA", "protein")DHRS7B_lmProtRNA=lm(protein ~ RNA, data=DHRS7B_apaTotProtRNA)
DHRS7B_lmProtRNA_sum=glance(DHRS7B_lmProtRNA)
DHRS7B_lmProtAPA=lm(protein ~ peak132690+peak132692+peak132693+peak132694+peak132720+peak132721+peak132722+peak132723+peak132724+peak132725+peak132728+peak132729+peak132730+peak132732+peak132733+peak132734+peak132735+peak132738+peak132739+peak132740+peak132741+peak132742+peak132744+peak132745 +peak132746+peak132747+peak132748+peak132749+peak132750+peak132751+peak132753+peak132754+ peak132755+peak132756+peak132757+peak132760+peak132761+peak132762+peak132763+peak132764+peak132766+peak132774+peak132775+peak132777+peak132778+peak132780, data=DHRS7B_apaTotProtRNA)
DHRS7B_lmProtAPA_sum=glance(DHRS7B_lmProtAPA)
DHRS7B_lmProtAPARNA=lm(protein ~ peak132690+peak132692+peak132693+peak132694+peak132720+peak132721+peak132722+peak132723+peak132724+peak132725+peak132728+peak132729+peak132730+peak132732+peak132733+peak132734+peak132735+peak132738+peak132739+peak132740+peak132741+peak132742+peak132744+peak132745 +peak132746+peak132747+peak132748+peak132749+peak132750+peak132751+peak132753+peak132754+ peak132755+peak132756+peak132757+peak132760+peak132761+peak132762+peak132763+peak132764+peak132766+peak132774+peak132775+peak132777+peak132778+peak132780, data=DHRS7B_apaTotProtRNA)
DHRS7B_lmProtAPARNA_sum=glance(DHRS7B_lmProtAPARNA)
adjR2_DHRS7B=cbind("DHRS7B",DHRS7B_lmProtRNA_sum[1,2], DHRS7B_lmProtAPA_sum[1,2], DHRS7B_lmProtAPARNA_sum[1,2])
colnames(adjR2_DHRS7B)=c("Gene", "RNA", "APA", "RNAandAPA")Example MMAB
ENSG00000139428 MMAB 12:109997847
MMAB_APAandRNAfromProtQTLs.sh
#!/bin/bash
#SBATCH --job-name=MMAB_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=MMAB_APAandRNAfromProtQTLs.out
#SBATCH --error=MMAB_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load python
python APAandRNAfromProtQTLs.py MMAB ENSG00000139428 12:109997847 nom_names=c("gene", "snp", "dist", "pval", "slope")
MMAB_RNA=read.table("../data/pQTL_otherphen/MMAB_12:109997847_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>% filter(!duplicated(snp))
MMAB_Prot=read.table("../data/pQTL_otherphen/MMAB_12:109997847_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>% select(snp, slope) %>% filter(!duplicated(snp))
#FILTER snps in the list from Rna and prot
MMAB_apaTot=read.table("../data/pQTL_otherphen/MMAB_12:109997847_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp))
MMAB_apaNuc=read.table("../data/pQTL_otherphen/MMAB_12:109997847_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp)) Start with RNA and protein because they are the most simple
MMAB_ProtRNA=MMAB_RNA %>% left_join(MMAB_Prot, by="snp")
colnames(MMAB_ProtRNA)=c("snp", "RNA", "Protein")For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.
MMAB_apaTot_s=spread(MMAB_apaTot, "peak", "slope",drop = F)MMAB_apaNuc_s=spread(MMAB_apaNuc, "peak", "slope",drop = F)I can look at the protein ~ apaTotal.
MMAB_apaTotProt=MMAB_apaTot_s %>% left_join(MMAB_Prot, by="snp")
colnames(MMAB_apaTotProt)=c(names(MMAB_apaTot_s),"Protein")Try with all:
MMAB_apaTotProtRNA=MMAB_apaTot_s %>% inner_join(MMAB_ProtRNA, by="snp")
colnames(MMAB_apaTotProtRNA)=c(names(MMAB_apaTot_s),"RNA", "protein")MMAB_lmProtRNA=lm(protein ~ RNA, data=MMAB_apaTotProtRNA)
MMAB_lmProtRNA_sum=glance(MMAB_lmProtRNA)
MMAB_lmProtAPA=lm(protein ~ peak78754+peak78755+peak78756+peak78757+peak78758+peak78759+peak78760+peak78761+peak78762+peak78763+peak78764+peak78765+peak78766, data=MMAB_apaTotProtRNA)
MMAB_lmProtAPA_sum=glance(MMAB_lmProtAPA)
MMAB_lmProtAPARNA=lm(protein ~RNA+ peak78754+peak78755+peak78756+peak78757+peak78758+peak78759+peak78760+peak78761+peak78762+peak78763+peak78764+peak78765+peak78766, data=MMAB_apaTotProtRNA)
MMAB_lmProtAPARNA_sum=glance(MMAB_lmProtAPARNA)
adjR2_MMAB=cbind("MMAB",MMAB_lmProtRNA_sum[1,2], MMAB_lmProtAPA_sum[1,2], MMAB_lmProtAPARNA_sum[1,2])
colnames(adjR2_MMAB)=c("Gene", "RNA", "APA", "RNAandAPA")Example FYTTD1
FYTTD1 ENSG00000122068 3:197035232
FYTTD1_APAandRNAfromProtQTLs.sh
#!/bin/bash
#SBATCH --job-name=FYTTD1_APAandRNAfromProtQTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=FYTTD1_APAandRNAfromProtQTLs.out
#SBATCH --error=FYTTD1_APAandRNAfromProtQTLs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load python
python APAandRNAfromProtQTLs.py FYTTD1 ENSG00000122068 3:197035232 nom_names=c("gene", "snp", "dist", "pval", "slope")
FYTTD1_RNA=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_RNAres.txt", col.names = nom_names, stringsAsFactors = F) %>% select(snp, slope) %>% filter(!duplicated(snp))
FYTTD1_Prot=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_Protres.txt", col.names = nom_names, stringsAsFactors = F)%>% select(snp, slope) %>% filter(!duplicated(snp))
#FILTER snps in the list from Rna and prot
FYTTD1_apaTot=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_Totalres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp))
FYTTD1_apaNuc=read.table("../data/pQTL_otherphen/FYTTD1_3:197035232_Nuclearres.txt", col.names = nom_names, stringsAsFactors = F) %>% separate(gene, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(snp,peak, slope) %>% group_by(peak) %>% filter(!duplicated(snp)) Start with RNA and protein because they are the most simple
FYTTD1_ProtRNA=FYTTD1_RNA %>% left_join(FYTTD1_Prot, by="snp")
colnames(FYTTD1_ProtRNA)=c("snp", "RNA", "Protein")For APA I need to get a data frame that has the snps by the effect sizes for each peak.I basically want to spread this df.
FYTTD1_apaTot_s=spread(FYTTD1_apaTot, "peak", "slope",drop = F)FYTTD1_apaNuc_s=spread(FYTTD1_apaNuc, "peak", "slope",drop = F)I can look at the protein ~ apaTotal.
FYTTD1_apaTotProt=FYTTD1_apaTot_s %>% left_join(FYTTD1_Prot, by="snp")
colnames(FYTTD1_apaTotProt)=c(names(FYTTD1_apaTot_s),"Protein")FYTTD1_apaTotProtRNA=FYTTD1_apaTot_s %>% inner_join(FYTTD1_ProtRNA, by="snp")
colnames(FYTTD1_apaTotProtRNA)=c(names(FYTTD1_apaTot_s),"RNA", "protein")Model:
FYTTD1_lmProtRNA=lm(protein ~ RNA, data=FYTTD1_apaTotProtRNA)
FYTTD1_lmProtRNA_sum=glance(FYTTD1_lmProtRNA)
FYTTD1_lmProtAPA=lm(protein ~ peak234016 +peak234082 +peak234088+peak234089+peak234090+peak234092+peak234093+peak234094+peak234095+peak234099+peak234101+peak234103+peak234104+peak234105+peak234106+peak234107+peak234108+peak234111+peak234113+peak234114+peak234117+peak234119+peak234120+peak234122+peak234123+peak234124+peak234125+peak234131+peak234135, data=FYTTD1_apaTotProtRNA)
FYTTD1_lmProtAPA_sum=glance(FYTTD1_lmProtAPA)
FYTTD1_lmProtAPARNA=lm(protein ~RNA+ peak234016 +peak234082 +peak234088+peak234089+peak234090+peak234092+peak234093+peak234094+peak234095+peak234099+peak234101+peak234103+peak234104+peak234105+peak234106+peak234107+peak234108+peak234111+peak234113+peak234114+peak234117+peak234119+peak234120+peak234122+peak234123+peak234124+peak234125+peak234131+peak234135, data=FYTTD1_apaTotProtRNA)
FYTTD1_lmProtAPARNA_sum=glance(FYTTD1_lmProtAPARNA)
adjR2_FYTTD1=cbind("FYTTD1",FYTTD1_lmProtRNA_sum[1,2], FYTTD1_lmProtAPA_sum[1,2], FYTTD1_lmProtAPARNA_sum[1,2])
colnames(adjR2_FYTTD1)=c("Gene", "RNA", "APA", "RNAandAPA")Analysis
In each of these examples the total APA explains more of the protein variability. I am going to plot the adjR2 for each gene and model
adjR2=rbind(adjR2_CCDC51,adjR2_DTD1,adjR2_DHRS7B, adjR2_MMAB, adjR2_FYTTD1)
adjR2_melt=melt(adjR2, id.vars="Gene")
colnames(adjR2_melt)=c("Gene", "Model", "AdjR2")Plot this
ggplot(adjR2_melt, aes(x=Gene, y=AdjR2, by=Model, fill=Model)) + geom_bar(stat="identity", position = "dodge") + labs(title="Adjusted R2 for variation\n in protein effect sizes explained in each model",x="pQTL Gene") + scale_fill_brewer(palette="Paired")
Expand here to see past versions of unnamed-chunk-46-1.png:
| Version | Author | Date |
|---|---|---|
| 8bb97f6 | Briana Mittleman | 2018-12-03 |
Residuals method:
I want to use the resid(lm) method to get the residuals for the RNA model and see if I can explain some of these with the APA information:
Get RNA model residuals
CCDC51_RNA_Resid=resid(CCDC51_lmProtRNA)
DTD1_RNA_Resid=resid(DTD1_lmProtRNA)
DHRS7B_RNA_Resid=resid(DHRS7B_lmProtRNA)
MMAB_RNA_Resid=resid(MMAB_lmProtRNA)
FYTTD1_RNA_Resid=resid(FYTTD1_lmProtRNA)Model APA against residuals
CCDC51
CCDC51_lmResidAPA=lm(CCDC51_RNA_Resid ~ CCDC51_apaTotProt$peak216857 +CCDC51_apaTotProt$peak216858 + CCDC51_apaTotProt$peak216859 + CCDC51_apaTotProt$peak216860 + CCDC51_apaTotProt$peak216867)
CCDC51_lmResidRNA_sum=glance(CCDC51_lmResidAPA)DTD1
DTD1_lmResidAPA=lm(DTD1_RNA_Resid ~ DTD1_apaTotProtRNA$peak195416+ DTD1_apaTotProtRNA$peak195418 +DTD1_apaTotProtRNA$peak195419+DTD1_apaTotProtRNA$peak195420+ DTD1_apaTotProtRNA$peak195423+ DTD1_apaTotProtRNA$peak195424+DTD1_apaTotProtRNA$peak195425 +DTD1_apaTotProtRNA$peak195426+ DTD1_apaTotProtRNA$peak195427+ DTD1_apaTotProtRNA$peak195428+ DTD1_apaTotProtRNA$peak195429+ DTD1_apaTotProtRNA$peak195430 + DTD1_apaTotProtRNA$peak195431+DTD1_apaTotProtRNA$peak195432 + DTD1_apaTotProtRNA$peak195433+DTD1_apaTotProtRNA$peak195434+DTD1_apaTotProtRNA$peak195436+DTD1_apaTotProtRNA$peak195438+DTD1_apaTotProtRNA$peak195443+DTD1_apaTotProtRNA$peak195444+DTD1_apaTotProtRNA$peak195445+DTD1_apaTotProtRNA$peak195446+DTD1_apaTotProtRNA$peak195447+DTD1_apaTotProtRNA$peak195449+DTD1_apaTotProtRNA$peak195450+DTD1_apaTotProtRNA$peak195451+DTD1_apaTotProtRNA$peak195452+DTD1_apaTotProtRNA$peak195453+ DTD1_apaTotProtRNA$peak195454 +DTD1_apaTotProtRNA$peak195455)
DTD1_lmResidRNA_sum=glance(DTD1_lmResidAPA)DHRS7B
DHRS7B_lmResidAPA=lm(DHRS7B_RNA_Resid ~ DHRS7B_apaTotProtRNA$peak132690+DHRS7B_apaTotProtRNA$peak132692+DHRS7B_apaTotProtRNA$peak132693+DHRS7B_apaTotProtRNA$peak132694+DHRS7B_apaTotProtRNA$peak132720+DHRS7B_apaTotProtRNA$peak132721+DHRS7B_apaTotProtRNA$peak132722+DHRS7B_apaTotProtRNA$peak132723+DHRS7B_apaTotProtRNA$peak132724+DHRS7B_apaTotProtRNA$peak132725+DHRS7B_apaTotProtRNA$peak132728+DHRS7B_apaTotProtRNA$peak132729+DHRS7B_apaTotProtRNA$peak132730+DHRS7B_apaTotProtRNA$peak132732+DHRS7B_apaTotProtRNA$peak132733+DHRS7B_apaTotProtRNA$peak132734+DHRS7B_apaTotProtRNA$peak132735+DHRS7B_apaTotProtRNA$peak132738+DHRS7B_apaTotProtRNA$peak132739+DHRS7B_apaTotProtRNA$peak132740+DHRS7B_apaTotProtRNA$peak132741+DHRS7B_apaTotProtRNA$peak132742+DHRS7B_apaTotProtRNA$peak132744+DHRS7B_apaTotProtRNA$peak132745 +DHRS7B_apaTotProtRNA$peak132746+DHRS7B_apaTotProtRNA$peak132747+DHRS7B_apaTotProtRNA$peak132748+DHRS7B_apaTotProtRNA$peak132749+DHRS7B_apaTotProtRNA$peak132750+DHRS7B_apaTotProtRNA$peak132751+DHRS7B_apaTotProtRNA$peak132753+DHRS7B_apaTotProtRNA$peak132754+ DHRS7B_apaTotProtRNA$peak132755+DHRS7B_apaTotProtRNA$peak132756+DHRS7B_apaTotProtRNA$peak132757+DHRS7B_apaTotProtRNA$peak132760+DHRS7B_apaTotProtRNA$peak132761+DHRS7B_apaTotProtRNA$peak132762+DHRS7B_apaTotProtRNA$peak132763+DHRS7B_apaTotProtRNA$peak132764+DHRS7B_apaTotProtRNA$peak132766+DHRS7B_apaTotProtRNA$peak132774+DHRS7B_apaTotProtRNA$peak132775+DHRS7B_apaTotProtRNA$peak132777+DHRS7B_apaTotProtRNA$peak132778+DHRS7B_apaTotProtRNA$peak132780)
DHRS7B_lmResidRNA_sum=glance(DHRS7B_lmResidAPA)MMAB
MMAB_lmResidAPA=lm(MMAB_RNA_Resid ~ MMAB_apaTotProtRNA$peak78754+MMAB_apaTotProtRNA$peak78755+MMAB_apaTotProtRNA$peak78756+MMAB_apaTotProtRNA$peak78757+MMAB_apaTotProtRNA$peak78758+MMAB_apaTotProtRNA$peak78759+MMAB_apaTotProtRNA$peak78760+MMAB_apaTotProtRNA$peak78761+MMAB_apaTotProtRNA$peak78762+MMAB_apaTotProtRNA$peak78763+MMAB_apaTotProtRNA$peak78764+MMAB_apaTotProtRNA$peak78765+MMAB_apaTotProtRNA$peak78766)
MMAB_lmResidRNA_sum=glance(MMAB_lmResidAPA)FYTTD1
FYTTD1_lmResidAPA=lm(FYTTD1_RNA_Resid ~ FYTTD1_apaTotProtRNA$peak234016 +FYTTD1_apaTotProtRNA$peak234082 +FYTTD1_apaTotProtRNA$peak234088+FYTTD1_apaTotProtRNA$peak234089+FYTTD1_apaTotProtRNA$peak234090+FYTTD1_apaTotProtRNA$peak234092+FYTTD1_apaTotProtRNA$peak234093+FYTTD1_apaTotProtRNA$peak234094+FYTTD1_apaTotProtRNA$peak234095+FYTTD1_apaTotProtRNA$peak234099+FYTTD1_apaTotProtRNA$peak234101+FYTTD1_apaTotProtRNA$peak234103+FYTTD1_apaTotProtRNA$peak234104+FYTTD1_apaTotProtRNA$peak234105+FYTTD1_apaTotProtRNA$peak234106+FYTTD1_apaTotProtRNA$peak234107+FYTTD1_apaTotProtRNA$peak234108+FYTTD1_apaTotProtRNA$peak234111+FYTTD1_apaTotProtRNA$peak234113+FYTTD1_apaTotProtRNA$peak234114+FYTTD1_apaTotProtRNA$peak234117+FYTTD1_apaTotProtRNA$peak234119+FYTTD1_apaTotProtRNA$peak234120+FYTTD1_apaTotProtRNA$peak234122+FYTTD1_apaTotProtRNA$peak234123+FYTTD1_apaTotProtRNA$peak234124+FYTTD1_apaTotProtRNA$peak234125+FYTTD1_apaTotProtRNA$peak234131+FYTTD1_apaTotProtRNA$peak234135)
FYTTD1_lmResidRNA_sum=glance(FYTTD1_lmResidAPA)Plot with Residuals
RNA_Residuals=rbind(CCDC51_lmResidRNA_sum[1,2],DTD1_lmResidRNA_sum[1,2],DHRS7B_lmResidRNA_sum[1,2],MMAB_lmResidRNA_sum[1,2],FYTTD1_lmResidRNA_sum[1,2])
adjR2_resid=cbind(adjR2,RNA_Residuals)
colnames(adjR2_resid)=c("Gene", "RNA", "APA", "RNAandAPA", "APAinRNAResiduals")
adjR2_resid_melt=melt(adjR2_resid, id.vars="Gene")
colnames(adjR2_resid_melt)=c("Gene", "Model", "AdjR2")
adjR2_resid_melt_sub=adjR2_resid_melt %>% filter(Model=="APAinRNAResiduals" | Model== "RNA")ggplot(adjR2_resid_melt_sub, aes(x=Gene, y=AdjR2, by=Model, fill=Model)) + geom_bar(stat="identity", position = "dodge") + labs(title="Adjusted R2 for variation\n in protein effect sizes explained in each model",x="pQTL Gene") + scale_fill_brewer(palette="Paired") 
Expand here to see past versions of unnamed-chunk-54-1.png:
| Version | Author | Date |
|---|---|---|
| 8bb97f6 | Briana Mittleman | 2018-12-03 |
There is most likely an overfitting problem.
Inference
RSS= residuial sum of squares
#subset model
RSS_sub=anova(CCDC51_lmProtRNA)["Residuals", "Sum Sq"]
df_sub=1466
#full model
RSS_full=anova(CCDC51_lmProtAPARNA)["Residuals", "Sum Sq"]
df_full=14661
anova(CCDC51_lmProtAPARNA)Analysis of Variance Table
Response: Protein
Df Sum Sq Mean Sq F value Pr(>F)
RNA 1 19.427 19.427 175.950 < 2.2e-16 ***
peak216857 1 100.409 100.409 909.411 < 2.2e-16 ***
peak216858 1 26.726 26.726 242.057 < 2.2e-16 ***
peak216859 1 3.528 3.528 31.951 1.898e-08 ***
peak216860 1 13.273 13.273 120.211 < 2.2e-16 ***
peak216867 1 6.939 6.939 62.846 4.402e-15 ***
Residuals 1461 161.310 0.110
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1F_CCDC51=((RSS_sub-RSS_full)/(df_sub-df_full))/(RSS_full/df_full)
F_CCDC51[1] -1.039217#i can do this with the annova function
anova(CCDC51_lmProtRNA,CCDC51_lmProtAPARNA)Analysis of Variance Table
Model 1: Protein ~ RNA
Model 2: Protein ~ RNA + peak216857 + peak216858 + peak216859 + peak216860 +
peak216867
Res.Df RSS Df Sum of Sq F Pr(>F)
1 1466 312.18
2 1461 161.31 5 150.87 273.3 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(MMAB_lmProtRNA,MMAB_lmProtAPARNA)Analysis of Variance Table
Model 1: protein ~ RNA
Model 2: protein ~ RNA + peak78754 + peak78755 + peak78756 + peak78757 +
peak78758 + peak78759 + peak78760 + peak78761 + peak78762 +
peak78763 + peak78764 + peak78765 + peak78766
Res.Df RSS Df Sum of Sq F Pr(>F)
1 2942 529.17
2 2929 245.38 13 283.79 260.57 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(DHRS7B_lmProtRNA,DHRS7B_lmProtAPARNA)Analysis of Variance Table
Model 1: protein ~ RNA
Model 2: protein ~ peak132690 + peak132692 + peak132693 + peak132694 +
peak132720 + peak132721 + peak132722 + peak132723 + peak132724 +
peak132725 + peak132728 + peak132729 + peak132730 + peak132732 +
peak132733 + peak132734 + peak132735 + peak132738 + peak132739 +
peak132740 + peak132741 + peak132742 + peak132744 + peak132745 +
peak132746 + peak132747 + peak132748 + peak132749 + peak132750 +
peak132751 + peak132753 + peak132754 + peak132755 + peak132756 +
peak132757 + peak132760 + peak132761 + peak132762 + peak132763 +
peak132764 + peak132766 + peak132774 + peak132775 + peak132777 +
peak132778 + peak132780
Res.Df RSS Df Sum of Sq F Pr(>F)
1 3071 479602
2 3026 2144 45 477458 14978 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(DHRS7B_lmProtRNA,DHRS7B_lmProtAPARNA)Analysis of Variance Table
Model 1: protein ~ RNA
Model 2: protein ~ peak132690 + peak132692 + peak132693 + peak132694 +
peak132720 + peak132721 + peak132722 + peak132723 + peak132724 +
peak132725 + peak132728 + peak132729 + peak132730 + peak132732 +
peak132733 + peak132734 + peak132735 + peak132738 + peak132739 +
peak132740 + peak132741 + peak132742 + peak132744 + peak132745 +
peak132746 + peak132747 + peak132748 + peak132749 + peak132750 +
peak132751 + peak132753 + peak132754 + peak132755 + peak132756 +
peak132757 + peak132760 + peak132761 + peak132762 + peak132763 +
peak132764 + peak132766 + peak132774 + peak132775 + peak132777 +
peak132778 + peak132780
Res.Df RSS Df Sum of Sq F Pr(>F)
1 3071 479602
2 3026 2144 45 477458 14978 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(FYTTD1_lmProtRNA,FYTTD1_lmProtAPARNA)Analysis of Variance Table
Model 1: protein ~ RNA
Model 2: protein ~ RNA + peak234016 + peak234082 + peak234088 + peak234089 +
peak234090 + peak234092 + peak234093 + peak234094 + peak234095 +
peak234099 + peak234101 + peak234103 + peak234104 + peak234105 +
peak234106 + peak234107 + peak234108 + peak234111 + peak234113 +
peak234114 + peak234117 + peak234119 + peak234120 + peak234122 +
peak234123 + peak234124 + peak234125 + peak234131 + peak234135
Res.Df RSS Df Sum of Sq F Pr(>F)
1 3409 143.379
2 3380 77.295 29 66.083 99.645 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The models are significantly different
Random values
CCDC51_apaTotProtRNA_rand=transform(CCDC51_apaTotProtRNA,peak216857=sample(peak216857), peak216858=sample(peak216858),peak216859=sample(peak216859), peak216860=sample(peak216860) ,peak216867=sample(peak216867))
CCDC51_lmProtAPA_rand=lm(CCDC51_RNA_Resid ~ CCDC51_apaTotProtRNA_rand$peak216857 + CCDC51_apaTotProtRNA_rand$peak216858 + CCDC51_apaTotProtRNA_rand$peak216859 + CCDC51_apaTotProtRNA_rand$peak216860 + CCDC51_apaTotProtRNA_rand$peak216867)
summary(CCDC51_lmProtAPA_rand)
Call:
lm(formula = CCDC51_RNA_Resid ~ CCDC51_apaTotProtRNA_rand$peak216857 +
CCDC51_apaTotProtRNA_rand$peak216858 + CCDC51_apaTotProtRNA_rand$peak216859 +
CCDC51_apaTotProtRNA_rand$peak216860 + CCDC51_apaTotProtRNA_rand$peak216867)
Residuals:
Min 1Q Median 3Q Max
-1.28924 -0.25246 -0.06547 0.25200 2.14234
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.008342 0.012878 0.648 0.5172
CCDC51_apaTotProtRNA_rand$peak216857 0.080512 0.042575 1.891 0.0588
CCDC51_apaTotProtRNA_rand$peak216858 0.020707 0.030660 0.675 0.4995
CCDC51_apaTotProtRNA_rand$peak216859 -0.046768 0.046426 -1.007 0.3139
CCDC51_apaTotProtRNA_rand$peak216860 -0.027177 0.042205 -0.644 0.5197
CCDC51_apaTotProtRNA_rand$peak216867 -0.031990 0.040653 -0.787 0.4315
(Intercept)
CCDC51_apaTotProtRNA_rand$peak216857 .
CCDC51_apaTotProtRNA_rand$peak216858
CCDC51_apaTotProtRNA_rand$peak216859
CCDC51_apaTotProtRNA_rand$peak216860
CCDC51_apaTotProtRNA_rand$peak216867
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4611 on 1462 degrees of freedom
Multiple R-squared: 0.004125, Adjusted R-squared: 0.0007196
F-statistic: 1.211 on 5 and 1462 DF, p-value: 0.3014Start Again: Use individal phenotypes
I need to use the phenotypes rather than the effect sizes. I need to first look at the overlap of individuals in the protein, RNA, and APA data.
/project2/gilad/briana/threeprimeseq/data/molecular_phenos/fastqtl_qqnorm_RNAseq_phase2.Samples.txt
Samples for apa:
#python
fout = open("/project2/gilad/briana/threeprimeseq/data/molecular_phenos/APA_SAMPLE.txt ",'w')
for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt", "r"):
bam, sample = ln.split()
line=sample[:-2]
fout.write("NA"+line + "\n")
fout.close()Imput samples
samp_name=c("samples")
RNA_samp=read.table("../data/pQTL_otherphen/fastqtl_qqnorm_RNAseq_phase2.Samples.txt", col.names = samp_name, stringsAsFactors = F)
Prot_samp=read.table("../data/pQTL_otherphen/fastqtl_qqnorm_prot.fixed.noChr.SAMP.txt", col.names = samp_name, stringsAsFactors = F)
APA_samp=read.table("../data/pQTL_otherphen/APA_SAMPLE.txt", stringsAsFactors = F, col.names = samp_name)Inner Join these to see individuals with all data
all_samp=RNA_samp %>% inner_join(Prot_samp, by="samples") %>% inner_join(APA_samp, by="samples")
nrow(all_samp)[1] 25all_samp_list=all_samp$samples
write.table(all_samp, "../data/pQTL_otherphen/ProtRNAAPA_samples.txt", quote = F, col.names = F, row.names = F)This shows that we have 25 samples with data for all individuals. I can use this to subset the phenotype vector/matrics for each phenotype.
Write script to make lm matrix
Necessary files:
* /project2/gilad/briana/threeprimeseq/data/explainProtVar/ProtRNAAPA_samples.txt
/project2/gilad/briana/threeprimeseq/data/molecular_phenos/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt
/project2/gilad/briana/threeprimeseq/data/molecular_phenos/fastqtl_qqnorm_prot.fixed.noChr.txt
/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr*.gz
Input: This script will need to take in all of these files along with the chromosome, gene name, and ensg gene name. I will write this in R first by doing an example on my computer than making it a general script for midway.
CCDC51 ENSG00000164051 Chr3
Had to get rid of the # in the header to make it come into R.
TotApaChr3=read.table("../data/explainProtVar/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr3", header=T)
RNAPhen=read.table("../data/mol_pheno/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt", header = T)
ProtPhen=read.table("../data/mol_pheno/fastqtl_qqnorm_prot.fixed.noChr.txt", header=T)selct for samples in all_samp and filter for the correct gene
TotApaChr3_CCDC51= TotApaChr3 %>% filter(str_detect(ID, 'CCDC51')) %>% select(all_samp_list) %>% t()
CCDC51_peak= TotApaChr3 %>% filter(str_detect(ID, 'CCDC51')) %>% separate(ID, into=c("chr", "start", "end", "id"), sep=":") %>% separate(id, into=c("Gene.name", "strand", "peaknum"), sep="_") %>% select(peaknum)
RNAPhen_CCDC51=RNAPhen %>% filter(str_detect(ID, 'ENSG00000164051')) %>% select(all_samp_list)%>% t()
ProtPhen_CCDC51= ProtPhen %>% filter(str_detect(ID, 'ENSG00000164051')) %>% select(all_samp_list) %>% t()
CCDC51_full=data.frame(cbind(ProtPhen_CCDC51,RNAPhen_CCDC51,TotApaChr3_CCDC51))
colnames(CCDC51_full)=c("Protein", "RNA",CCDC51_peak$peaknum )Now I have the data and I can run the linear model for protein~ RNA. This asks the question, is there variation in protein not explained by RNA
CCDC51_lmbyInd=lm(Protein~RNA, data=CCDC51_full)
#named list of the residuals
CCDC51_lmbyInd_Resid=as.vector(CCDC51_lmbyInd$residuals)Next step is to assess if APA can explain the residuals. I am asking if variation not explained by RNA explained by APA. To deal with the correlation structure in the peaks. I will use the Stephens lab method Susie.
#devtools::install_github("stephenslab/susieR")
library(susieR)
set.seed(1)My prior can be the total number of peaks (in this case 5)
fitted <- susie(as.matrix(CCDC51_full[,3:ncol(CCDC51_full)]), CCDC51_lmbyInd_Resid,
L = 5,
estimate_residual_variance = TRUE,
scaled_prior_variance = 0.1,
verbose = TRUE)[1] "objective:-35.8428944689953"
[1] "objective:-35.8017789815143"
[1] "objective:-35.6832562642526"
[1] "objective:-35.6830774074188"
[1] "objective:-35.6764286074409"
[1] "objective:-35.6764283743828"
[1] "objective:-35.6750914759185"
[1] "objective:-35.6750914677743"
[1] "objective:-35.6747946648829"
[1] "objective:-35.6747946648393"Get out the credible set:
print(fitted$sets)$cs
NULL
$coverage
[1] 0.95susie_plot(fitted, y="PIP")
The possterior inclusion probability (PIP)
Session information
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] susieR_0.6.2.0394 bindrcpp_0.2.2 cowplot_0.9.3
[4] broom_0.5.0 reshape2_1.4.3 forcats_0.3.0
[7] stringr_1.3.1 dplyr_0.7.6 purrr_0.2.5
[10] readr_1.1.1 tidyr_0.8.1 tibble_1.4.2
[13] ggplot2_3.0.0 tidyverse_1.2.1 workflowr_1.1.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35
[4] expm_0.999-3 colorspace_1.3-2 htmltools_0.3.6
[7] yaml_2.2.0 rlang_0.2.2 R.oo_1.22.0
[10] pillar_1.3.0 glue_1.3.0 withr_2.1.2
[13] R.utils_2.7.0 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 matrixStats_0.54.0 bindr_0.1.1
[19] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[22] cellranger_1.1.0 rvest_0.3.2 R.methodsS3_1.7.1
[25] evaluate_0.11 labeling_0.3 knitr_1.20
[28] Rcpp_0.12.19 scales_1.0.0 backports_1.1.2
[31] jsonlite_1.5 hms_0.4.2 digest_0.6.17
[34] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[37] cli_1.0.1 tools_3.5.1 magrittr_1.5
[40] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[43] pkgconfig_2.0.2 Matrix_1.2-14 xml2_1.2.0
[46] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.8 R6_2.3.0
[52] nlme_3.1-137 git2r_0.23.0 compiler_3.5.1
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