characterizeTotalApaQtls.Rmd

---
title: "Characterize Total ApaQTLs"
author: "Briana Mittleman"
date: "10/11/2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

This analysis will be used to characterize the total ApaQTLs. I will run the analysis on the total APAqtls in this analysis and will then run a similar analysis on the nuclear APAqtls in another analysis. I would like to study:  


* Distance metrics:  
    + distance from snp to TSS of gene  
    + Distance from snp to peak  

* Expression metrics: 
    + expression of genes with significant QTLs vs other genes  (by rna seq)
    + expression of genes with significant QTLs vs other genes  (peak coverage)  

* Chrom HMM metrics:  
    + look at the chrom HMM interval for the significant QTLs  


 




##Upload Libraries and Data:  

Library
```{r}
library(workflowr)
library(reshape2)
library(tidyverse)
library(VennDiagram)
library(data.table)
library(cowplot)
```


Permuted Results from APA:

I will add a column to this dataframe that will tell me if the association is significant at 10% FDR.  This will help me plot based on significance later in the analysis. I am also going to seperate the PID into relevant pieces.  

```{r}
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T)  %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>%  separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak"))

totalAPA$sig=as.factor(totalAPA$sig)


print(names(totalAPA))
```



##Distance Metrics  

### Distance from snp to TSS  

I ran the QTL analysis based on the starting position of the gene. 


```{r}
ggplot(totalAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp to TSS", x="Base Pairs") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))
```

It looks like most of the signifcant values are 100,000 bases. This makes sense.  I can zoom in on this portion.  

```{r}
ggplot(totalAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)+coord_cartesian(xlim = c(-150000, 150000))
```

### Distance from snp to peak  

To perform this analysis I need to recover the peak positions.  

The peak file I used for the QTL analysis is: /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed  

```{r}
peaks=read.table("../data/PeaksUsed/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed", col.names = c("chr", "peakStart", "peakEnd", "PeakNum", "PeakScore", "Strand", "Gene")) %>% mutate(peak=paste("peak", PeakNum,sep="")) %>% mutate(PeakCenter=peakStart+ (peakEnd- peakStart))
```

I want to join the peak start to the totalAPA file but the peak column. I will then create a column that is snppos-peakcenter.

```{r}
totalAPA_peakdist= totalAPA %>%  inner_join(peaks, by="peak") %>%  separate(sid, into=c("snpCHR", "snpLOC"), by=":")
totalAPA_peakdist$snpLOC= as.numeric(totalAPA_peakdist$snpLOC)

totalAPA_peakdist= totalAPA_peakdist %>%  mutate(DisttoPeak= snpLOC-PeakCenter)
```

Plot this by significance.  
```{r}
ggplot(totalAPA_peakdist, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp peak", x="log10 absolute value Distance to Peak") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))


```

Look at the summarys based on significance:  

```{r}
totalAPA_peakdist_sig=totalAPA_peakdist %>% filter(sig==1)
totalAPA_peakdist_notsig=totalAPA_peakdist %>% filter(sig==0)


summary(totalAPA_peakdist_sig$DisttoPeak)
summary(totalAPA_peakdist_notsig$DisttoPeak)
```

```{r}
ggplot(totalAPA_peakdist, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))
```

Look like there are some outliers that are really far. I will remove variants greater than 1*10^6th away   

```{r}
totalAPA_peakdist_filt=totalAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))

ggplot(totalAPA_peakdist_filt, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)

ggplot(totalAPA_peakdist_filt, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)

```
This gives a similar distribution. 


I did snp - peak. This means if the peak is downstream of the snp on the positive strand the number will be negative. 

In this case the peak is downstream of the snp. 

```{r}
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="+") %>%  filter(DisttoPeak < 0) %>% nrow()
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="-") %>%  filter(DisttoPeak > 0) %>% nrow()
```
Peak is upstream of the snp. 

```{r}
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="+") %>%  filter(DisttoPeak > 0) %>% nrow()
totalAPA_peakdist %>% filter(sig==1) %>% filter(strand=="-") %>%  filter(DisttoPeak < 0) %>% nrow()
```
This means there is about 50/50 distribution around the peak start.  

I am going to plot a violin plot for just the significant ones.  

```{r}
ggplot(totalAPA_peakdist_sig, aes(x=DisttoPeak)) + geom_density()
```
Within 1000 bases of the peak center.  

```{r}
totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()

totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()

totalAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()
```
29 QTLs are within 1000 bp of the peak center, 57 within 10,000bp and 98 within 100,000bp   



##Expression metrics