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GIREMI_Development.Rmd
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GIREMI_Development.Rmd
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```{r include=F ,echo=F,warning=F,message=FALSE}
# example
# chr1:59,692,533-59,692,616
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("TxDb.Mmusculus.UCSC.mm10.knownGene")
# BiocManager::install('VariantAnnotation')
# BiocManager::install("org.Mm.eg.db")
# BiocManager::install("trackViewer")
# install.packages("WhopGenome")
library(VariantAnnotation,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(GenomicRanges,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
#library(WhopGenome)
library(trackViewer)
library("pheatmap")
library(vcfR)
library(seqinr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(ggplot2,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library("viridis",quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(edgeR,quietly = T,verbose = F)
library('GenomicFeatures',quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library('rtracklayer',quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
#library(GeneStructureTools)
library(matrixStats,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(plyr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(tidyr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(fitdistrplus,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(stringr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(data.table)
library(reshape)
library(knitr)
```
# Overview
**Align using RASER:**
/data/RBL_NCI/Phil/RASER/public_release_v0.521/raser \
-x /data/RBL_NCI/Phil/RASER/hg19_ensembl.ridx \
-i1 /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/FASTQ/FASTQ_HepG2_ADAR1/HepG2_ADAR_ENCLB012QRD.R1.fastq \
-i2 /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/FASTQ/FASTQ_HepG2_ADAR1/HepG2_ADAR_ENCLB012QRD.R2.fastq \
-o /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.sam \
-m .05 \
-b .03
## Test Data processing methods to identify SNPs
Example data provides total counts and editng counts for each ADAR1 sample.
Locations include RNAediting sites found from any RBP dataset in the ADAR1 batch.
SNP locations are limited to AtoG mutations from chr2 and have to have at least two reads covering the location.
This means that not all the locations have reads with mutations at the SNP locations
and will not be identified in GIREMI analysis
For these comparisons I will compare the counts from ADAR1 - sample 1 data.
data will not be filtered according to requirements for GIREMI calculations to give as many points as possible to identify trends
### Test Remove Duplicates method
**samtools rmdup:**
samtools rmdup -S /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.bam /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.dedup.bam
**samtools markdup:**
samtools markdup -r -t -@32 /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/022520/HepG2_ADAR_ENCLB012QRD.fm.s.bam - | samtools sort > /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/022520/HepG2_ADAR_ENCLB012QRD.fm.mkdedup.bam
**Picard MarkDuplicates**
java -Xmx110g -jar $PICARDJARPATH/picard.jar MarkDuplicates \
I=/data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/022520/HepG2_ADAR_ENCLB012QRD.n95.bam \
O=/data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/022520/HepG2_ADAR_ENCLB012QRD.Picarddedup.bam \
REMOVE_DUPLICATES=TRUE \
TMP_DIR=/data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/Variation/GIREMI/GIREMI_redo_hg19/tmp \
CREATE_INDEX=TRUE \
VALIDATION_STRINGENCY=LENIENT \
METRICS_FILE=/data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/022520/HepG2_ADAR_ENCLB012QRD.Picarddedup.txt
### Test Variant Caller
**mpileup|bcftools:**
samtools mpileup -uf /data/RBL_NCI/Phil/GIREMI_ref/Human/fdb.GENCODE.Gencode_human.release_19.GRCh37_p13_genome.fa \ /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.dedup.bam | bcftools call -c -v | bcftools filter -i'DP>=5' > /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.dedup.vcf \
**Freebayes:**
Check all positions
freebayes -f /data/RBL_NCI/Phil/Reference/GIREMI_ref/Human/fdb.GENCODE.Gencode_human.release_19.GRCh37_p13_genome.fa \
-C 1 -F 0 \
-t /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/bed_OLchr2.bed \
/data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.dedup.bam >/data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.dedup_OLchr2.vcf
## Test counts filters after calling SNPs
**(Lee et.al 2013)**
Read count > 5
Ediing read count > 2
editing ratio > 10%
## Test SNV location filters
“dbSNP” filter: removes RDDs annotated as SNPs (dbSNP 135);
“splice site” filter: removes intronic RDDs within 4 bp of splice junctions;
“simple repeat” filter: discards sites in simple repeats;
“homopolymer” filter: removes sites in homopolyer runs of ≥5 bp;
“high editing level” filter: removes sites with 100% editing levels;
<br>
<br>
When calling variants I include basic filter for minimum coverage of 5 reads and 1 read with SNV.
freebayes -f /data/RBL_NCI/Phil/GIREMI_ref/Human/fdb.GENCODE.Gencode_human.release_19.GRCh37_p13_genome.fa \
-b /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/HepG2_ADAR_ENCLB012QRD.dedup.bam \
-C 1 --min-coverage 5 \
-u > \ /data/RBL_NCI/Wolin/Phil/RNAEdit_test/RNAEdit_test/RASER/varFB_HepG2_ADAR_ENCLB012QRD.dedup.vcf
<br>
-C --min-alternate-count
<br>
<br>
<br>
# Test Data processing methods to identify SNPs
```{r include=F ,echo=F,warning=F,eval=T}
rm(list=ls())
'%ni%' <- Negate('%in%')
OL_ADAR_1_es=read.delim(paste0("../testdata/editing_sites.ADAR_Rep1.txt"), header=F, sep="\t",stringsAsFactors = F)
OL_ADAR_2_es=read.delim(paste0("../testdata/editing_sites.ADAR_Rep2.txt"), header=F, sep="\t",stringsAsFactors = F)
OL_ADARcntr_1_es=read.delim(paste0("../testdata/editing_sites.CONTROL305XWT_Rep1.txt"), header=F, sep="\t",stringsAsFactors = F)
OL_ADARcntr_2_es=read.delim(paste0("../testdata/editing_sites.CONTROL305XWT_Rep2.txt"), header=F, sep="\t",stringsAsFactors = F)
# OL_DHX30_1_es=read.delim(paste0("../testdata/editing_sites.DHX30_Rep1.txt"), header=F, sep="\t",stringsAsFactors = F)
# OL_DHX30_2_es=read.delim(paste0("../testdata/editing_sites.DHX30_Rep2.txt"), header=F, sep="\t",stringsAsFactors = F)
# OL_ADAR_1_es_VCF=read.delim(paste0("../testdata/editing_sites.ADAR_Rep1.txt"), header=F, sep="\t",stringsAsFactors = F)
OL_ADAR_1_es$V0=paste0(OL_ADAR_1_es$V1,':',OL_ADAR_1_es$V2)
OL_ADAR_2_es$V0=paste0(OL_ADAR_2_es$V1,':',OL_ADAR_2_es$V2)
OL_ADAR_1_es$editing_ratio=OL_ADAR_1_es$V4/OL_ADAR_1_es$V5
OL_ADAR_2_es$editing_ratio=OL_ADAR_2_es$V4/OL_ADAR_2_es$V5
#######################################
### DO NOT further filter example tables to compare dedup method and variant caller method as i want to use as many examples as possible
# filter Online SNP Data:
# 1) with eding counts less <3 (Lee et.al 2013)
# 2) or filter SNP with editing Freq less then .1 and min coveage less than 5 (Zhang et.al 2015) also how I ran freebayes
#
# # OL_ADAR_1_es=OL_ADAR_1_es[OL_ADAR_1_es$V4>2,]
# # OL_ADAR_2_es=OL_ADAR_2_es[OL_ADAR_2_es$V4>2,]
#
# OL_ADAR_1_es=OL_ADAR_1_es[OL_ADAR_1_es$V5>5&OL_ADAR_1_es$editing_ratio>.1,]
# OL_ADAR_2_es=OL_ADAR_2_es[OL_ADAR_2_es$V5>5&OL_ADAR_2_es$editing_ratio>.1,]
```
```{r ,fig.show='hold', include=F,echo=FALSE,message=FALSE,warning=FALSE,cache=F,eval=F}
### just make sure that names in both lists have same locations
# OL_all2=merge(OL_ADAR_1_es,OL_ADAR_2_es,by="V0",all=T)
# length(unique(OL_mastertable$editing_site))
# sum(duplicated(OL_mastertable$editing_site))
a=unique(OL_ADAR_1_es$V0)
b=unique(OL_ADAR_2_es$V0)
OVLG_all=unique(c(a,b))
a=OVLG_all%in%a
b=OVLG_all%in%b
C3=cbind(a,b)
rownames(C3)=OVLG_all;colnames(C3)=c('OL_ADAR_1_es','OL_ADAR_1_es')
vennDiagram(vennCounts(C3), main="Compare locations from online list",
circle.col = c("red", "blue", "green3"))
remove(OL_all2)
```
```{r ,fig.show='hold', include=F,echo=FALSE,message=FALSE,warning=FALSE,cache=F}
a=OL_ADAR_1_es[OL_ADAR_1_es$V4>0,]
b=OL_ADAR_2_es[OL_ADAR_2_es$V4>0,]
a=unique(a$V0)
b=unique(b$V0)
OVLG_all=unique(c(a,b))
a=OVLG_all%in%a
b=OVLG_all%in%b
C3=cbind(a,b)
rownames(C3)=OVLG_all;colnames(C3)=c('OL_1','OL_2')
vennDiagram(vennCounts(C3),main="Compair samples where online list has editing count >0",
circle.col = c("red", "blue", "green3"))
remove(a,b)
```
```{r include=F ,echo=F,warning=F,eval=T}
# Get DE Genes
OL_mastertable=read.delim(paste0("../testdata/Summary.all_de_sites.all_rbps.HepG2.new_fdr_corr.final.header.tab.txt"), header=T, sep="\t",stringsAsFactors = F)
# unique(OL_mastertable$RBP)[grepl("RO", unique(OL_mastertable$RBP))]
OL_mastertable_RO=OL_mastertable[OL_mastertable$RBP=="TROVE2",]
OL_mastertable=OL_mastertable[OL_mastertable$RBP=="ADAR",]
OL_mastertable=separate(OL_mastertable,col = editing_site,into = c("chr","pos",'strand'),sep = "\\|",remove = F)
OL_mastertable$V0=paste0(OL_mastertable$chr,':',OL_mastertable$pos)
OL_mastertable_OG=OL_mastertable
# OL_mastertable=OL_mastertable[OL_mastertable$KD_editing_ratio>.1,]
# OL_mastertable=OL_mastertable[OL_mastertable$chr%in%'chr2',]
```
```{r include=F ,echo=F,warning=T}
## editingsites Online example
OL_all=merge(OL_ADAR_1_es,OL_ADAR_2_es[,c("V0","V4","V5",'editing_ratio')],by="V0",suffixes=c(".1",".2"))
OL_all=OL_all[rowSums(OL_all[,c('V4.1','V4.2')],na.rm=T)>0,]
OL_all$avgV4=rowMeans(OL_all[,c('V4.1','V4.2')],na.rm=T)
OL_all$avgV5=rowMeans(OL_all[,c('V5.1','V5.2')],na.rm=T)
OL_all$avg_editing_ratio=rowMeans(OL_all[,c('editing_ratio.1','editing_ratio.2')],na.rm=T)
OL_all$DE=0
OL_all[OL_all$V0%in%OL_mastertable$V0,"DE"]=1
OL_all=merge(OL_all,OL_mastertable[,c("V0","KD_mean_coverage","KD_editing_ratio")],by="V0",all.x=T)
OL_all0=OL_all
OL_all0[is.na(OL_all0)]=0
```
```{r include=F ,echo=F,warning=T}
########################################################################
########################################################################
a=unique(OL_all$V0)
b=unique(OL_mastertable[OL_mastertable$chr=="chr2",'V0'])
xx=(OL_mastertable[OL_mastertable$V0%in%setdiff(b,a),])
xx$KO_editing_count=xx$KD_editing_ratio*xx$KD_mean_coverage
# View(OL_mastertable[OL_mastertable$V0%in%intersect(b,a),])
OVLG_all=unique(c(a,b))
a=OVLG_all%in%a
b=OVLG_all%in%b
C3=cbind(a,b)
rownames(C3)=OVLG_all;colnames(C3)=c('OL_all_cmp','OL_master_cmp')
{vennDiagram(vennCounts(C3),main="Compair All Online list with chr2 master list",
circle.col = c("red", "blue", "green3"))
text(1,-2,'Loc in DE list only \n are loc that are not AtoG')}
########################################################################
# a=unique(OL_all[OL_all$V4.1>0|OL_all$V4.2,'V0'])
# b=unique(OL_mastertable[OL_mastertable$chr=="chr2",'V0'])
#
# OVLG_all=unique(c(a,b))
#
#
# a=OVLG_all%in%a
# b=OVLG_all%in%b
#
# C3=cbind(a,b)
# rownames(C3)=OVLG_all;colnames(C3)=c('OL_all_cmp','OL_master_cmp')
# vennDiagram(vennCounts(C3),main="Compair Online list with chr2 master list",
# circle.col = c("red", "blue", "green3"))
########################################################################
########################################################################
V5avg=rowMeans(OL_all0[,c('V5.1','V5.2')],na.rm = T)
{plot(V5avg,OL_all0$KD_mean_coverage,xlab="Average OL_all ",ylab="KD_mean_coverage\nDE Loc",main="Compare Remove Duplicates:\nRead Depth",col='red')
abline(a=0,b=1)
text(40,6,"Loc along x axis are non-DE RNA editing sites")
}
V4avg=rowMeans(OL_all0[,c('V4.1','V4.2')],na.rm = T)
KD_editing_count=OL_all0$KD_editing_ratio*OL_all0$KD_mean_coverage
{plot(V4avg,KD_editing_count,xlab="Average OL_all ",ylab="KD_mean_coverage\nDE Loc",main="Compare Remove Duplicates:\nEditing Depth",col='red')
abline(a=0,b=1)
text(20,2,"Loc along x axis are non-DE RNA editing sites")
}
```
```{r include=F ,echo=F,warning=F,eval=T}
ADAR_1_RZR_pcd <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB012QRD.Picarddedup_OLchr2testdata.vcf", verbose = FALSE )
ADAR_2_RZR_pcd <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB918BSZ.Picarddedup_OLchr2testdata.vcf", verbose = FALSE )
# Command for this VCF from freebayes
ADAR_1_RZR_pcd@meta[grepl("command",(ADAR_1_RZR_pcd@meta))]
ADAR_1_RZR_pcd <- cbind(as.data.frame(getFIX(ADAR_1_RZR_pcd)), INFO2df(ADAR_1_RZR_pcd))
ADAR_2_RZR_pcd <- cbind(as.data.frame(getFIX(ADAR_2_RZR_pcd)), INFO2df(ADAR_2_RZR_pcd))
#
# ADAR_1_RZR_pcd=ADAR_1_RZR_pcd[(ADAR_1_RZR_pcd$REF=='A'&ADAR_1_RZR_pcd$ALT=='G')|(ADAR_1_RZR_pcd$REF=='T'&ADAR_1_RZR_pcd$ALT=='C'),]
# ADAR_2_RZR_pcd=ADAR_2_RZR_pcd[(ADAR_2_RZR_pcd$REF=='A'&ADAR_2_RZR_pcd$ALT=='G')|(ADAR_2_RZR_pcd$REF=='T'&ADAR_2_RZR_pcd$ALT=='C'),]
ADAR_1_RZR_pcd=ADAR_1_RZR_pcd[grepl('A',ADAR_1_RZR_pcd$REF)&grepl('G',ADAR_1_RZR_pcd$ALT)|grepl('T',ADAR_1_RZR_pcd$REF)&grepl('C',ADAR_1_RZR_pcd$ALT),]
ADAR_2_RZR_pcd=ADAR_2_RZR_pcd[grepl('A',ADAR_2_RZR_pcd$REF)&grepl('G',ADAR_2_RZR_pcd$ALT)|grepl('T',ADAR_2_RZR_pcd$REF)&grepl('C',ADAR_2_RZR_pcd$ALT),]
# unique(ADAR_1_RZR_pcd$TYPE)
ADAR_1_RZR_pcd <- ADAR_1_RZR_pcd[grep("snp",ADAR_1_RZR_pcd$TYPE),]
ADAR_2_RZR_pcd <- ADAR_2_RZR_pcd[grep("snp",ADAR_2_RZR_pcd$TYPE),]
ADAR_1_RZR_pcd <- ADAR_1_RZR_pcd[(duplicated(ADAR_1_RZR_pcd)==F),]
ADAR_2_RZR_pcd <- ADAR_2_RZR_pcd[(duplicated(ADAR_2_RZR_pcd)==F),]
########
```
```{r include=F ,echo=F,warning=F,eval=T}
ADAR_1_RZR_rmdup <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB012QRD.fm.mkdedup_OLchr2testdata.vcf", verbose = FALSE )
ADAR_2_RZR_rmdup <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB918BSZ.fm.mkdedup_OLchr2testdata.vcf", verbose = FALSE )
# Command for this VCF from freebayes
ADAR_2_RZR_rmdup@meta[grepl("command",(ADAR_2_RZR_rmdup@meta))]
ADAR_1_RZR_rmdup <- cbind(as.data.frame(getFIX(ADAR_1_RZR_rmdup)), INFO2df(ADAR_1_RZR_rmdup))
ADAR_2_RZR_rmdup <- cbind(as.data.frame(getFIX(ADAR_2_RZR_rmdup)), INFO2df(ADAR_2_RZR_rmdup))
# ADAR_1_RZR_rmdup=ADAR_1_RZR_rmdup[(ADAR_1_RZR_rmdup$REF=='A'&ADAR_1_RZR_rmdup$ALT=='G')|(ADAR_1_RZR_rmdup$REF=='T'&ADAR_1_RZR_rmdup$ALT=='C'),]
# ADAR_2_RZR_rmdup=ADAR_2_RZR_rmdup[(ADAR_2_RZR_rmdup$REF=='A'&ADAR_2_RZR_rmdup$ALT=='G')|(ADAR_2_RZR_rmdup$REF=='T'&ADAR_2_RZR_rmdup$ALT=='C'),]
ADAR_1_RZR_rmdup=ADAR_1_RZR_rmdup[grepl('A',ADAR_1_RZR_rmdup$REF)&grepl('G',ADAR_1_RZR_rmdup$ALT)|grepl('T',ADAR_1_RZR_rmdup$REF)&grepl('C',ADAR_1_RZR_rmdup$ALT),]
ADAR_2_RZR_rmdup=ADAR_2_RZR_rmdup[grepl('A',ADAR_2_RZR_rmdup$REF)&grepl('G',ADAR_2_RZR_rmdup$ALT)|grepl('T',ADAR_2_RZR_rmdup$REF)&grepl('C',ADAR_2_RZR_rmdup$ALT),]
# unique(ADAR_1_RZR_rmdup$TYPE)
ADAR_1_RZR_rmdup <- ADAR_1_RZR_rmdup[grep("snp",ADAR_1_RZR_rmdup$TYPE),]
ADAR_2_RZR_rmdup <- ADAR_2_RZR_rmdup[grep("snp",ADAR_2_RZR_rmdup$TYPE),]
ADAR_1_RZR_rmdup <- ADAR_1_RZR_rmdup[(duplicated(ADAR_1_RZR_rmdup)==F),]
ADAR_2_RZR_rmdup <- ADAR_2_RZR_rmdup[(duplicated(ADAR_2_RZR_rmdup)==F),]
########
```
```{r include=F ,echo=F,warning=F,eval=T}
ADAR_1_RZR_dedup <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB012QRD.dedup_OLchr2testdata.vcf", verbose = FALSE )
ADAR_2_RZR_dedup <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB918BSZ.dedup_OLchr2testdata.vcf", verbose = FALSE )
# Command for this VCF from freebayes
ADAR_1_RZR_dedup@meta[grepl("command",(ADAR_1_RZR_dedup@meta))]
ADAR_1_RZR_dedup <- cbind(as.data.frame(getFIX(ADAR_1_RZR_dedup)), INFO2df(ADAR_1_RZR_dedup))
ADAR_2_RZR_dedup <- cbind(as.data.frame(getFIX(ADAR_2_RZR_dedup)), INFO2df(ADAR_2_RZR_dedup))
ADAR_1_RZR_dedupOG=ADAR_1_RZR_dedup
ADAR_2_RZR_dedupOG=ADAR_2_RZR_dedup
# ADAR_1_RZR_dedup=ADAR_1_RZR_dedup[(ADAR_1_RZR_dedup$REF=='A'&ADAR_1_RZR_dedup$ALT=='G')|(ADAR_1_RZR_dedup$REF=='T'&ADAR_1_RZR_dedup$ALT=='C'),]
# ADAR_2_RZR_dedup=ADAR_2_RZR_dedup[(ADAR_2_RZR_dedup$REF=='A'&ADAR_2_RZR_dedup$ALT=='G')|(ADAR_2_RZR_dedup$REF=='T'&ADAR_2_RZR_dedup$ALT=='C'),]
ADAR_1_RZR_dedup=ADAR_1_RZR_dedup[grepl('A',ADAR_1_RZR_dedup$REF)&grepl('G',ADAR_1_RZR_dedup$ALT)|grepl('T',ADAR_1_RZR_dedup$REF)&grepl('C',ADAR_1_RZR_dedup$ALT),]
ADAR_2_RZR_dedup=ADAR_2_RZR_dedup[grepl('A',ADAR_2_RZR_dedup$REF)&grepl('G',ADAR_2_RZR_dedup$ALT)|grepl('T',ADAR_2_RZR_dedup$REF)&grepl('C',ADAR_2_RZR_dedup$ALT),]
# unique(ADAR_1_RZR_dedup$TYPE)
ADAR_1_RZR_dedup <- ADAR_1_RZR_dedup[grep("snp",ADAR_1_RZR_dedup$TYPE),]
ADAR_2_RZR_dedup <- ADAR_2_RZR_dedup[grep("snp",ADAR_2_RZR_dedup$TYPE),]
ADAR_1_RZR_dedup <- ADAR_1_RZR_dedup[(duplicated(ADAR_1_RZR_dedup)==F),]
ADAR_2_RZR_dedup <- ADAR_2_RZR_dedup[(duplicated(ADAR_2_RZR_dedup)==F),]
########
```
## Remove Duplicates
use samtools rmdup (Dedup)
use samtools markdup -r -t (rmdup)
use picard_remvdup (pcd)
samtools markdup and picard_remvdup give same answer
```{r include=F ,echo=F,warning=F,eval=T}
# Evaluate Variant counts with with freebayes
#
# see above from VCF
```
```{r include=F ,echo=F,warning=F,eval=T}
######
ADAR_1_RZR_dedup=ADAR_1_RZR_dedup[ADAR_1_RZR_dedup$CHROM=="chr2",]
ADAR_2_RZR_dedup=ADAR_2_RZR_dedup[ADAR_2_RZR_dedup$CHROM=="chr2",]
ADAR_1_RZR_dedup=ADAR_1_RZR_dedup[grep("snp",ADAR_1_RZR_dedup$TYP),]
ADAR_2_RZR_dedup=ADAR_2_RZR_dedup[grep("snp",ADAR_2_RZR_dedup$TYPE),]
ADAR_1_RZR_dedup$POS=as.numeric(as.character(ADAR_1_RZR_dedup$POS))
ADAR_2_RZR_dedup$POS=as.numeric(as.character(ADAR_2_RZR_dedup$POS))
ADAR_1_RZR_dedup$AO=as.numeric(as.character(ADAR_1_RZR_dedup$AO))
ADAR_2_RZR_dedup$AO=as.numeric(as.character(ADAR_2_RZR_dedup$AO))
ADAR_1_RZR_dedup$V0=paste0(ADAR_1_RZR_dedup$CHROM,':',ADAR_1_RZR_dedup$POS)
ADAR_2_RZR_dedup$V0=paste0(ADAR_2_RZR_dedup$CHROM,':',ADAR_2_RZR_dedup$POS)
ADAR_1_RZR_dedup$editing_ratio=ADAR_1_RZR_dedup$AO/ADAR_1_RZR_dedup$DP
ADAR_2_RZR_dedup$editing_ratio=ADAR_2_RZR_dedup$AO/ADAR_2_RZR_dedup$DP
OL=merge(OL_all,ADAR_1_RZR_dedup[,c("V0",'AO','DP','editing_ratio')],all.x=T,by="V0")
OL=merge(OL,ADAR_2_RZR_dedup[,c("V0",'AO','DP','editing_ratio')],all.x=T,by="V0",suffixes=c(".1_dedup",".2_dedup"))
OL$DP_dedup=rowMeans(OL[,c('DP.1_dedup','DP.2_dedup')],na.rm = T)
OL$AO_dedup=rowMeans(OL[,c('AO.1_dedup','AO.2_dedup')],na.rm = T)
######
ADAR_1_RZR_rmdup=ADAR_1_RZR_rmdup[ADAR_1_RZR_rmdup$CHROM=="chr2",]
ADAR_2_RZR_rmdup=ADAR_2_RZR_rmdup[ADAR_2_RZR_rmdup$CHROM=="chr2",]
ADAR_1_RZR_rmdup=ADAR_1_RZR_rmdup[grep("snp",ADAR_1_RZR_rmdup$TYPE),]
ADAR_2_RZR_rmdup=ADAR_2_RZR_rmdup[grep("snp",ADAR_2_RZR_rmdup$TYPE),]
ADAR_1_RZR_rmdup$POS=as.numeric(as.character(ADAR_1_RZR_rmdup$POS))
ADAR_2_RZR_rmdup$POS=as.numeric(as.character(ADAR_2_RZR_rmdup$POS))
ADAR_1_RZR_rmdup$AO=as.numeric(as.character(ADAR_1_RZR_rmdup$AO))
ADAR_2_RZR_rmdup$AO=as.numeric(as.character(ADAR_2_RZR_rmdup$AO))
ADAR_1_RZR_rmdup$V0=paste0(ADAR_1_RZR_rmdup$CHROM,':',ADAR_1_RZR_rmdup$POS)
ADAR_2_RZR_rmdup$V0=paste0(ADAR_2_RZR_rmdup$CHROM,':',ADAR_2_RZR_rmdup$POS)
######
ADAR_1_RZR_pcd=ADAR_1_RZR_pcd[ADAR_1_RZR_pcd$CHROM=="chr2",]
ADAR_2_RZR_pcd=ADAR_2_RZR_pcd[ADAR_2_RZR_pcd$CHROM=="chr2",]
ADAR_1_RZR_pcd=ADAR_1_RZR_pcd[grep("snp",ADAR_1_RZR_pcd$TYPE),]
ADAR_2_RZR_pcd=ADAR_2_RZR_pcd[grep("snp",ADAR_2_RZR_pcd$TYPE),]
ADAR_1_RZR_pcd$POS=as.numeric(as.character(ADAR_1_RZR_pcd$POS))
ADAR_2_RZR_pcd$POS=as.numeric(as.character(ADAR_2_RZR_pcd$POS))
ADAR_1_RZR_pcd$AO=as.numeric(as.character(ADAR_1_RZR_pcd$AO))
ADAR_2_RZR_pcd$AO=as.numeric(as.character(ADAR_2_RZR_pcd$AO))
ADAR_1_RZR_pcd$V0=paste0(ADAR_1_RZR_pcd$CHROM,':',ADAR_1_RZR_pcd$POS)
ADAR_2_RZR_pcd$V0=paste0(ADAR_2_RZR_pcd$CHROM,':',ADAR_2_RZR_pcd$POS)
OL=merge(OL,ADAR_1_RZR_pcd[,c("V0",'AO','DP')],all.x=T,by="V0")
OL=merge(OL,ADAR_2_RZR_pcd[,c("V0",'AO','DP')],all.x=T,by="V0",suffixes=c(".1_pcd",".2_pcd"))
OL$DP_pcd=rowMeans(OL[,c('DP.1_pcd','DP.2_pcd')],na.rm = T)
OL$AO_pcd=rowMeans(OL[,c('AO.1_pcd','AO.2_pcd')],na.rm = T)
OL0=OL
OL0[is.na(OL0)]=0
```
```{r include=F ,echo=F,warning=F,eval=T}
colP<- colorRampPalette(c("blue", "white", "red"))(200)
a=OL[,c('V5.1',"V5.2",'DP.1_pcd','DP.2_pcd','DP.1_dedup','DP.2_dedup')]
a[is.na(a)]=0
OL_1cor=cor(a,method = c("pearson"))
x3=pheatmap(OL_1cor, cluster_rows = F,cluster_cols = F,display_numbers=T,fontsize_number=10,fontsize = 8,angle_col=45, cellwidth = 30, cellheight = 30,cutree_cols=NA, col=colP,main="All Genes \n Log Transformed")
```
```{r include=T ,echo=F,warning=F,eval=F}
{plot(OL0$V5.1,OL0$DP.1_dedup,xlab="Example Data ",ylab="Custom Alignment Counts",main="Compare Samples:\nRead Depth",pch=20,xlim=c(0,70),ylim=c(0,80))
points(OL0$V5.1,OL0$DP.2_dedup,pch=1,col='red')
abline(a=0,b=1)
legend(35,20,legend = c('OL_sample1 VS rmdup1_QRD', 'OL_sample1 VS rmdup2_BSZ'),col=c('black','red'),pch = 20)}
# {plot(OL0$V5.2,OL0$DP.1_dedup,xlab="Example Data 2",ylab="dedup",main="Alternative Editing count",pch=20,xlim=c(0,70),ylim=c(0,80))
# points(OL0$V5.2,OL0$DP.2_dedup,pch=20,col='red')
# abline(a=0,b=1)}
```
```{r include=T ,echo=F,warning=F,eval=T}
{plot(OL0$V5.1,OL0$DP.2_pcd,xlab="Example Data ",ylab="Custom Alignment Counts",main="Compare Remove Duplicates:\nRead Depth",col='red')
# points(OL0$V5.1,OL0$DP.2_pcd,col='red')
points(OL0$V5.1,OL0$DP.2_dedup,pch=20)
# points(OL0$V5.1,OL0$DP.2_dedup,pch=20,col='red')
abline(a=0,b=1)
legend(1,200,legend = c('Picard:MarkDuplicates', 'samtools:rmdup'),col=c('red','black'),pch = c(1,20))
text(12,140,"points along x axis are loc.\n not detected from Example data")}
# {plot(OL0$V5.2,OL0$DP.1_pcd,xlab="Example Data 2",ylab="Program",main="Alternative Editing count",col='red')
# # points(OL0$V5.2,OL0$DP.2_pcd,col='red')
# points(OL0$V5.2,OL0$DP.1_dedup,xlab="Example Data 2",ylab="Program",main="Alternative Editing count",pch=20)
# # points(OL0$V5.2,OL0$DP.2_dedup,pch=20,col='red')
# abline(a=0,b=1)}
```
```{r include=T ,echo=F,warning=F,eval=F}
{plot(OL0$V5.1,OL0$V5.2,xlab="Example Data-1",ylab="Example Data-2",main="Read Depth",pch=20)
abline(a=0,b=1)}
# cor(OL0$V5.1,OL0$V5.2)
# {plot(OL_1$DP,OL_1$DP.rmdup,xlab="Example Data",ylab="dedup",main="Alternative Editing count",pch=20)}
# qq(OL_1$V5,OL_1$DP.dedup,pch=20)
# qqplot(OL_1$V5,OL_1$DP.rmdup,pch=20,col='red')
# qqplot(OL_1$DP.dedup,OL_1$DP.rmdup,pch=20,col='blue')
#
#
# {points(qqnorm(OL_1$DP.dedup,pch=20,col='green'))
# points(qqnorm(OL_1$DP.rmdup,pch=20,col='red'))
# points(qqnorm(OL_1$V5,pch=20,col='blue'))}
```
```{r include=F ,echo=F,warning=F,eval=T}
# Compare Editing Count
a=OL0[,c('V4.1',"V4.2",'AO.1_pcd','AO.2_pcd','AO.1_dedup','AO.2_dedup')]
a$AO.1_pcd=as.numeric(as.character(a$AO.1_pcd));a$AO.2_pcd=as.numeric(as.character(a$AO.2_pcd))
a$AO.1_dedup=as.numeric(as.character(a$AO.1_dedup));a$AO.2_dedup=as.numeric(as.character(a$AO.2_dedup))
a[is.na(a)]=0
OL_1cor=cor(a,method = c("pearson"))
x3=pheatmap(OL_1cor, cluster_rows = T,cluster_cols = T,display_numbers=T,fontsize_number=10,fontsize = 8,angle_col=45, cellwidth = 30, cellheight = 30,cutree_cols=NA, col=colP,main="All Genes \n Log Transformed")
```
<br>
```{r include=T ,echo=F,warning=F,eval=F}
{plot(OL0$V4.1,OL0$AO.1_dedup,xlab="Example Data ",ylab="Custom Alignment Counts",main="Compare Samples:\nEditing count",pch=20,xlim=c(0,35),ylim=c(0,35))
points(OL0$V4.1,OL0$AO.2_dedup,pch=20,col='red')
abline(a=0,b=1)
legend(19,7,legend = c('OL_sample1 VS rmdup1_QRD', 'OL_sample1 VS rmdup2_BSZ'),col=c('black','red'),pch = 20)}
# {plot(OL0$V4.2,OL0$AO.1_dedup,xlab="Example Data 2",ylab="dedup",main="Alternative Editing count",pch=20,xlim=c(0,70),ylim=c(0,80))
# points(OL0$V4.2,OL0$AO.2_dedup,pch=20,col='red')
# abline(a=0,b=1)}
```
```{r include=T ,echo=F,warning=F,eval=T}
{plot(OL0$V4.1,OL0$AO.2_pcd,xlab="Example Data ",ylab="Custom Alignment Counts",main="Compare Remove Duplicates:\nEditing count",col='red')
# points(OL0$V4.1,OL0$AO.2_pcd,col='red')
points(OL0$V4.1,OL0$AO.2_dedup,pch=20)
# points(OL0$V5.1,OL0$AO.2_dedup,pch=20,col='red')
abline(a=0,b=1)
legend(19,12,legend = c('Picard:MarkDuplicates', 'samtools:rmdup'),col=c('red','black'),pch = c(1,20))}
# {plot(OL0$V5.2,OL0$AO.1_pcd,xlab="Example Data 2",ylab="Program",main="Alternative Editing count",col='red')
# # points(OL0$V5.2,OL0$AO.2_pcd,col='red')
# points(OL0$V5.2,OL0$AO.1_dedup,xlab="Example Data 2",ylab="Program",main="Alternative Editing count",pch=20)
# # points(OL0$V5.2,OL0$AO.2_dedup,pch=20,col='red')
# abline(a=0,b=1)}
# {plot(OL0$V5.1,OL0$V5.2,xlab="Example Data",ylab="dedup",main="Alternative Editing count",pch=20)
# abline(a=0,b=1)}
```
<br>
Samtools rmdup/Picard remvdup result in higher counts than counts supplied from paper.
<br>
## Variant caller
freebayes vs mpilup | bcftools
```{r include=F ,echo=F,warning=F,eval=T}
ADAR_1_RZR_dedup_PU <- read.vcfR( "../RASER/mpileup/HepG2_ADAR_ENCLB012QRD.dedup.bam_mpile_chr2.vcf", verbose = FALSE )
ADAR_2_RZR_dedup_PU <- read.vcfR( "../RASER/mpileup/HepG2_ADAR_ENCLB918BSZ.dedup.bam_mpile_chr2.vcf", verbose = FALSE )
ADAR_1_RZR_dedup_PU <- cbind(as.data.frame(getFIX(ADAR_1_RZR_dedup_PU)), INFO2df(ADAR_1_RZR_dedup_PU))
ADAR_2_RZR_dedup_PU <- cbind(as.data.frame(getFIX(ADAR_2_RZR_dedup_PU)), INFO2df(ADAR_2_RZR_dedup_PU))
# ADAR_1_RZR_dedup_PU=ADAR_1_RZR_dedup_PU[(ADAR_1_RZR_dedup_PU$REF=='A'&ADAR_1_RZR_dedup_PU$ALT=='G')|(ADAR_1_RZR_dedup_PU$REF=='T'&ADAR_1_RZR_dedup_PU$ALT=='C'),]
# ADAR_2_RZR_dedup_PU=ADAR_2_RZR_dedup_PU[(ADAR_2_RZR_dedup_PU$REF=='A'&ADAR_2_RZR_dedup_PU$ALT=='G')|(ADAR_2_RZR_dedup_PU$REF=='T'&ADAR_2_RZR_dedup_PU$ALT=='C'),]
ADAR_1_RZR_dedup_PU <- ADAR_1_RZR_dedup_PU[(duplicated(ADAR_1_RZR_dedup_PU)==F),]
ADAR_1_RZR_dedup_PU <- ADAR_1_RZR_dedup_PU[(duplicated(ADAR_1_RZR_dedup_PU)==F),]
########
```
```{r include=F ,echo=F,warning=F,eval=T}
######
ADAR_1_RZR_dedup_PU=ADAR_1_RZR_dedup_PU[ADAR_1_RZR_dedup_PU$CHROM=="chr2",]
ADAR_2_RZR_dedup_PU=ADAR_2_RZR_dedup_PU[ADAR_2_RZR_dedup_PU$CHROM=="chr2",]
ADAR_1_RZR_dedup_PU$POS=as.numeric(as.character(ADAR_1_RZR_dedup_PU$POS))
ADAR_2_RZR_dedup_PU$POS=as.numeric(as.character(ADAR_2_RZR_dedup_PU$POS))
ADAR_1_RZR_dedup_PU$V0=paste0(ADAR_1_RZR_dedup_PU$CHROM,':',ADAR_1_RZR_dedup_PU$POS)
ADAR_2_RZR_dedup_PU$V0=paste0(ADAR_2_RZR_dedup_PU$CHROM,':',ADAR_2_RZR_dedup_PU$POS)
ADAR_1_RZR_dedup_PU=separate(ADAR_1_RZR_dedup_PU,DP4,into = c('A','B','C','D'),sep = ",",remove = F)
ADAR_1_RZR_dedup_PU$C=as.numeric(ADAR_1_RZR_dedup_PU$C);ADAR_1_RZR_dedup_PU$D=as.numeric(ADAR_1_RZR_dedup_PU$D)
ADAR_1_RZR_dedup_PU$ALTnum=rowSums((ADAR_1_RZR_dedup_PU[,c('C','D')]))
ADAR_2_RZR_dedup_PU=separate(ADAR_2_RZR_dedup_PU,DP4,into = c('A','B','C','D'),sep = ",",remove = F)
ADAR_2_RZR_dedup_PU$C=as.numeric(ADAR_2_RZR_dedup_PU$C);ADAR_2_RZR_dedup_PU$D=as.numeric(ADAR_2_RZR_dedup_PU$D)
ADAR_2_RZR_dedup_PU$ALTnum=rowSums((ADAR_2_RZR_dedup_PU[,c('C','D')]))
OLvarcall=merge(OL,ADAR_1_RZR_dedup_PU[,c("V0",'DP','ALTnum')],all.x=T,by="V0")
OLvarcall=merge(OLvarcall,ADAR_2_RZR_dedup_PU[,c("V0",'DP','ALTnum')],all.x=T,by="V0",suffixes=c(".1_dedup_PU",".2_dedup_PU"))
OLvarcall0=OLvarcall
OLvarcall0[is.na(OLvarcall0)]=0
```
```{r include=T ,echo=F,warning=F,eval=F}
a=OLvarcall0[,c('V4.1',"V4.2",'DP.1_dedup_PU','DP.2_dedup_PU','DP.1_dedup','DP.2_dedup')]
a$DP.1_dedup=as.numeric(as.character(a$DP.1_dedup));a$DP.2_dedup=as.numeric(as.character(a$DP.2_dedup))
a[is.na(a)]=0
OL_1cor=cor(a,method = c("pearson"))
x3=pheatmap(OL_1cor, cluster_rows = T,cluster_cols = T,display_numbers=T,fontsize_number=10,fontsize = 8,angle_col=45, cellwidth = 30, cellheight = 30,cutree_cols=NA, col=colP,main="All Genes \n Log Transformed")
```
```{r include=T ,echo=F,warning=F,eval=F}
{plot(OLvarcall0$V5.1,OLvarcall0$DP.1_dedup_PU,xlab="Example Data ",ylab="Custom Alignment Counts",main="Compare samples with mpilup | bcftools: \nRead Depth", pch=20,xlim=c(0,80),ylim=c(0,80))
points(OLvarcall0$V5.1,OLvarcall0$DP.2_dedup_PU,pch=1,col='red')
abline(a=0,b=1)
legend(40,18,legend = c('OL_sample1 VS rmdup_PU1_QRD', 'OL_sample1 VS rmdup_PU2_BSZ'),col=c('black','red'),pch = 20)}
# {plot(OLvarcall0$V5.2,OLvarcall0$DP.1_dedup_PU,xlab="Example Data 2",ylab="dedup",main="Alternative Editing count",pch=20,xlim=c(0,35),ylim=c(0,35))
# points(OLvarcall0$V5.2,OLvarcall0$DP.2_dedup_PU,pch=20,col='red')
# abline(a=0,b=1)}
```
```{r include=T ,echo=F,warning=F,eval=T}
{plot(OLvarcall0$V5.1,OLvarcall0$DP.2_dedup_PU,xlab="Example Data ",ylab="Custom Alignment Counts",main="Compare samples Variant Callers: \nRead Depth",col='red',xlim=c(0,80),ylim=c(0,80))
points(OLvarcall0$V5.1,OLvarcall0$DP.2_dedup,xlab="Example Data 1",ylab="Program",main="Alternative Editing count",pch=20)
abline(a=0,b=1)
legend(40,18,legend = c('mpileup | bcftools', 'freebayes'),col=c('red','black'),pch = c(1,20))}
```
```{r include=T ,echo=F,warning=F,eval=T}
{plot(OLvarcall0$V4.1,OLvarcall0$ALTnum.2_dedup_PU,xlab="Example Data ",ylab="Custom Alignment Counts",main="Compare samples Variant Callers: \nEditing count",col='red',xlim=c(0,40),ylim=c(0,40))
points(OLvarcall0$V4.1,OLvarcall0$AO.2_dedup,xlab="Example Data 1",ylab="Program",main="Alternative Editing count",pch=20)
abline(a=0,b=1)
legend(20,9,legend = c('mpileup | bcftools', 'freebayes'),col=c('red','black'),pch = c(1,20))}
```
<br>
freebayes method identifies more of the variant positions than the mpileup | bcftools method.
The counts for the positions that are identified are the same for both methods.
<br>
# Test counts filters after calling SNPs
**Compare Averaged data from both ADAR1 samples**
Given the list of possible RNAediting sites from chr2, determine how counts filters effect ability to reproduce data.
Only call variants at sites identified from online data.
<br>
**Example data Editing sites:**
Average Read depth > 0
**Freebayes filter:**
Variant read count > 1
total read depth > 5
variant frequency > 10%
```{r include=F ,echo=F,warning=F,eval=F}
OL3
```
```{r include=F ,echo=F,warning=F,eval=T}
ADAR_1_RZR_dedup_flt <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB012QRD.dedup_filter_OLchr2testdata.vcf", verbose = FALSE )
ADAR_2_RZR_dedup_flt <- read.vcfR( "../RASER/HepG2_ADAR_ENCLB918BSZ.dedup_filter_OLchr2testdata.vcf", verbose = FALSE )
# Command for this VCF from freebayes
ADAR_1_RZR_dedup_flt@meta[grepl("command",(ADAR_1_RZR_dedup_flt@meta))]
ADAR_1_RZR_dedup_flt <- cbind(as.data.frame(getFIX(ADAR_1_RZR_dedup_flt)), INFO2df(ADAR_1_RZR_dedup_flt))
ADAR_2_RZR_dedup_flt <- cbind(as.data.frame(getFIX(ADAR_2_RZR_dedup_flt)), INFO2df(ADAR_2_RZR_dedup_flt))
ADAR_1_RZR_dedup_fltOG = ADAR_1_RZR_dedup_flt
ADAR_2_RZR_dedup_fltOG = ADAR_2_RZR_dedup_flt
ADAR_1_RZR_dedup_flt$AO=as.numeric(ADAR_1_RZR_dedup_flt$AO)
ADAR_2_RZR_dedup_flt$AO=as.numeric(ADAR_2_RZR_dedup_flt$AO)
ADAR_1_RZR_dedup_flt$POS=as.numeric(as.character(ADAR_1_RZR_dedup_flt$POS))
ADAR_2_RZR_dedup_flt$POS=as.numeric(as.character(ADAR_2_RZR_dedup_flt$POS))
ADAR_1_RZR_dedup_flt$POS=as.numeric(as.character(ADAR_1_RZR_dedup_flt$POS))
ADAR_2_RZR_dedup_flt$POS=as.numeric(as.character(ADAR_2_RZR_dedup_flt$POS))
# ADAR_1_RZR_dedup_flt=ADAR_1_RZR_dedup_flt[(ADAR_1_RZR_dedup_flt$REF=='A'&ADAR_1_RZR_dedup_flt$ALT=='G')|(ADAR_1_RZR_dedup_flt$REF=='T'&ADAR_1_RZR_dedup_flt$ALT=='C'),]
# ADAR_2_RZR_dedup_flt=ADAR_2_RZR_dedup_flt[(ADAR_2_RZR_dedup_flt$REF=='A'&ADAR_2_RZR_dedup_flt$ALT=='G')|(ADAR_2_RZR_dedup_flt$REF=='T'&ADAR_2_RZR_dedup_flt$ALT=='C'),]
ADAR_1_RZR_dedup_flt=ADAR_1_RZR_dedup_flt[grepl('A',ADAR_1_RZR_dedup_flt$REF)&grepl('G',ADAR_1_RZR_dedup_flt$ALT)|grepl('T',ADAR_1_RZR_dedup_flt$REF)&grepl('C',ADAR_1_RZR_dedup_flt$ALT),]
ADAR_2_RZR_dedup_flt=ADAR_2_RZR_dedup_flt[grepl('A',ADAR_2_RZR_dedup_flt$REF)&grepl('G',ADAR_2_RZR_dedup_flt$ALT)|grepl('T',ADAR_2_RZR_dedup_flt$REF)&grepl('C',ADAR_2_RZR_dedup_flt$ALT),]
#
ADAR_1_RZR_dedup_flt <- ADAR_1_RZR_dedup_flt[(duplicated(ADAR_1_RZR_dedup_flt)==F),]
ADAR_1_RZR_dedup_flt <- ADAR_1_RZR_dedup_flt[(duplicated(ADAR_1_RZR_dedup_flt)==F),]
########
```
```{r include=F ,echo=F,warning=F,eval=T}
######
ADAR_1_RZR_dedup_flt=ADAR_1_RZR_dedup_flt[ADAR_1_RZR_dedup_flt$CHROM=="chr2",]
ADAR_2_RZR_dedup_flt=ADAR_2_RZR_dedup_flt[ADAR_2_RZR_dedup_flt$CHROM=="chr2",]
ADAR_1_RZR_dedup_flt=ADAR_1_RZR_dedup_flt[grep("snp",ADAR_1_RZR_dedup_flt$TYPE),]
ADAR_2_RZR_dedup_flt=ADAR_2_RZR_dedup_flt[grep("snp",ADAR_2_RZR_dedup_flt$TYPE),]
ADAR_1_RZR_dedup_flt$V0=paste0(ADAR_1_RZR_dedup_flt$CHROM,':',ADAR_1_RZR_dedup_flt$POS)
ADAR_2_RZR_dedup_flt$V0=paste0(ADAR_2_RZR_dedup_flt$CHROM,':',ADAR_2_RZR_dedup_flt$POS)
### currently did not include freq filter so adding it in now
ADAR_1_RZR_dedup_flt$editing_ratio=ADAR_1_RZR_dedup_flt$AO/ADAR_1_RZR_dedup_flt$DP
ADAR_2_RZR_dedup_flt$editing_ratio=ADAR_2_RZR_dedup_flt$AO/ADAR_2_RZR_dedup_flt$DP
ADAR_1_RZR_dedup_fltbyER=unique(ADAR_1_RZR_dedup_flt[(ADAR_1_RZR_dedup_flt$editing_ratio<=.1|is.na(ADAR_1_RZR_dedup_flt$editing_ratio)),'V0'])
ADAR_2_RZR_dedup_fltbyER=unique(ADAR_2_RZR_dedup_flt[(ADAR_2_RZR_dedup_flt$editing_ratio<=.1|is.na(ADAR_2_RZR_dedup_flt$editing_ratio)),'V0'])
ADAR_1_RZR_dedup_flt=ADAR_1_RZR_dedup_flt[((ADAR_1_RZR_dedup_flt$editing_ratio>.1)),]
ADAR_1_RZR_dedup_flt=ADAR_1_RZR_dedup_flt[is.na(ADAR_1_RZR_dedup_flt$V0)==F,]
ADAR_2_RZR_dedup_flt=ADAR_2_RZR_dedup_flt[((ADAR_2_RZR_dedup_flt$editing_ratio>.1)),]
ADAR_2_RZR_dedup_flt=ADAR_2_RZR_dedup_flt[is.na(ADAR_2_RZR_dedup_flt$V0)==F,]
OL3=merge(OL,ADAR_1_RZR_dedup_flt[,c("V0",'AO','DP','editing_ratio')],all.x=T,by="V0")
OL3=merge(OL3,ADAR_2_RZR_dedup_flt[,c("V0",'AO','DP','editing_ratio')],all.x=T,by="V0",suffixes=c(".1_dedup_flt",".2_dedup_flt"))
OL3=OL3[,setdiff(colnames(OL3),c('AO.1_pcd','DP.1_pcd','AO.2_pcd','DP.2_pcd','DP_pcd','AO_pcd'))]
OL3=OL3[duplicated(OL3)==F,]
OL3$DP_dedup_flt=rowMeans(OL3[,c('DP.1_dedup_flt','DP.2_dedup_flt')],na.rm = T)
# OL3$AO_dedup_flt=rowMeans(OL3[,c('AO.1_dedup_flt','AO.2_dedup_flt')],na.rm = T)
OL30=OL3
OL30[is.na(OL30)]=0
```
```{r include=F ,echo=F,warning=F,eval=T}
a=OL30[,c('V4.1',"V4.2",'DP.1_dedup_flt','DP.2_dedup_flt','DP.1_dedup','DP.2_dedup')]
a$DP.1_dedup=as.numeric(as.character(a$DP.1_dedup));a$DP.2_dedup=as.numeric(as.character(a$DP.2_dedup))
a$DP.1_dedup_flt=as.numeric(as.character(a$DP.1_dedup_flt));a$DP.2_dedup_flt=as.numeric(as.character(a$DP.2_dedup_flt))
a[is.na(a)]=0
OL_1cor=cor(a,method = c("pearson"))
x3=pheatmap(OL_1cor, cluster_rows = F,cluster_cols = F,display_numbers=T,fontsize_number=10,fontsize = 8,angle_col=45, cellwidth = 30, cellheight = 30,cutree_cols=NA, col=colP,main="All Genes \n Log Transformed")
```
```{r include=F ,echo=F,warning=F,eval=F}
## VenDiagram shows all possible Variants that overlap with Online coordinates (using bam range for each cordinate -1 through +1)
######3
a=OL3$V0
# a=unique(c(OL_ADAR_1_es[,"V0"],OL_ADAR_2_es[,"V0"]));
# a=OL3[OL3$DE==1,'V0']
b=unique(c(ADAR_1_RZR_dedup_flt[,"V0"],ADAR_2_RZR_dedup_flt[,"V0"]));
# b=b[is.na(b)==F]
c=unique(c(ADAR_1_RZR_dedup[,"V0"],ADAR_2_RZR_dedup[,"V0"]));
# c=c[is.na(c)==F]
OVLG_all=unique(c(a,b,c))
# OVLG_all=unique(c(b,c))
OVLG_all=OVLG_all[is.na(OVLG_all)==F]
a=OVLG_all%in%a
b=OVLG_all%in%b
c=OVLG_all%in%c
C3=cbind(a,b,c)
# C3=cbind(b,c)
rownames(C3)=OVLG_all;colnames(C3)=c('Example_Data','IdentifiedVariants_filtered' ,'IdentifiedVariants_unfiltered')
# rownames(C3)=OVLG_all;colnames(C3)=c('IdentifiedVariants_filtered' ,'IdentifiedVariants_unfiltered')
{vennDiagram(vennCounts(C3),
circle.col = c("red", "blue", "green3"),
cex = 1)
title('Filtered data refer to freebayes filters')}
remove(a,b,c,OVLG_all,C3)
```
```{r include=T ,echo=F,warning=F,eval=T}
## VenDiagram shows only Variants At positions that overlap exactly with Example data
### criteria: Read dpeth >0
a=OL3$V0
b=c(OL3[OL3$DP.1_dedup_flt>0&(is.na(OL3$DP.1_dedup_flt)==F),"V0"],OL3[OL3$DP.2_dedup_flt>0&(is.na(OL3$DP.2_dedup_flt)==F),"V0"]);
# b=b[is.na(b)==F]
c=c(OL3[OL3$DP.1_dedup>0&(is.na(OL3$DP.1_dedup)==F),"V0"],OL3[OL3$DP.2_dedup>0&(is.na(OL3$DP.2_dedup)==F),"V0"]);
# c=c[is.na(c)==F]
d=OL3[OL3$DE==1,'V0']
OVLG_all=unique(c(a,b,c))
# OVLG_all=unique(c(a,b,c,d))
OVLG_all=OVLG_all[is.na(OVLG_all)==F]
a=OVLG_all%in%a
b=OVLG_all%in%b
c=OVLG_all%in%c
# d=OVLG_all%in%d
C3=cbind(a,b,c)
# C3=cbind(a,b,c,d)
rownames(C3)=OVLG_all;colnames(C3)=c('Example_Data','IdentifiedVariants_filtered' ,'IdentifiedVariants_unfiltered')
# rownames(C3)=OVLG_all;colnames(C3)=c('Example_Data','IdentifiedVariants_filtered' ,'IdentifiedVariants_unfiltered','DE Genes')
{vennDiagram(vennCounts(C3),
circle.col = c("red", "blue", "green3",'orange'),
cex = 1)
remove(a,b,c,OVLG_all,C3)
title('Filtered data refer to freebayes filters')}
```
```{r include=T ,echo=F,warning=F,eval=T}
a=OL3$V0
b=c(OL3[OL3$DP.1_dedup_flt>0&(is.na(OL3$DP.1_dedup_flt)==F),"V0"],OL3[OL3$DP.2_dedup_flt>0&(is.na(OL3$DP.2_dedup_flt)==F),"V0"]);
c=c(OL3[OL3$DP.1_dedup>0&(is.na(OL3$DP.1_dedup)==F),"V0"],OL3[OL3$DP.2_dedup>0&(is.na(OL3$DP.2_dedup)==F),"V0"]);
OVLG_all=unique(c(a,b,c))
OVLG_all=OVLG_all[is.na(OVLG_all)==F]
aa=OVLG_all%in%a
bb=OVLG_all%in%b
cc=OVLG_all%in%c
# d=OVLG_all%in%d
C3=cbind(aa,bb,cc)
OL3=OL3
OL3$filter=NA
OL3[OL3$V0%in%(OVLG_all[C3[,'aa']==T&C3[,'bb']==F&C3[,'cc']==T]),'filter']='lost by Freebayes count filter'
OL3[OL3$V0%in%(OVLG_all[C3[,'aa']==T&C3[,'bb']==T&C3[,'cc']==T]),'filter']='No loss'
OL3[OL3$V0%in%(OVLG_all[C3[,'aa']==T&C3[,'bb']==F&C3[,'cc']==F]),'filter']='Not Identified'
# OL3=OL3[OL3$filter!='No loss',]
ggplot(OL3, aes(x=filter, y=avgV4, fill=filter)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.7, alpha=0.9) +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Editing count") +
xlab("")+ylab("Average Editing count (Example Data)")+ylim(0,40)+
annotate('text',x=3,y=35,label="Rare variant counts\nAverave values all under 2")+
annotate('text',x=1,y=35,label="Mostly rare variant counts\nValues > 2 were removed\nbecause editing freq <0.1")+
annotate('text',x=2,y=35,label="Low values < 2 still kept because\nediting ratio > .1 \n average values lower because \nhigh in only one sample ")
# OL3=OL3[OL3$filter!='No loss',]
ggplot(OL3, aes(x=filter, y=avgV5, fill=filter)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.7, alpha=0.9) +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Total count") +
xlab("")+ylab("Average count (Example Data)")
# OL3=OL3[OL3$filter!='No loss',]
ggplot(OL3, aes(x=filter, y=avg_editing_ratio, fill=filter)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.7, alpha=0.9) +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Editing ratio") +
xlab("")+ylab("Average Editing Ratio (Example Data)")
```
```{r include=F ,echo=F,warning=F,eval=T}
x=OL3[,c('V0','V1','V2','V3','avgV4','avgV5','filter')]
colnames(x)=c('Location','chr','coordinate','Editing_type','editng_Count(average)','Total_count(average)','filter')
write.table(x,file=("./data/AverageExprssion_Exampledata_freebayescounts.txt"), sep = "\t", row.names = FALSE, col.names = T, append = F, quote= FALSE)
```
data_table:
[AverageExprssion_Exampledata_freebayescounts.txt](./data/AverageExprssion_Exampledata_freebayescounts.txt)
<br>
Missed Editing sites reflect the imposed filters when calling variants.
Variants that were not detected have editing count less then 2 in online example.
```{r include=F ,echo=F,warning=F,eval=F}
# The Variant lost after Freebayes with filters is a result of low allele frequency
# eg. chr2:24223463
# Online Data : Coverage = 17, editing count = 2
# Custom Call: Coverage = 25, editing count = 2 - so below eding frequency
```
```{r include=F ,echo=F,warning=F,eval=T}
# plot_multi_histogram <- function(df, feature, label_column) {
# plt <- ggplot(df, aes(x=eval(parse(text=feature)), fill=eval(parse(text=label_column)))) +
# geom_histogram(alpha=0.7, position="identity", aes(y = ..density..), color="black") +
# geom_density(alpha=0.7) +
# geom_vline(aes(xintercept=mean(eval(parse(text=feature)))), color="black", linetype="dashed", size=1) +
# labs(x=feature, y = "Density")
# plt + guides(fill=guide_legend(title=label_column))
# }
# plot_multi_histogram(OL3,'avgV4','filter')+xlim(0,10)
OL30=OL3
# OL30=OL30[OL30$filter!='No loss',]
OL30[is.na(OL30)]=0
ggplot(OL3, aes(x=DP_dedup, y=avgV5, color=filter)) +
geom_point() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
theme(
plot.title = element_text(size=11)
)
# ggtitle("Editing count") +
ggplot(OL3, aes(x=AO_dedup, y=avgV4, color=filter)) +
geom_point() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
theme(
plot.title = element_text(size=11)
)
ggplot(OL3, aes(x=AO_dedup, y=avgV4, color=filter,size=filter)) +
geom_point() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
scale_size_manual(values = c(5,3,1))+
theme(
plot.title = element_text(size=11)
) +xlim(0,5)+ylim(0,5)
```
```{r include=F ,echo=F,warning=F,eval=F}
ex=OL3[OL3$filter=='lost by Freebayes minimum count filter',]
View(ex[ex$DP_dedup>20,])
ex2=OL3[OL3$filter=='No loss',]
# View(ex2[ex2$DP_dedup>5|ex2$DP_dedup<20,])
{plot(ex$avgV4,ex$AO_dedup)
points(ex2$avgV4,ex2$AO_dedup,col='red',pch=20)}
xxx
```
```{r include=F ,echo=F,warning=F,eval=T}
## VenDiagram shows only Variants At Identified Example
a=OL3$V0
b=c(OL3[OL3$DP.1_dedup_flt>0&(is.na(OL3$DP.1_dedup_flt)==F),"V0"],OL3[OL3$DP.2_dedup_flt>0&(is.na(OL3$DP.2_dedup_flt)==F),"V0"]);
# b=b[is.na(b)==F]
# c=c(OL3[OL3$DP.1_dedup>0&(is.na(OL3$DP.1_dedup)==F),"V0"],OL3[OL3$DP.2_dedup>0&(is.na(OL3$DP.2_dedup)==F),"V0"]);
# c=c[is.na(c)==F]
d=OL3[OL3$DE==1,'V0']
OVLG_all=unique(c(a,b,d))
# OVLG_all=unique(c(a,b,c,d))
OVLG_all=OVLG_all[is.na(OVLG_all)==F]
a=OVLG_all%in%a
b=OVLG_all%in%b
# c=OVLG_all%in%c
d=OVLG_all%in%d
# C3=cbind(a,b,c,d)
C3=cbind(a,b,d)
rownames(C3)=OVLG_all;colnames(C3)=c('Example_Data','IdentifiedVariants_filtered' ,"Example_DE")
# rownames(C3)=OVLG_all;colnames(C3)=c('Example_Data','IdentifiedVariants_filtered' ,'IdentifiedVariants_unfiltered',"Example_DE")
#
vennDiagram(vennCounts(C3),
circle.col = c("red", "blue",'orange'),
cex = .9)
remove(a,b,d,OVLG_all,C3)
```