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Analyse_project_MERGE_AML_ANALYSIS_NEW_share-changedparallels.r
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#library(SKAT) ## skat method
suppressMessages(library(GenomicFeatures))
suppressMessages(library(HardyWeinberg))
suppressMessages(library(Biostrings))
suppressMessages(library("BSgenome.Hsapiens.UCSC.hg19"))
#library(doMC)
suppressMessages(library(foreach))
suppressMessages(library(doSNOW))
suppressMessages(library(doParallel))
suppressMessages(library(skatMeta)) ## ridge regression
#library(SKAT) ## skat method
suppressMessages(library(GenomicFeatures))
suppressMessages(library(HardyWeinberg))
suppressMessages(library(Biostrings))
# options(width=250,max.print=5000)
code.dir<-"P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set"
setwd(code.dir)
source("annotate_SNPs_subroutines.r")
source("hwe.r")
analysis.dir<-"P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\Analysis"
annotate.dir<-"P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\Annotate"
project.extension<-".All-maf-filtered.txt"
project.name<-"2015-01-27_AML_with_AOGCControl" ## prefix for output file
fam<-c("TGCM-AML") # ALL or c() ""-one project (the prefix of the summary files to collect
the.sample.sheet<-"P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\TGCM-AML-combine_SampleSheet.csv"
remove.from.controls<-c() # expand.labels.to.samples(remove.from.controls,control.samples)
remove.from.all.samples<-c() #expand.labels.to.samples(remove.from.all.samples,all.samples)
remove.cols<-c()
#regions.file<-"/media/scratch2/AOGC-NGS/GFOS/gefos.seq/METHODS/0613-skatmeta-gefos/static/Homo_sapiens.GRCh37.70.protein_coding.genespace_boundaries.5k.split100k.txt"
core.ann<-c("chr","start","end","REF","ALT","TYPE") # out put to annanlsys programs and need foe colun labels
dont.build.summary<-FALSE ##
GATK.SB<-TRUE
maf.threshold.filter.to.use<-c(0.05)
a.label<-"CoVarRun.noControl.AML.regions"
###########################################################################
###################################### load ola aogc######################3
## load("/media/Bioinform-D/Research/annovar/humandb/aogc.count.data.RData")
## #print(colnames(indels))
## print(colnames(geno.aogc))
## use.key<-build.key(geno.aogc,core.ann)
## insert.location<-70 ### this is where to add AOGC data INSERTS AFTER THE LOCATION
## ################################ add aogc
## ann<-readRDS("/media/scratch2/AOGC-NGS/Analysis/AOGC_sequnnce_LS/AOGC_sequence_10_LS_ANNOTATION.rds")
geneanno.DB<-c("refGene","knownGene","ensGene") # returns 2 extra columns
names(geneanno.DB)<-c("refGene","knownGene","ensGene")
##################################################### DEFINE A GENE LIST #####################################################
##################################################### PILOT - GENE LIST #####################################################
## gene.list.file<-"/media/Bioinform-D/Research/exome Chip/AOGC_Gene_Hits_0.05_0.2-sent.txt"
## gene.list<-read.delim(gene.list.file,header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
## gene.list[1:5,]
## colnames(gene.list)[colnames(gene.list)=="chr"]<-"CHR"
## colnames(gene.list)[colnames(gene.list)=="start"]<-"START"
## colnames(gene.list)[colnames(gene.list)=="end"]<-"END"
## colnames(gene.list)[colnames(gene.list)=="value"]<-"GENE_NAME"
## gene.list[1:5,]
######################################################################################################################################
######################################################################################################################################
##################################################### GEFOS - GENE LIST #####################################################
## gene.list.file<-"/media/scratch2/AOGC-NGS/GFOS/gefos.seq/METHODS/0613-skatmeta-gefos/static/Homo_sapiens.GRCh37.70.protein_coding.genespace_boundaries.5k.split100k.txt"
## gene.list<-read.delim(gene.list.file,header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
## #target<-"CHR"
## target<-"GENE_NAME"
## #target<-"GENE_ID"
## sort(tapply(gene.list[,target],gene.list[,target],length),decreasing=TRUE)[1:150]
## order.by<-order(gene.list[,"CHR"],gene.list[,"START"],gene.list[,"END"])
## gene.list<-gene.list[order.by,]
## gene.list[1:5,]
######################################################################################################################################
######################################################################################################################################
############################################SET UP FNCTIONAL FILTERS #######################################################
####################### MUTATION TYPE DEINITIONS
possible.mutations<-c("frameshift substitution","nonframeshift substitution","downstream","frameshift deletion","frameshift insertion","intergenic","intronic","ncRNA_exonic","ncRNA_intronic","ncRNA_splicing","ncRNA_UTR3","ncRNA_UTR5","ncRNA_UTR5;ncRNA_UTR3","nonframeshift deletion","nonframeshift insertion","nonsynonymous SNV","splicing","stopgain SNV","stoploss SNV","synonymous SNV","unknown","upstream","upstream;downstream","UTR3","UTR5","UTR5;UTR3")
# select for these
interesting.coding.mutations<-c("frameshift substitution","nonframeshift substitution","nonframeshift deletion","nonframeshift insertion","frameshift deletion","frameshift insertion","nonsynonymous SNV","stopgain SNV","stoploss SNV","splicing")
interesting.mutations.use<-c("frameshift substitution","nonframeshift substitution","nonframeshift deletion","nonframeshift insertion","frameshift deletion","frameshift insertion","nonsynonymous SNV","stopgain SNV","stoploss SNV","splicing","ncRNA_exonic")
wanted.noncoding.subtypes<-c("miRNA","lincRNA") # filter by interesting to prefiler and vep.noncoding so dones get ncRNA intronic ::use gerp.score.threshold.low only these subtypes
interesting.to.prefilter<-c("UTR3","UTR5","UTR5;UTR3","snoRNA","snRNA","antisense","sense_intronic","ncRNA_exonic","ncRNA_splicing") #use gerp.score.threshold
extra.vep.annotations<-c("Uploaded_variation","Gene","Feature","Protein_position","Amino_acids")
#"not_assigned",
vep.types<-c("stop_gained","stop_lost","missense_variant","splice_acceptor_variant","splice_donor_variant","splice_region_variant","initiator_codon_variant","stop_retained_variant","incomplete_terminal_codon_variant","frameshift_variant","inframe_deletion","inframe_insertion","5_prime_UTR_variant","3_prime_UTR_variant","non_coding_exon_variant","NC_stop_gained","NC_stop_lost","NC_splice_acceptor_variant","NC_splice_donor_variant","NC_splice_region_variant","NC_initiator_codon_variant","NC_stop_retained_variant","NC_non_coding_exon_variant","NC_incomplete_terminal_codon_variant","NC_3_prime_UTR_variant","mature_miRNA_variant","NC_5_prime_UTR_variant","TF_binding_site_variant","TFBS_ablation","TFBS_amplification","regulatory_region_variant","intron_variant","NC_intron_variant","synonymous_variant","coding_sequence_variant","NC_synonymous_variant","upstream_gene_variant","downstream_gene_variant","intergenic_variant","NC_intergenic_variant","NMD_transcript_variant","nc_transcript_variant","NC_nc_transcript_variant","feature_truncation","feature_elongation")
## vep.types<-c( "not_assigned","stop_gained","stop_lost","stop_lost,NMD_transcript_variant","stop_gained,splice_region_variant,NMD_transcript_variant","initiator_codon_variant,splice_region_variant","splice_region_variant,3_prime_UTR_variant","stop_gained,NMD_transcript_variant","missense_variant,splice_region_variant","missense_variant","splice_acceptor_variant","splice_acceptor_variant,nc_transcript_variant","splice_region_variant,3_prime_UTR_variant,NMD_transcript_variant","splice_donor_variant,nc_transcript_variant","splice_region_variant,intron_variant,NMD_transcript_variant","splice_donor_variant","splice_region_variant","splice_region_variant,5_prime_UTR_variant","splice_region_variant,synonymous_variant","splice_region_variant,intron_variant,nc_transcript_variant","splice_region_variant,non_coding_exon_variant,nc_transcript_variant","missense_variant,NMD_transcript_variant","splice_region_variant,intron_variant","NMD_transcript_variant","intron_variant,NMD_transcript_variant","mature_miRNA_variant","5_prime_UTR_variant","5_prime_UTR_variant,NMD_transcript_variant","non_coding_exon_variant,nc_transcript_variant","3_prime_UTR_variant,NMD_transcript_variant","non_coding_exon_variant","TF_binding_site_variant","intron_variant,nc_transcript_variant","synonymous_variant,NMD_transcript_variant","3_prime_UTR_variant","regulatory_region_variant","upstream_gene_variant","downstream_gene_variant","intergenic_variant","intron_variant","synonymous_variant")
#"not_assigned",
vep.coding<-c("stop_gained","stop_lost","missense_variant","splice_acceptor_variant","splice_donor_variant","splice_region_variant","initiator_codon_variant","stop_retained_variant","incomplete_terminal_codon_variant","frameshift_variant","inframe_deletion","inframe_insertion")
vep.noncoding<-c("5_prime_UTR_variant","3_prime_UTR_variant","non_coding_exon_variant","NC_stop_gained","NC_stop_lost","NC_splice_acceptor_variant","NC_splice_donor_variant","NC_splice_region_variant","NC_initiator_codon_variant","NC_stop_retained_variant","NC_non_coding_exon_variant","NC_incomplete_terminal_codon_variant","NC_3_prime_UTR_variant","mature_miRNA_variant","NC_5_prime_UTR_variant","TF_binding_site_variant","TFBS_ablation","TFBS_amplification","regulatory_region_variant")
vep.unwanted<-c("intron_variant","NC_intron_variant","synonymous_variant","coding_sequence_variant","NC_synonymous_variant","upstream_gene_variant","downstream_gene_variant","intergenic_variant","NC_intergenic_variant","NMD_transcript_variant","nc_transcript_variant","NC_nc_transcript_variant","feature_truncation","feature_elongation")
missense.variant<-c("nonsynonymous SNV","missense_variant")
hwe.control.threshold<-1e-8
gerp.score.threshold.high<-2.5 # gerp score >= will be included
gerp.score.threshold.low<-2.0 # gerp score >= will be included
gerp.score.threshold.unknown<-0
#generic.filter.DB
maf.threshold.filter.to.use<-sort(maf.threshold.filter.to.use)
maf.threshold.filter<-maf.threshold.filter.to.use
interesting.mutations<-interesting.mutations.use
if(GATK.SB) {
global.quality.labs<-c("QUAL","QD","HRun","SB","FILTER","FILTER","PolyPhen.scores","PolyPhen.scores","SIFT.scores","mut.taster::score","phylo::score","PolyPhen.desc","SIFT.desc","GERP::score","GERP::score","GERP::score","MAF.ALL","MAF.HIGH","MAF.LOW","TYPE") ### THESE ARE THE COLUMN LABELS IN THE DATA these become the "good.qual" filter
global.quality.names<-c("QUAL","QD","HRun","SB","FILTER_PASS","FILTER_100","PolyPhen.low","PolyPhen.high","SIFT.high","mut.taster.high","phylo.high","PolyPhen.bad","SIFT.bad","GERP.high","GERP.low","GERP.unknown","MAF.ALL","MAF.HIGH","MAF.LOW","flat") ### THESE ARE THE COLUMN LABELS IN the quality.filter TABLE
#global.quality.cut<-c(50,0.5,5,1,"PASS","TruthSensitivityTranche99.90to100.00",0.1,0.4,0.4,0.4,0.4,"damaging","deleterious",2,2,0.25,0.25,0.25)
global.quality.cut<-c(50,0.5,5,1,"PASS","TruthSensitivityTranche99.90to100.00",0.1,0.4,0.4,0.4,0.4,"damaging","deleterious",gerp.score.threshold.high,gerp.score.threshold.low,gerp.score.threshold.unknown,0.25,0.25,0.25,"flat")
global.quality.type<-c("numeric","numeric","numeric","numeric","factor","factor","numeric","numeric","numeric","numeric","numeric","factor","factor","numeric","numeric","numeric","numeric","numeric","numeric","factor")
global.quality.dirn<-c("greater","greater","less","less","exact","exact","greater","greater","greater","greater","greater","ends_with","exact","greater","greater","exact","greater","greater","greater","ends_with")
} else {
global.quality.labs<-c("QUAL","QD","HRun","FS","FILTER","FILTER") ### these become the "good.qual" filter
global.quality.names<-c("QUAL","QD","HRun","FS","FILTER_PASS","FILTER_100")
global.quality.cut<-c(50,0.5,5,60,"PASS","TruthSensitivityTranche99.90to100.00")
global.quality.type<-c("numeric","numeric","numeric","numeric","factor","factor")
global.quality.dirn<-c("greater","greater","less","less","exact","exact")
}
names(global.quality.cut)<-global.quality.labs
names(global.quality.dirn)<-global.quality.labs
names(global.quality.type)<-global.quality.names
global.labs<-unique(global.quality.labs)
global.quality.cut
global.quality.dirn
global.quality.type
quality.cut<-global.quality.cut
quality.type<-global.quality.type
quality.dirn<-global.quality.dirn
######################################################################################################################
# coverage information
seq.type.file<-"P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\QC_stat_SAMPLE_Tue_Oct_14_2014.txt"
seq.type<-read.delim(seq.type.file,header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
seq.type[1:5,]
## seq.type<-seq.type[seq.type[,"Project"]=="AOGC-NGS",]
nim.samples<-seq.type[seq.type[,"Project"]=="AOGC-NGS" & seq.type[,"Capture.Method"]=="TruD:NimX","Sample"]
ill.samples<-seq.type[seq.type[,"Project"]=="AOGC-NGS" & seq.type[,"Capture.Method"]=="TruD:TruX","Sample"]
nim.samples<-paste(nim.samples,"GT",sep=".")
ill.samples<-paste(ill.samples,"GT",sep=".")
length(nim.samples)
length(ill.samples)
############################################ Nimblegen and illuma capture loci ####################################################
the.chroms<-seqlengths(Hsapiens)
## load("/media/UQCCG/Sequencing/Data/Genomes/hg19/Human_Exome_Targets_illumina_v2_hg19_targets.RData")
## ill.gr<-data.gr
## load("/media/UQCCG/Sequencing/Data/Genomes/hg19/Human_Exome_Targets_Nimble_v2_hg19_targets.RData")
## nim.gr<-data.gr
## genome(ill.gr)<-"hg19"
## genome(nim.gr)<-"hg19"
## names(ill.gr)
## the.chromo<- as.character(unique(seqnames(ill.gr)))
## sum(the.chromo!=as.character(unique(seqnames(nim.gr))))==0 ## must be true else chr in different order
## human.chromlens<-the.chroms[the.chromo]
## ## ill.gr<-ill.gr+200
## ## nim.gr<-nim.gr+200
## ## overlaps<-overlapsAny(ill.gr,nim.gr)
## ## possible.loci.ori<-ill.gr[overlaps]
## ill.gr<-reduce(ill.gr)
## nim.gr<-reduce(nim.gr)
## cov.ill.gr<-coverage(ill.gr,weight=5,width=human.chromlens) ## Problems exist here is the ir.gerp are not unique get coverage*weight
## cov.nim.gr<-coverage(nim.gr,weight=5,width=human.chromlens) ## Problems exist here is the ir.gerp are not unique get coverage*weight
## cov.nim.gr<-cov.nim.gr[names(cov.ill.gr)] # make sure in same order
## cov.all<-cov.nim.gr+cov.ill.gr
## x<- slice(cov.all,lower=9,upper=11) ### over is 10 otherwise is 5
## x[[5]][1:5]
## x
## ########check
## regionViews<-x
## the.counts<-{}
## order.chromos<-names(regionViews)
## # ik<-13
## for (ik in 1:length(order.chromos)){
## chromo<-order.chromos[ik]
## ## print( chromo)
## a.chr<-chromo
## a.start<-start(regionViews[[chromo]])
## a.end<-end(regionViews[[chromo]])
## a.width<-width(regionViews[[chromo]])
## a.set<-cbind(a.chr,a.start,a.end,a.width)
## the.counts<-rbind(the.counts,a.set)
## }
## colnames(the.counts) <- c("chr","start","end","length")
## ## }) # system.time
## the.counts[1:5,]
## write.table(the.counts,file="Common_target_loci_between_Nimblegen_v2.and.v3.txt",col.names=TRUE,row.names=FALSE,sep="\t",quote=FALSE)
## save(list=c("the.counts"),file="Common_target_loci_between_Nimblegen_v2.and.v3.Rdata")
## getwd()
#load("/media/UQCCG/Sequencing/Data/Sequence_Genotypes/2013-10-27_AML_with_AOGCControl_NoFailedLane/Analysis/Common_target_loci_between_Nimblegen_v2.and.v3.Rdata")
#load("/media/UQCCG/Sequencing/Data/Genomes/hg19/NexteraRapidCapture_Exome_TargetedRegions_hg19_targets.RData")
######################################################################################################################################
## gene.gr<-GRanges(seqnames =gene.list[,"CHR"],ranges = IRanges(start=as.numeric(gene.list[,"START"]),end=as.numeric(gene.list[,"END"])),strand="+")
## length(gene.gr)
## length(reduce(gene.gr))
## location.key<-build.key(gene.list,c("CHR","START","END"))
## unique.locations<-unique(location.key)
## length(unique.locations)
## dim(gene.list)
## posns<-grep("GRIA1.",gene.list[,target],fixed=TRUE)
## gene.list[posns,]
## vep.coding<-c("stop_gained","stop_lost","missense_variant","initiator_codon_variant","stop_retained_variant","incomplete_terminal_codon_variant","frameshift_variant","inframe_deletion","inframe_insertion")
## vep.other.coding<-c("synonymous_variant","coding_sequence_variant")
the.sample.sheet
sample.sheet.full<-read.delim(the.sample.sheet,header=T,sep=",",fill=TRUE,stringsAsFactors=FALSE)
sample.sheet.full[1:5,]
colnames(sample.sheet.full)
dim(sample.sheet.full)
##### fix 0 and 9 for missing to NA
## pheno.types<-c("BMD_EFF_STD_HIP","BMD_EFF_STD_RAD","BMD_EFF_STD_LS","BMD_EFF_STD_FN","EVER_FX_50_EXCL_TRIVIAL")
## names(pheno.types)<-c("HIP","RAD","LS","FN","FX")
######### Check and fix the sample sheet
# coverage information
coverage<-seq.type # read.delim("/media/UQCCG/Sequencing/Data/QC for all samples summary/Coverage_QC/QC_stat_SAMPLE_Tue_Oct_14_2014.txt",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
dim(coverage)
coverage[1:5,]
unique(coverage[,"Project"])
aml.projects<-c("AML-exome","RSGB_AML","AMAS", "TGCM-AML")
aml.samples<-coverage[coverage[,"Project"] %in% aml.projects,"Sample"]
length(aml.samples)
aml.have<-sample.sheet.full[,"ParticipantCode"] %in% aml.samples
table(sample.sheet.full[,"SampleProject"])
table(sample.sheet.full[aml.have,"SampleProject"])
table(sample.sheet.full[!aml.have,"SampleProject"])
a.control<-sample.sheet.full[,"SampleProject"]=="Control"
#sample.sheet.full[aml.have & a.control,]
## cg.samples<-read.delim("/media/UQCCG/Sequencing/CompleteGenomics/917_Data_Delivery_091214.csv",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
## cg.samples[1:5,]
## recodes<-read.delim("/media/UQCCG/Sequencing/CompleteGenomics/RecodingSampleID.csv",header=T,sep=",",fill=TRUE,stringsAsFactors=FALSE)
## recodes[1:5,]
## analysis.samples<-read.delim("/media/UQCCG/Sequencing/CompleteGenomics/regions_Jonathan/rosetta.txt",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
## analysis.samples[1:5,]
posns<-match(sample.sheet.full[,"ParticipantCode"],coverage[,"Sample"])
missing<-is.na(posns)
sum(missing)
## sample.sheet.full[posns[missing],"ParticipantCode"]
## coverage[,"Sample"]
## sample.sheet.full[missing,]
## coverage<-coverage[posns,]
colnames(coverage)
Capture<-coverage[posns,"Capture.Method"]
cbind(sample.sheet.full,Capture)
############## ASSUME HERE THAT SampleProjuect has classes Control and AML
pheno.types<-c("SampleProject")
names(pheno.types)<-c("SampleProject")
table(sample.sheet.full[,pheno.types[1]])
case.control<-c("SampleProject")
case.control.classes<-c(0,1)
names(case.control.classes)<-c("Control","AML")
case.control.classes
# ib<-1
for(ib in 1:length(case.control)){
if(!(case.control[ib] %in% colnames(sample.sheet.full))){next}
sample.sheet.full[( !(sample.sheet.full[,case.control[ib]] %in% names(case.control.classes)) | is.na(sample.sheet.full[,case.control[ib]]) | sample.sheet.full[,case.control[ib]]==0 | sample.sheet.full[,case.control[ib]]==9) ,case.control[ib]]<-NA
}
#tapply(sample.sheet.full[,"SampleProject"],sample.sheet.full[,"SampleProject"],length)
sample.sheet.full[1:5,]
control.samples<-{}
colnames(sample.sheet.full)[colnames(sample.sheet.full)=="ParticipantCode"]<-"SAMPLE"
all.samples<-sample.sheet.full[,"SAMPLE"]
length(all.samples)
## o.remove.all<-expand.labels.to.samples(remove.from.all.samples,all.samples)
## to.remove.samples<-unique(to.remove.all)
## remove.cols<-unique(c(remove.cols,to.remove.samples))
# fam
#### test fam list
files<-dir(analysis.dir)
the.extension<-paste(project.extension,"$",sep="")
files<-files[grepl(the.extension ,files)]
toString( unique(unlist( mapply(function(x){x[length(x)]}, strsplit(gsub(the.extension,"",files),split=".",fixed=TRUE) ))) )
toString( files)
files
####
#############################################################################################################
######################################### Predefined variables required
##################################################################################
#### assume has format project.chr.fam.extension or chr.project.fam.extension
setwd(analysis.dir)
getwd()
files<-dir(analysis.dir)
the.extension<-paste(project.extension,"$",sep="")
files<-files[grepl(the.extension ,files)]
if(fam=="ALL" | fam=="All" | fam=="all"){
fam<-unique(unlist( mapply(function(x){x[length(x)]}, strsplit(gsub(the.extension,"",files),split=".",fixed=TRUE) )))
}
fam
## ifam<-2
## geno.aogc<-read.delim("/media/scratch2/AOGC-NGS/Analysis/AOGC-Genotyping.output.AOGC_ALL.geno.all.txt",header=T,skip=0,fill=TRUE,sep="\t",stringsAsFactors=FALSE,check.names=FALSE)
## setwd("/media/Bioinform-D/Research/annovar/humandb/")
## save(list=c("geno.aogc","use.key"),file="aogc.count.data.RData")
#load("/media/Bioinform-D/Research/annovar/humandb/aogc.count.data.RData")
#print(colnames(indels))
## print(colnames(geno.aogc))
## use.key<-build.key(geno.aogc,core.ann)
## insert.location<-70
### this is where to add AOGC data
################################ add aogc
## project.files
#
ifam<-1
#for(ifam in 1:length(fam)) {
the.extension<-paste(fam[ifam],project.extension,"$",sep="")
project.files<-files[grepl(the.extension ,files)]
print(sort(paste("Doing: ",project.files,sep=""))) # project.files<-project.files[1:22]
indels<-{}
the.col<-{}
project.files
ichr<-1
#for(ichr in 1:length(project.files)) { ### loop over chromosomes
setwd(analysis.dir)
## grep("Gene.Name",a.indel[,16])
## save(list=c("column.labels"),file="column.labels.RData")
## load("column.labels.RData")
################## fast read ###########
column.labels<-read.delim("P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\2013-10-27_AML_with_AOGCControl_NoFailedLane.TGCM-AML.All-maf-filtered.txt",header=F,nrows=1,sep="\t",fill=TRUE,stringsAsFactors=FALSE,na.strings="",quote="\"")
num.vars<-dim(column.labels)[2]
# read in file of massive data - modify
a.indel<-scan("P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\2013-10-27_AML_with_AOGCControl_NoFailedLane.TGCM-AML.All-maf-filtered.txt",what=character(num.vars),skip=1,sep="\t",fill=TRUE,na.strings="",quote="\"")
num.lines<-length(a.indel)/(num.vars)
dim(a.indel)<-c(num.vars,num.lines)
a.indel<-t(a.indel)
colnames(a.indel)<-column.labels
########################################
the.chr<-unique(a.indel[,"chr"])
print(paste("Doing Chromosome ",the.chr))
if(sum(!grepl("^chr",the.chr))>0) {
a.indel[,"chr"]<-paste("chr",a.indel[,"chr"],sep="")
}
a.indel[1:50,"chr"]
key<-build.key(a.indel,core.ann)
rownames(a.indel)<-key
rownames(a.indel)[1:5]
#tapply(gene.list[,"CHR"],gene.list[,"CHR"],length)
all.possible.samples<-gsub(".GT$","",colnames(a.indel)[grep(".GT$",colnames(a.indel))],perl=TRUE)
length(all.possible.samples)
pheno.types
## bad.samples
## colnames(a.indel)[grep("8014",colnames(a.indel))]
## dim(a.indel)
########################### REMOVE BAD SAMPLES HERE
## analysis.samples[1:5,]
## bad.samples<-as.character(c(1:101))
## bad.samples.labels<-expand.labels.to.samples(bad.samples,c("GT","AD","DP","GQ"),paste.after=TRUE)
## a.indel<-a.indel[,colnames(a.indel)[!(colnames(a.indel) %in% bad.samples.labels)]]
## all.possible.samples
## colnames(a.indel)[1:20]
a.indel[1:5,1:10]
col<-grepl("REF",a.indel[,"REF"])
a.indel[col,1:15]
a.indel<-a.indel[!col,]
colnames(a.indel)[1:50]
dim(a.indel)
sort(unique(a.indel[,"chr"]))
sort(unique(a.indel[,"FILTER"]))
test<-"AOGC-08-0287.GT"
wanted<-a.indel[,test]!="0/0" | is.na(a.indel[,test])
sum(wanted)
a.indel[wanted,c("chr","start", "end","Consequence.Embl",test)][1:10,]
sort(table(a.indel[wanted, "Consequence.Embl"]))
sort(table(a.indel[wanted, "TYPE"]))
test<-all.possible.samples
#ALT.Alleles.Control
#################################### got some missing gene names still.
## all.genes[grep("GTPBP4",names(all.genes))]
no.gene<-is.na(a.indel[,"Gene.Names"]) | a.indel[,"Gene.Names"]=="NA"
all.genes<-sort(table(a.indel[,"Gene.Names"]),decreasing=TRUE)
ens.to.hgnc<-read.delim("P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\ENSG_to_HGNC.txt",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE,na.strings="",quote="\"")
ens.to.hgnc[1:5,]
genes<-unique(a.indel[no.gene,"Gene.Embl"])
posns<-match(genes,ens.to.hgnc[,"Ensembl.Gene.ID"])
missing<-is.na(posns)
ens.to.hgnc<-ens.to.hgnc[posns[!missing],]
dups<-duplicated(ens.to.hgnc[,"Ensembl.Gene.ID"])
ens.to.hgnc<-ens.to.hgnc[!dups,]
ens.to.hgnc[1:5,]
posns<-match(a.indel[no.gene,"Gene.Embl"],ens.to.hgnc[,"Ensembl.Gene.ID"])
missing<-is.na(posns)
sum(missing)
a.indel[no.gene,"Gene.Names"]<-ens.to.hgnc[posns,"HGNC.symbol"]
a.indel[no.gene,"Gene.Names"][1:10]
ens.to.hgnc[posns,"HGNC.symbol"][1:10]
no.gene<-is.na(a.indel[,"Gene.Names"]) | a.indel[,"Gene.Names"]=="NA"
a.indel[no.gene,"Gene.Names"]<-a.indel[no.gene,"Gene.Embl"]
a.dash<-a.indel[,"Gene.Names"]=="-"
sum(a.dash)
a.indel[a.dash,][1:5,1:50]
a.indel[a.dash,"Gene.Names"]<-a.indel[a.dash,"Feature.Embl"]
a.dash<-a.indel[,"Gene.Names"]=="-"
sum(a.dash)
a.indel[a.dash,][1:5,1:50]
final.names<-a.indel[a.dash,"knownGene::gene"]
final.names<-gsub("\\(dist=\\d*\\)","",final.names)
final.names<-gsub("\\(dist=NONE\\)","",final.names)
final.names[grepl("^NO_ANNOVAR",final.names)]<-"-"
a.indel[a.dash,"Gene.Names"]<-final.names
a.dash<-a.indel[,"Gene.Names"]=="-"
sum(a.dash)
a.indel[a.dash,c("chr","start")]
final.names<-build.key(a.indel[a.dash,],c("chr","start"))
a.indel[a.dash,"Gene.Names"]<-final.names
unannotated.hits<-a.dash
all.genes<-sort(table(a.indel[,"Gene.Names"]),decreasing=TRUE)
all.genes[1:30]
## MUC4 TTN MUC16 MUC5B MUC12 FAM182B ZNF717 NBPF1 FAM182A AHNAK2
## 1357 540 366 347 326 289 284 283 279 269
## MST1L HYDIN FLG SYNE1 NBPF14 POM121L8P NEB MUC6 POM121L1P MST1P2
## 267 255 249 238 233 213 212 211 204 198
## PDE4DIP HLA-DRB1 CTBP2 MUC17 OBSCN FLJ45445 USH2A LRP1B SSPO DNAH11
## 196 192 186 184 184 178 175 172 172 167
## MUC16 MUC4 ANKRD36 TTN HLA-DRB1 SYNE1 FRG1B ZNF717 NEB
## 1176 1171 849 726 712 642 598 576 546
## FRG1 MUC12 HLA-DRB5 ANKRD30BL CSMD1 RYR1 CDC27 DNAH17 FAM182B
## 487 470 467 450 430 429 398 390 378
## DNAH11 KMT2C PKHD1 SSPO ANKRD20A8P FCGBP CTBP2 MUC5B DNAH5
## 376 364 362 355 354 347 342 341 337
## GPR98 MIR1273H LAMA5
## 334 329 328
grep("NOTCH1",names(all.genes))
common.hit.genes<-names(all.genes)[1:10] # common.hit.genes<-names(all.genes)[1:4] - top 10 most ocmmon genes
all.genes["SCN2A"]
###############################################
# tells skat what variants belong to which gene and which genes belong to which pathway
snpinfo.raw<-cbind(key,a.indel[,"Gene.Names"],a.indel[,"Gene.Names"])
snpinfo.raw[1:5,]
tail(snpinfo.raw)
colnames(snpinfo.raw)<-c("Name","gene","cluster")
dim(snpinfo.raw)
snpinfo.raw[1:5,]
dim(snpinfo.raw)
snpinfo<-snpinfo.raw
## other.clusters<-read.delim("/media/UQCCG/Sequencing/Data/Sequence_Genotypes/2013-10-27_AML_with_AOGCControl_NoFailedLane/Analysis/Final_FANC_clusters.csv",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
## other.clusters<-read.delim("/media/UQCCG/Sequencing/Data/Sequence_Genotypes/2013-10-27_AML_with_AOGCControl_NoFailedLane/Analysis/other_clusters.csv",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
## other.clusters[1:5,]
## colnames(other.clusters)
## for(ic in 1:dim(other.clusters)[2]){
## cluster.genes<-clusters[clusters[,"Cluster"]==clusters.wanted[ic],"Genes.assigned.to.group"]
## cluster.genes<-unlist(strsplit(cluster.genes,split=", "))
## cluster.genes
## last.cluster.length<-length(cluster.genes)
## if(ic==1){clinical.genes<-cluster.genes}
## if(ic==2){fanc.genes<-cluster.genes}
## snpinfo[ snpinfo[,"gene"] %in% cluster.genes ,"cluster"] <-clusters.wanted[ic]
## }
# /media/UQCCG/Sequencing/Data/Sequence_Genotypes/2013-10-27_AML_with_AOGCControl_NoFailedLane/Analysis/other_clusters.csv
#clusters<-read.delim("/media/UQCCG/Sequencing/Data/Sequence_Genotypes/2013-10-27_AML_with_AOGCControl_NoFailedLane/Analysis/Clusters Definitions.csv",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
### CLUSTERS TO CHANGE
clusters<-read.delim("P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\Final_FANC_clusters.csv",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
clusters
#clusters.wanted<-c("Clinical","FANC - ACID")
clusters.wanted<-colnames(clusters)
ic<-1
snpinfo[1:5,]
cbind(unique(clusters[,22]),unique(clusters[,22]))
snpinfo[1:5,]
gene.aliases<-read.delim("P:\\PhD\\Exome Capture Data and Mutations\\Raw Sequencing Data\\AML with Controls - FANC Set\\Gene_symbol_aliases.txt",header=T,sep="\t",fill=TRUE,stringsAsFactors=FALSE)
gene.aliases[1:5,]
gene.aliases<-unwind(gene.aliases, "Aliases",delimit=", ")
gene.aliases[1:5,]
############### all.genes
gene.aliases[grepl("SLX1",gene.aliases[,1]),]
gene.aliases[grepl("SLX1",gene.aliases[,2]),]
############FIX MISSING gene names in pathways tests
#Check clusters have genes that are missing from the sequencing list then make sure have the approved gene name
ic<-5
recode<-{}
for(ic in 1:length(clusters.wanted)){
# print(ic)
cluster.genes<-clusters[,clusters.wanted[ic]]
cluster.genes<-cluster.genes[cluster.genes!="" | is.na(cluster.genes)]
cluster.genes<-unique(cluster.genes)
# all.genes[1:5]
missing.name<-!(cluster.genes %in% names(all.genes))
if(sum( missing.name)>0) {
posns<-match(cluster.genes[missing.name],gene.aliases[, "Aliases"])
missing<-is.na(posns)
if(sum(missing)>0) {
print(paste("in cluster",clusters.wanted[ic], " missing"))
print(cluster.genes[missing.name][!missing])
}
recode<-cbind(cluster.genes[missing.name][!missing],gene.aliases[posns[!missing], "Approved.Symbol"])
colnames(recode)<-c("old","new")
###### transfer to new gene lists
posns<-match(clusters[,clusters.wanted[ic]],recode[,"old"])
missing<-is.na(posns)
clusters[!missing,clusters.wanted[ic]]<-recode[posns[!missing],"new"] ### redefine the clusters
}
}
#########################################################
snpinfo<-snpinfo.raw
ic<-1
for(ic in 1:length(clusters.wanted)){
cluster.genes<-clusters[,clusters.wanted[ic]]
cluster.genes<-cluster.genes[cluster.genes!="" | is.na(cluster.genes)]
print(paste(clusters.wanted[ic],paste(cluster.genes,collapse=","),sep=": "))
extra<-snpinfo.raw[snpinfo.raw[,"gene"] %in% cluster.genes,]
print(dim(extra))
extra[,"cluster"]<-clusters.wanted[ic]
snpinfo<-rbind(snpinfo, extra)
}
snpinfo.ori<-snpinfo
## HAVE
# snpinfo.raw (original from a.indel)
# snpinfo # with extra clusters
# snpinfo.raw a permanent copy of snpinfo
## "FANCM " "MHF1" "MHF2" "FAAP24"
clusters[,1]
chk<-apply(clusters,2,function(x){ length(x[x!=""])})
write.table(clusters,file="clusters_as.using.txt",col.names=TRUE,row.names=FALSE,sep="\t",quote=FALSE)
############################
a.indel[1:20,1:5]
the.samples<-colnames(a.indel)[grepl(".GT$", colnames(a.indel))]
filtered.genotype(a.indel[1:20,],gsub(".GT$","",the.samples),prefix="",suffix="",20,0.02,0.98,0.20,0.80,7,2)
num.cores<-4
num.bits<-num.cores
##registerDoMC(cores=num.cores)
registeredCores <- makeCluster(num.cores)
registerDoParallel(registeredCores)
colnames(a.indel)[grepl(".GQ$", colnames(a.indel))]
while((dim(a.indel)[1] %% num.bits)< 2) {
num.bits<-num.bits+1
} ### go don't get matrix issues
# modify if cant use multicore
#fil.genotypes<-foreach(a.indel.bit=iter(a.indel,by='row',chunksize=as.integer(dim(a.indel)[1]/num.bits) ), .combine='rbind', .multicombine=TRUE, .inorder=TRUE) %dopar% filtered.genotype(a.indel.bit,gsub(".GT$","",the.samples),prefix="",suffix="",20,0.02,0.98,0.20,0.80,7,2)
fil.genotypes <- filtered.genotype(a.indel,gsub(".GT$","",the.samples),prefix="",suffix="",20,0.02,0.98,0.20,0.80,7,2)
# 20,0.02,0.98,0.20,0.8,10,5 # for cancers where het may be way out
## length(col)
## fil.genotypes[col,1:5]
## fil.genotypes<-fil.genotypes[!col,]
# 0.02< het(0/1)<0.98
# ref(0/0)< 0.2
# (1/1) > 0.8
dim(fil.genotypes)
colnames(fil.genotypes)[1:5]
rownames(fil.genotypes)[1:5]
dim(fil.genotypes)
dim(a.indel)
tail(rownames(a.indel))
## a.indel<-a.indel[1:342791,]
## fil.genotypes<-fil.genotypes[1:342791,]
############################### do one phenotype ####################
#
ipheno<-1
# for(ipheno in 1:length(pheno.types)){
print(paste("Doing phenotype:",pheno.types[ipheno]))
target.pheno<-names(pheno.types)[ipheno]
target.pheno.col<-pheno.types[ipheno]
length(sample.sheet.full[,target.pheno.col])
pheno<-sample.sheet.full[!is.na(sample.sheet.full[,target.pheno.col]) ,] ## pheno only contains SAMPLES that have a phenotype
print(dim(pheno))
print(paste("Number Samples:",dim(pheno)[1]))
covars<-c("PCA1","PCA2","PCA3","PCA4")
covars<-c("the.run")
covars<-c("1")
dim(pheno)
pheno[1:5,]
# what send to skatmeta
formula<-paste(target.pheno.col,"~",paste(covars,collapse="+"),sep="")
print(formula)
formula<-formula(formula)
#seq.type[1:57,]
posns<-match(pheno[,"SAMPLE"],seq.type[,"Sample"])
missing<-is.na(posns)
sum(missing)
capture<-seq.type[posns,"Capture.Method"]
## the.runs<-seq.type[posns,"Run"]
## the.run<-the.runs
## the.run[the.runs=="C1F6GACXX"]<-0
## the.run[the.runs!="C1F6GACXX"]<-1
## the.run<-as.numeric(the.run)
## names(the.runs)<-the.runs
## unique.runs<-unique(the.runs)
## unique.runs.fill<-c(1:length(unique.runs))
## names(unique.runs.fill)<-unique.runs
## the.run<-unique.runs.fill[the.runs]
## the.run<-as.numeric(the.run)
table(capture) ### all illume here
pheno<-cbind(pheno,capture)
pheno[1:5,]
pheno<-pheno[pheno[,"SAMPLE"] %in% all.possible.samples,]
the.samples<-paste(pheno[,"SAMPLE"],"GT",sep=".") ## samples same order as in pheno
print(paste("Number samples: ",length(the.samples),sep=""))
table(pheno[,"SampleProject"])
## ###############GEFOS SNP type restrictions
######################## make sure pheno has sample samples as A.indel
##### AFFSTAT_IN_WORDS is mostly correct to the regression #####
pheno[1:5,]
summary.geno.extra<-{}
target<-"AML"
use.samples<-the.samples[pheno[,"SampleProject"]=="AML"]
length(use.samples)
genotypes<-a.indel[,use.samples]
dim(genotypes)
summary.geno<-genotype.summary(as.matrix(genotypes))
colnames(summary.geno)<-paste(c("MAF","ALT.Alleles","REF.Alleles","TOTAL.Alleles","MISSING.Alleles","ALT_HOMO","ALT_HETRO","GENO"),target,sep=".")
#summary.geno[1:5,]
if(is.null(dim(summary.geno.extra))) {
summary.geno.extra<-summary.geno
} else {
summary.geno.extra<-cbind(summary.geno.extra,summary.geno)
}
summary.geno.extra[1:5,]
target<-"Control"
use.samples<-the.samples[pheno[,"SampleProject"]=="Control"]
length(use.samples)
genotypes<-a.indel[,use.samples]
dim(genotypes)
summary.geno<-genotype.summary(as.matrix(genotypes))
colnames(summary.geno)<-paste(c("MAF","ALT.Alleles","REF.Alleles","TOTAL.Alleles","MISSING.Alleles","ALT_HOMO","ALT_HETRO","GENO"),target,sep=".")
#summary.geno[1:5,]
if(is.null(dim(summary.geno.extra))) {
summary.geno.extra<-summary.geno
} else {
summary.geno.extra<-cbind(summary.geno.extra,summary.geno)
}
target<-"AML.filt"
use.samples<-the.samples[pheno[,"SampleProject"]=="AML"]
length(use.samples)
genotypes<-fil.genotypes[,use.samples]
dim(genotypes)
summary.geno<-genotype.summary(as.matrix(genotypes))
colnames(summary.geno)<-paste(c("MAF","ALT.Alleles","REF.Alleles","TOTAL.Alleles","MISSING.Alleles","ALT_HOMO","ALT_HETRO","GENO"),target,sep=".")
#summary.geno[1:5,]
if(is.null(dim(summary.geno.extra))) {
summary.geno.extra<-summary.geno
} else {
summary.geno.extra<-cbind(summary.geno.extra,summary.geno)
}
target<-"Control.filt"
use.samples<-the.samples[pheno[,"SampleProject"]=="Control"]
length(use.samples)
genotypes<-fil.genotypes[,use.samples]
dim(genotypes)
summary.geno<-genotype.summary(as.matrix(genotypes))
colnames(summary.geno)<-paste(c("MAF","ALT.Alleles","REF.Alleles","TOTAL.Alleles","MISSING.Alleles","ALT_HOMO","ALT_HETRO","GENO"),target,sep=".")
#summary.geno[1:5,]
if(is.null(dim(summary.geno.extra))) {
summary.geno.extra<-summary.geno
} else {
summary.geno.extra<-cbind(summary.geno.extra,summary.geno)
}
a.indel[1:5,1:10]
summary.geno.extra[1:5,]
#colnames(summary.geno.extra)
#rownames(summary.geno.extra)<-key
#getHWE(obs_hets, obs_hom1, obs_hom2)
hw.p.control<-getHWE(summary.geno.extra[,"GENO.Control"]) ## used 16 CPUs
hw.p.control.filt<-getHWE(summary.geno.extra[,"GENO.Control.filt"]) ## used 16 CPUs
## class(summary.geno.extra)
hw.p.control[1:5]
length(hw.p.control)
names(hw.p.control)<-key
names(hw.p.control.filt)<-key
######### testing
## hw.p.control[200100:200195]
## hw.p.control[22:25]
## summary.geno.extra[22:25,"GENO.Control"]
## test.loc<-c(24,200129:200143)
## geno<-strsplit(summary.geno.extra[test.loc,"GENO.Control"],split=",")
## AA<-as.numeric(unlist(lapply(geno,function(x) x[1])))
## BB<-as.numeric(unlist(lapply(geno,function(x) x[3])))
## AB<-as.numeric(unlist(lapply(geno,function(x) x[2])))
## x<-cbind(AA,AB,BB)
## x
## HWExactMat(x)
## hw.p.control[test.loc] ## correct in original order
######################## inbreeding and no genotypes
group.maf.thresh<-0.20 # group.maf.thresh<-0.10 if more common that this in Group then discard: Discard Rare but common in this cohort ## not a point level
missing.threshold<-0.20 # missing.threshold<-0.50 60 % of genotypes missing
## missing.threshold.nimblgen<-0.20
## missing.threshold.illumina<-0.20
## rare.in.group<-( summary.geno.extra[,c("MAF.HIGH","MAF.LOW")]< group.maf.thresh) | ( summary.geno.extra[,c("MAF.HIGH","MAF.LOW")] > (1-group.maf.thresh)) | (is.na( summary.geno.extra[,c("MAF.HIGH","MAF.LOW")]))
rare.in.group.table<-( summary.geno.extra[,c("MAF.AML","MAF.Control","MAF.AML.filt","MAF.Control.filt")]< group.maf.thresh) | ( summary.geno.extra[,c("MAF.AML","MAF.Control","MAF.AML.filt","MAF.Control.filt")] > (1-group.maf.thresh)) | (is.na( summary.geno.extra[,c("MAF.AML","MAF.Control","MAF.AML.filt","MAF.Control.filt")]))
rare.in.group.table[1:5,]
#rare.in.group<-cbind( indels[,c("MAF.HIGH","MAF.LOW")]< group.maf.thresh) , (indels[,c("MAF.HIGH","MAF.LOW")] > (1-group.maf.thresh)) , (is.na(indels[,c("MAF.HIGH","MAF.LOW")])))
rare.in.group<-combine.boolean(rare.in.group.table,c("MAF.AML","MAF.Control"),"OR")
rare.in.group.filt<-combine.boolean(rare.in.group.table,c("MAF.AML.filt","MAF.Control.filt"),"OR")
sum(!rare.in.group)
sum(!rare.in.group.filt)
#no.genotypes<-(indels[,c("MAF.HIGH","MAF.LOW")]== 0) | (is.na(indels[,c("MAF.HIGH","MAF.LOW")])) # no genotypes in test classes for a mutataion after individaul quality filtering
### checking if BOTH not mono mrphic
no.genotypes.table<-(summary.geno.extra[,c("MAF.AML","MAF.Control","MAF.AML.filt","MAF.Control.filt")]== 0) | summary.geno.extra[,c("MAF.AML","MAF.Control","MAF.AML.filt","MAF.Control.filt")]=="NaN" | (is.na( summary.geno.extra[,c("MAF.AML","MAF.Control","MAF.AML.filt","MAF.Control.filt")])) # no genotypes in test classes for a mutataion after individaul quality filtering
no.genotypes.table[1:5,]
no.genotypes<-combine.boolean(no.genotypes.table,c("MAF.AML","MAF.Control"),"AND") # was AND
no.genotypes.filt<-combine.boolean(no.genotypes.table,c("MAF.AML.filt","MAF.Control.filt"),"AND") # was AND
sum(no.genotypes)
sum(no.genotypes.filt)
summary.geno.extra[1:5,]
missing.targets<-c("AML","Control","AML.filt","Control.filt")
high.missing<-{}
imt<-1
for(imt in 1:length(missing.targets)) {
the.missing.alleles<-paste("MISSING.Alleles",missing.targets[imt],sep=".")
the.total.alleles<-paste("TOTAL.Alleles",missing.targets[imt],sep=".")
a.missing.test<-as.numeric(summary.geno.extra[,the.missing.alleles])/(as.numeric(summary.geno.extra[,the.total.alleles])+as.numeric(summary.geno.extra[,the.missing.alleles]))
if(is.null(length(high.missing)) | length(high.missing)==0){
high.missing<-a.missing.test
} else {
high.missing<-cbind(high.missing,a.missing.test)
}
}
colnames(high.missing)<-missing.targets
rownames(high.missing)<-key
## nimblegen.total.missing<-subset(high.missing,select=c("nimblegen"))
## nimblegen.total.missing[1:5,]
## nimblegen.total.missing<-nimblegen.total.missing > missing.threshold.nimblgen
## ## nimblegen.total.missing<-combine.boolean(high.total.missing,c("LOW","HIGH","LOW.pheno","HIGH.pheno"),"OR")
## sum(nimblegen.total.missing)
## illumina.total.missing<-subset(high.missing,select=c("illumina"))
## illumina.total.missing[1:5,]
## illumina.total.missing<-illumina.total.missing > missing.threshold.illumina
## ## nimblegen.total.missing<-combine.boolean(high.total.missing,c("LOW","HIGH","LOW.pheno","HIGH.pheno"),"OR")
## sum(illumina.total.missing)
## sum(high.total.missing | nimblegen.total.missing | illumina.total.missing)
high.missing[1:5,]
#high.missing[places,]
#ok.missing.test[places,]
ok.missing.test<-high.missing <= missing.threshold
ok.missing.test[1:5,]
ok.missing<-combine.boolean(ok.missing.test,c("AML","Control"),"AND") # was AND
ok.missing.filt<-combine.boolean(ok.missing.test,c("AML.filt","Control.filt"),"AND") # was AND
sum(!ok.missing)
sum(!ok.missing.filt)
## ok.missing[places]
## diff<-ok.missing & !ok.missing.filt
## ####testing
## sum(diff)
## grep(TRUE,diff)[1:10]
## target<-"chr10:93396:93396:C:T:snp"
## target<-key[diff][83:84] # target<-key[test]
## high.missing[target,]
## chk<-gsub(".GT$",".AD",the.samples[1:96])
## chk.GT<-the.samples[1:96]
## a.indel[target,chk]
## a.indel[target,chk.GT]
## fil.genotypes[target,chk.GT]
## summary.geno.extra[target,paste("GENO",c("AML","Control","AML.filt","Control.filt"),sep=".")]
## hw.p.control.filt[target]
#test<-pass & !rare.in.group
## > summary.geno.extra[target,paste("GENO",c("AML","Control","AML.filt","Control.filt"),sep=".")]
## GENO.AML GENO.Control GENO.AML.filt GENO.Control.filt
## chr11:6579106:6579106:C:A:snp:6579106 "9,52,35" "27,97,76" "9,48,13" "26,87,50"
## chr11:7022531:7022531:A:T:snp:7022531 "49,34,13" "74,107,19" "49,34,0" "74,97,2"
## chr12:11183451:11183451:A:T:snp:11183451 "53,39,4" "120,67,13" "53,31,0" "120,63,0"
## chr16:84808824:84808824:C:G:snp:84808824 "37,45,14" "70,97,33" "36,44,3" "70,96,4"
## chr17:21216964:21216964:T:C:snp "24,72,0" "54,143,0" "24,64,0" "39,59,0"
## chr17:72927123:72927123:G:T:snp:72927123 "42,39,15" "85,95,18" "42,39,2" "83,81,11"
## chr19:13135824:13135824:G:T:snp "0,41,55" "5,81,112" "0,41,48" "0,30,99"
## chr19:55285045:55285045:G:T:snp "2,44,50" "5,75,118" "2,41,46" "1,53,83"
## chr19:55286796:55286796:G:A:snp "8,50,38" "5,88,105" "8,50,38" "4,86,103"
## chr19:55286854:55286854:A:G:snp "5,53,38" "9,86,103" "4,52,37" "8,75,95"
## chr1:13183439:13183439:T:C:snp "52,44,0" "96,103,0" "52,44,0" "96,103,0"
## chr1:18808526:18808526:A:C:snp:18808526 "38,40,18" "69,87,36" "37,40,2" "54,60,20"
## chr1:248457979:248457979:C:A:snp:248457979 "53,39,4" "93,92,14" "53,39,0" "93,91,0"
## chr21:15013735:15013735:A:G:snp "39,39,18" "54,104,42" "39,39,18" "54,104,41"
## chr21:43541342:43541342:C:T:snp:43541342 "38,39,19" "67,106,27" "38,39,0" "67,105,4"
## chr4:187538942:187538942:T:G:snp:187538942 "27,54,15" "73,94,33" "27,52,0" "73,87,2"
## chr6:31237833:31237833:T:C:snp:31237833 "58,28,10" "97,76,27" "54,28,3" "86,60,14"
## chr6:31238259:31238259:G:T:snp:31238259 "56,32,8" "97,79,24" "52,30,2" "80,57,22"
## chr7:87160618:87160618:A:C:snp:87160618 "28,44,24" "61,87,52" "28,44,2" "61,81,26"
################## get loci in common
## load("/media/Bioinform-D/Research/AML sequencing/Human_Exome_Targets_illumina_v2_hg19_targets.RData")
## ill.gr<-data.gr
## load("/media/Bioinform-D/Research/AML sequencing/Human_Exome_Targets_Nimble_v2_hg19_targets.RData")
## nim.gr<-data.gr
## genome(ill.gr)<-"hg19"
## genome(nim.gr)<-"hg19"
## ill.gr<-ill.gr+200
## nim.gr<-nim.gr+200
## #overlaps<-countOverlaps(ill.gr,nim.gr)
## overlaps<-overlapsAny(ill.gr,nim.gr)
## possible.loci<-ill.gr[overlaps]
## my.loci<-IRanges(start=as.numeric(a.indel[,"start"]),end=as.numeric(a.indel[,"end"]))
## on.chr<-as.vector(seqnames(possible.loci.ori))==a.indel[1,"chr"] # paste("chr",a.indel[1,"chr"],sep="")
## possible.space<-IRanges(start=as.numeric(start(possible.loci.ori))[on.chr],end=as.numeric(end(possible.loci.ori))[on.chr])
## common.loci<-overlapsAny(my.loci,possible.space)
## sum(common.loci)
## overlaps<-findOverlaps(possible.space,my.loci)
## snpinfo<-cbind(snp.names[subjectHits(overlaps)],gene.on.chr[queryHits(overlaps),"GENE_NAME"])
## colnames(snpinfo)<-c("Name","gene")
###############################################################################################################################################
################# HOWEVER ALT ALLELES ALL IS USED AS MINOR ALLELE FREQUNCY not alternative allele frequencies
maf.lt.all<-a.indel[,colnames(a.indel)[grepl("MAF.lt:",colnames(a.indel))]]
maf.lt.all[1:5,]
#maf.lt.all<-as.logical(maf.lt.all)
as.logical(maf.lt.all[1:5,5])
####################################################################################
##################################### make the POSITION filter matrix QUALITY.FILTER FUNATIOAL group-MAF done here
global.labs[!(global.labs %in% c(colnames(a.indel),colnames(summary.geno.extra)) )]
if(sum( !(global.labs %in% c(colnames(a.indel),colnames(summary.geno.extra))) )>0) {
print(paste("WARNING postion filters missing for data",
toString(global.labs[!(global.labs %in% c(colnames(a.indel))) ])))
}
#grep("ljb_gerp",colnames(a.indel))
##############$$$$$$$$$$$$$$ POSITION FILTER: combines a.indel, filter table and filter.table.pholy in this case.
qual<-position.quality.filter( cbind( a.indel[,colnames(a.indel) %in% global.labs],summary.geno.extra[,colnames(summary.geno.extra) %in% global.labs] ), global.quality.cut,global.quality.type,global.quality.dirn)
dim(qual)
dim(a.indel)
rownames(qual)<-key
qual[1:5,]