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GRIN2.0.ALEX.library.09.29.2022.R
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#################################################
# GRIN2.0-ALEX library
#######################################################
# get.chrom.length: This function will retrieve chromosome size data from chr.info
# txt file available on the UCSC genome browser based on the specified genome assembly.
get.chrom.length=function(genome.assembly) # one of four genome assemblies that include "Human_GRCh38", "Human_GRCh37", "Mouse_HGCm39" and "Mouse_HGCm38" can be specified
{
# retrieve chromosome size data for GRCh38 (hg38) genome build
if (genome.assembly=="Human_GRCh38")
{
chr.size.hg38= circlize::read.chromInfo(species = "hg38")
chr.size.hg38=as.data.frame(chr.size.hg38)
chr.size=cbind.data.frame(chrom=chr.size.hg38$chromosome,
size=chr.size.hg38$chr.len)
chr.size$chrom<-gsub("chr","",as.character(chr.size$chrom))
return(chr.size)
}
# retrieve chromosome size data for GRCh37 (hg19) genome build
if (genome.assembly=="Human_GRCh37")
{
chr.size.hg19= circlize::read.chromInfo(species = "hg19")
chr.size.hg19=as.data.frame(chr.size.hg19)
chr.size=cbind.data.frame(chrom=chr.size.hg19$chromosome,
size=chr.size.hg19$chr.len)
chr.size$chrom<-gsub("chr","",as.character(chr.size$chrom))
return(chr.size)
}
# retrieve chromosome size data for Mouse_HGCm39 (mm39) genome build
if (genome.assembly=="Mouse_HGCm39")
{
chr.size.mm39= circlize::read.chromInfo(species = "mm39")
chr.size.mm39=as.data.frame(chr.size.mm39)
chr.size=cbind.data.frame(chrom=chr.size.mm39$chromosome,
size=chr.size.mm39$chr.len)
chr.size$chrom<-gsub("chr","",as.character(chr.size$chrom))
return(chr.size)
}
# retrieve chromosome size data for Mouse_HGCm38 (mm10) genome build
if (genome.assembly=="Mouse_HGCm38")
{
chr.size.mm38= circlize::read.chromInfo(species = "mm10")
chr.size.mm38=as.data.frame(chr.size.mm38)
chr.size=cbind.data.frame(chrom=chr.size.mm38$chromosome,
size=chr.size.mm38$chr.len)
chr.size$chrom<-gsub("chr","",as.character(chr.size$chrom))
return(chr.size)
}
}
################################################################################
# get.ensembl.annotation: This function can be used to directly retrieve gene and
# regulatory features annotation data from ensembl biomaRt database based on the
# specified genome assembly
get.ensembl.annotation=function(genome.assembly) # one of four genome assemblies that include "Human_GRCh38", "Human_GRCh37", "Mouse_HGCm39" and "Mouse_HGCm38" can be specified
{
# retrieve gene annotation data for human_GRCh38 (hg38) genome assembly from ensembl biomaRt version 104
if (genome.assembly=="Human_GRCh38")
{
ensembl_GRCh38 = useEnsembl(biomart="genes",
dataset="hsapiens_gene_ensembl", # specify dataset for homosapiens
version = "104") # specifying version is critical to get stable search results. If we did not specify version, query will extract data from the most updated version
chromosomes = c(1:22, "X", "Y") # specify chromosomes of interst
hg38_gene_annotation <- getBM(attributes=c('ensembl_gene_id', 'chromosome_name',
'start_position','end_position',
'description','external_gene_name',
'gene_biotype', 'strand', 'band'),
filters = 'chromosome_name', values = chromosomes,
mart = ensembl_GRCh38) # attributes specify data to be retreived from biomaRt database
gene=hg38_gene_annotation[,1]
chrom=hg38_gene_annotation[,2]
loc.start=hg38_gene_annotation[,3]
loc.end=hg38_gene_annotation[,4]
description=hg38_gene_annotation[,5]
gene.name=hg38_gene_annotation[,6]
biotype=hg38_gene_annotation[,7]
chrom.strand=hg38_gene_annotation[,8]
chrom.band=hg38_gene_annotation[,9]
gene.data=cbind.data.frame(gene=gene, # we should change "gene" in the package functions to "Ensembl_ID"
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description,
gene.name=gene.name,
biotype=biotype,
chrom.strand=chrom.strand,
chrom.band=chrom.band)
# To retrieve data for regulatory features mapped to GRCh38
regulatory.hg38 = useEnsembl(biomart="regulation",
dataset="hsapiens_regulatory_feature", # regulatory features includes (promoters, promoter flanking regions, enhancers, CTCF binding sites, TF binding sites and open chromatin regions)
version = '104') # specify version for stable results
regulatory.features=c("Promoter", "Promoter Flanking Region", "Enhancer",
"CTCF Binding Site",
"TF binding site", "Open chromatin")
hg38.regulatory= getBM(attributes=c("regulatory_stable_id", "chromosome_name",
"chromosome_start","chromosome_end",
"feature_type_description"),
filters =c("regulatory_feature_type_name", "chromosome_name")
, values =list(regulatory.features,chromosomes) , # We are excluding regulatory features not mapped to chr1:22, X, Y
mart =regulatory.hg38)
gene=hg38.regulatory[,1]
chrom=hg38.regulatory[,2]
loc.start=hg38.regulatory[,3]
loc.end=hg38.regulatory[,4]
description=hg38.regulatory[,5]
regulation.data=cbind.data.frame(gene=gene,
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description)
res=list(gene.annotation=gene.data, # gene.data represent annotated genes
reg.annotation=regulation.data) # regulation.data represent predicted regulatory features
return(res)
}
# retrieve gene annotation data for human_GRCh37 (hg19) genome assembly from ensembl biomaRt version 75
if (genome.assembly=="Human_GRCh37")
{
ensemblGRCh37 <- useEnsembl(biomart = 'ensembl',
dataset = 'hsapiens_gene_ensembl',
version = '75')
chromosomes = c(1:22, "X", "Y")
hg19_gene_annotation <- getBM(attributes=c('ensembl_gene_id', 'chromosome_name',
'start_position','end_position',
'description','external_gene_id',
'gene_biotype', 'strand', 'band'),
filters = 'chromosome_name', values = chromosomes,
mart = ensemblGRCh37)
gene=hg19_gene_annotation[,1]
chrom=hg19_gene_annotation[,2]
loc.start=hg19_gene_annotation[,3]
loc.end=hg19_gene_annotation[,4]
description=hg19_gene_annotation[,5]
gene.name=hg19_gene_annotation[,6]
biotype=hg19_gene_annotation[,7]
chrom.strand=hg19_gene_annotation[,8]
chrom.band=hg19_gene_annotation[,9]
gene.data=cbind.data.frame(gene=gene,
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description,
gene.name=gene.name,
biotype=biotype,
chrom.strand=chrom.strand,
chrom.band=chrom.band)
# To retrieve data for regulatory features mapped to GRCh37
regulatory.hg19 = useEnsembl(biomart="regulation",
dataset="hsapiens_regulatory_feature",
version = 'GRCh37')
regulatory.features=c("Promoter", "Promoter Flanking Region", "Enhancer",
"CTCF Binding Site",
"TF binding site", "Open chromatin")
hg19.regulatory= getBM(attributes=c("regulatory_stable_id", "chromosome_name",
"chromosome_start","chromosome_end",
"feature_type_description"),
filters =c("regulatory_feature_type_name", "chromosome_name")
, values =list(regulatory.features,chromosomes) , # We are excluding regulatory features not mapped to chr1:22, X, Y
mart =regulatory.hg19)
gene=hg19.regulatory[,1]
chrom=hg19.regulatory[,2]
loc.start=hg19.regulatory[,3]
loc.end=hg19.regulatory[,4]
description=hg19.regulatory[,5]
regulation.data=cbind.data.frame(gene=gene,
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description)
res=list(gene.annotation=gene.data,
reg.annotation=regulation.data)
return(res)
}
# retrieve gene annotation data for Mouse_HGCm39 genome assembly from ensembl biomaRt version 104
if (genome.assembly=="Mouse_HGCm39")
{
ensembl.HGCm39 = useEnsembl(biomart="genes", dataset="mmusculus_gene_ensembl",
version = "104")
chromosomes = c(1:19, "X", "Y")
HGCm39.gene_annotation <- getBM(attributes=c('ensembl_gene_id', 'chromosome_name',
'start_position','end_position',
'description','external_gene_name',
'gene_biotype', 'strand', 'band'),
filters = 'chromosome_name', values = chromosomes,
mart = ensembl.HGCm39)
gene=HGCm39.gene_annotation[,1]
chrom=HGCm39.gene_annotation[,2]
loc.start=HGCm39.gene_annotation[,3]
loc.end=HGCm39.gene_annotation[,4]
description=HGCm39.gene_annotation[,5]
gene.name=HGCm39.gene_annotation[,6]
biotype=HGCm39.gene_annotation[,7]
chrom.strand=HGCm39.gene_annotation[,8]
chrom.band=HGCm39.gene_annotation[,9]
gene.data=cbind.data.frame(gene=gene,
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description,
gene.name=gene.name,
biotype=biotype,
chrom.strand=chrom.strand,
chrom.band=chrom.band)
# To retrieve data for mouse_regulatory features mapped to mm39
regulatory.mm39 = useEnsembl(biomart="regulation",
dataset="mmusculus_regulatory_feature",
version = '104')
regulatory.features=c("Promoter", "Promoter Flanking Region", "Enhancer",
"CTCF Binding Site",
"TF binding site", "Open chromatin")
mm39.regulatory= getBM(attributes=c("regulatory_stable_id", "chromosome_name",
"chromosome_start","chromosome_end",
"feature_type_description"),
filters =c("regulatory_feature_type_name", "chromosome_name")
, values =list(regulatory.features,chromosomes) , # We are excluding regulatory features not mapped to chr1:22, X, Y
mart =regulatory.mm39)
gene=mm39.regulatory[,1]
chrom=mm39.regulatory[,2]
loc.start=mm39.regulatory[,3]
loc.end=mm39.regulatory[,4]
description=mm39.regulatory[,5]
regulation.data=cbind.data.frame(gene=gene,
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description)
res=list(gene.annotation=gene.data,
reg.annotation=regulation.data)
return(res)
}
# retrieve gene annotation data for Mouse_HGCm38 from ensembl biomaRt version 102
if (genome.assembly=="Mouse_HGCm38")
{
ensemblGRCm38 <- useEnsembl(biomart = 'genes',
dataset = 'mmusculus_gene_ensembl',
version = '102')
chromosomes = c(1:19, "X", "Y")
Mouse_mm10.gene_annotation <- getBM(attributes=c('ensembl_gene_id', 'chromosome_name',
'start_position','end_position',
'description','external_gene_name',
'gene_biotype', 'strand', 'band'),
filters = 'chromosome_name', values = chromosomes,
mart = ensemblGRCm38)
gene=Mouse_mm10.gene_annotation[,1]
chrom=Mouse_mm10.gene_annotation[,2]
loc.start=Mouse_mm10.gene_annotation[,3]
loc.end=Mouse_mm10.gene_annotation[,4]
description=Mouse_mm10.gene_annotation[,5]
gene.name=Mouse_mm10.gene_annotation[,6]
biotype=Mouse_mm10.gene_annotation[,7]
chrom.strand=Mouse_mm10.gene_annotation[,8]
chrom.band=Mouse_mm10.gene_annotation[,9]
gene.data=cbind.data.frame(gene=gene,
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description,
gene.name=gene.name,
biotype=biotype,
chrom.strand=chrom.strand,
chrom.band=chrom.band)
# To retrieve data for mouse_regulatory features mapped to mm10
regulatory.mm10 = useEnsembl(biomart="regulation",
dataset="mmusculus_regulatory_feature",
version = '102')
regulatory.features=c("Promoter", "Promoter Flanking Region", "Enhancer",
"CTCF Binding Site",
"TF binding site", "Open chromatin")
mm10.regulatory= getBM(attributes=c("regulatory_stable_id", "chromosome_name",
"chromosome_start","chromosome_end",
"feature_type_description"),
filters =c("regulatory_feature_type_name", "chromosome_name")
, values =list(regulatory.features,chromosomes) , # We are excluding regulatory features not mapped to chr1:22, X, Y
mart =regulatory.mm10)
gene=mm10.regulatory[,1]
chrom=mm10.regulatory[,2]
loc.start=mm10.regulatory[,3]
loc.end=mm10.regulatory[,4]
description=mm10.regulatory[,5]
regulation.data=cbind.data.frame(gene=gene,
chrom=chrom,
loc.start=loc.start,
loc.end=loc.end,
description=description)
res=list(gene.annotation=gene.data,
reg.annotation=regulation.data)
return(res)
}
}
#################################################################################
# order.index.gene.data: This function order and index gene data by chromosome,
# start location, and end location.
order.index.gene.data=function(gene.data)
{
g=nrow(gene.data)
gene.ord=order(gene.data[,"chrom"],
gene.data[,"loc.start"],
gene.data[,"loc.end"])
gene.data=gene.data[gene.ord,]
new.chrom=which(gene.data[-1,"chrom"]!=gene.data[-g,"chrom"])
chr.start=c(1,new.chrom+1)
chr.end=c(new.chrom,g)
gene.index=cbind.data.frame(chrom=gene.data[chr.start,"chrom"],
row.start=chr.start,
row.end=chr.end)
gene.data$gene.row=1:g
res=list(gene.data=gene.data,
gene.index=gene.index)
return(res)
}
#################################################################################
# order.index.lsn.data: This function order and index lesion data by type,
# chromosome, and subject.
order.index.lsn.data=function(lsn.data)
{
l=nrow(lsn.data)
lsn.ord=order(lsn.data[,"lsn.type"],
lsn.data[,"chrom"],
lsn.data[,"ID"])
lsn.data=lsn.data[lsn.ord,]
lsn.chng=which((lsn.data[-1,"lsn.type"]!=lsn.data[-l,"lsn.type"])|
(lsn.data[-1,"chrom"]!=lsn.data[-l,"chrom"])|
(lsn.data[-1,"ID"]!=lsn.data[-l,"ID"]))
lsn.start=c(1,lsn.chng+1)
lsn.end=c(lsn.chng,l)
lsn.index=cbind.data.frame(lsn.type=lsn.data[lsn.start,"lsn.type"],
chrom=lsn.data[lsn.start,"chrom"],
ID=lsn.data[lsn.start,"ID"],
row.start=lsn.start,
row.end=lsn.end)
lsn.data$lsn.row=1:l
res=list(lsn.data=lsn.data,
lsn.index=lsn.index)
return(res)
}
##################################################################################
# prep.gene.lsn.data:This function prepare gene and lesion data for later computations.
prep.gene.lsn.data=function(lsn.data, # lesion data: ID, chrom, loc.start, loc.end, lsn.type
gene.data, # gene locus data: gene, chrom, loc.start, loc.end
mess.freq=10) # message frequency: display message every mess.freq^{th} lesion block
{
saf=options()$stringsAsFactors
options(stringsAsFactors=F)
# order lesion data by type, chromosome, and subject
lsn.dset=order.index.lsn.data(lsn.data)
lsn.data=lsn.dset$lsn.data
lsn.index=lsn.dset$lsn.index
# order and index gene locus data by chromosome and position
gene.dset=order.index.gene.data(gene.data)
gene.data=gene.dset$gene.data
gene.index=gene.dset$gene.index
# Extract some basic information
g=nrow(gene.data) # number of genes
l=nrow(lsn.data) # number of lesions
# Create gene position data
message(paste0("Formatting gene position data for counting: ",date()))
gene.pos.data=rbind.data.frame(cbind.data.frame(ID="", # gene start data
lsn.type="",
lsn.row=NA,
gene=gene.data[,"gene"],
gene.row=gene.data[,"gene.row"],
chrom=gene.data[,"chrom"],
pos=gene.data[,"loc.start"],
cty=1),
cbind.data.frame(ID="", # gene end data
lsn.type="",
lsn.row=NA,
gene=gene.data[,"gene"],
gene.row=gene.data[,"gene.row"],
chrom=gene.data[,"chrom"],
pos=gene.data[,"loc.end"],
cty=4)
)
# order gene position data
ord=order(gene.pos.data[,"chrom"],
gene.pos.data[,"pos"],
gene.pos.data[,"cty"])
gene.pos.data=gene.pos.data[ord,]
# Create lesion position data with one row for each edge of each lesion
message(paste0("Formatting lesion position data for counting: ",date()))
lsn.pos.data=rbind.data.frame(cbind.data.frame(ID=lsn.data[,"ID"],
lsn.type=lsn.data[,"lsn.type"],
lsn.row=lsn.data[,"lsn.row"],
gene="",
gene.row=NA,
chrom=lsn.data[,"chrom"],
pos=lsn.data[,"loc.start"],
cty=2),
cbind.data.frame(ID=lsn.data[,"ID"],
lsn.type=lsn.data[,"lsn.type"],
lsn.row=lsn.data[,"lsn.row"],
gene="",
gene.row=NA,
chrom=lsn.data[,"chrom"],
pos=lsn.data[,"loc.end"],
cty=3))
# order lesion position data
ord=order(lsn.pos.data[,"chrom"],
lsn.pos.data[,"pos"],
lsn.pos.data[,"cty"])
lsn.pos.data=lsn.pos.data[ord,]
# Combine gene & lesion data
message(paste0("Combining formatted gene and lesion postion data: ",date()))
gene.lsn.data=rbind.data.frame(gene.pos.data,
lsn.pos.data)
# Order and index gene & lesion data
ord=order(gene.lsn.data[,"chrom"],
gene.lsn.data[,"pos"],
gene.lsn.data[,"cty"])
gene.lsn.data=gene.lsn.data[ord,]
m=nrow(gene.lsn.data)
gene.lsn.data[,"glp.row"]=1:m
# compute vector to order gene.lsn.data by lsn.row and gene.row
ord=order(gene.lsn.data[,"lsn.row"],
gene.lsn.data[,"gene.row"],
gene.lsn.data[,"cty"])
# use that vector to add gene.lsn.data row.start and row.end indices to lsn.data
lsn.pos=gene.lsn.data[ord[1:(2*l)],]
lsn.data[,"glp.row.start"]=lsn.pos[2*(1:l)-1,"glp.row"]
lsn.data[,"glp.row.end"]=lsn.pos[2*(1:l),"glp.row"]
# use that vector to add gene.lsn.data row.start and row.end indices to gene.data
gene.pos=gene.lsn.data[ord[-(1:(2*l))],]
gene.data[,"glp.row.start"]=gene.pos[2*(1:g)-1,"glp.row"]
gene.data[,"glp.row.end"]=gene.pos[2*(1:g),"glp.row"]
# Double-check table pointers from lsn.data and gene.data to gene.lsn.data
message(paste0("Verifying structure of combined gene and lesion data: ",date()))
glp.gene.start=gene.lsn.data[gene.data$glp.row.start,c("gene","chrom","pos")]
colnames(glp.gene.start)=c("gene","chrom","loc.start")
ok.glp.gene.start=all(glp.gene.start==gene.data[,c("gene","chrom","loc.start")])
glp.gene.end=gene.lsn.data[gene.data$glp.row.end,c("gene","chrom","pos")]
colnames(glp.gene.end)=c("gene","chrom","loc.end")
ok.glp.gene.end=all(glp.gene.end==gene.data[,c("gene","chrom","loc.end")])
glp.lsn.start=gene.lsn.data[lsn.data$glp.row.start,c("ID","chrom","pos","lsn.type")]
colnames(glp.lsn.start)=c("ID","chrom","loc.start","lsn.type")
ok.glp.lsn.start=all(glp.lsn.start==lsn.data[,c("ID","chrom","loc.start","lsn.type")])
glp.lsn.end=gene.lsn.data[lsn.data$glp.row.end,c("ID","chrom","pos","lsn.type")]
colnames(glp.lsn.end)=c("ID","chrom","loc.end","lsn.type")
ok.glp.lsn.end=all(glp.lsn.end==lsn.data[,c("ID","chrom","loc.end","lsn.type")])
# Double-check table pointers from gene.lsn.data to gene.data and lsn.data
glp.gene.start=gene.lsn.data[gene.lsn.data$cty==1,c("gene.row","gene","chrom","pos")]
ok.gene.start=all(glp.gene.start[,c("gene","chrom","pos")]==gene.data[glp.gene.start$gene.row,c("gene","chrom","loc.start")])
glp.gene.end=gene.lsn.data[gene.lsn.data$cty==4,c("gene.row","gene","chrom","pos")]
ok.gene.end=all(glp.gene.end[,c("gene","chrom","pos")]==gene.data[glp.gene.end$gene.row,c("gene","chrom","loc.end")])
glp.lsn.start=gene.lsn.data[gene.lsn.data$cty==2,c("lsn.row","ID","chrom","pos","lsn.type")]
ok.lsn.start=all(glp.lsn.start[,c("ID","chrom","pos","lsn.type")]==lsn.data[glp.lsn.start$lsn.row,c("ID","chrom","loc.start","lsn.type")])
glp.lsn.end=gene.lsn.data[gene.lsn.data$cty==3,c("lsn.row","ID","chrom","pos","lsn.type")]
ok.lsn.end=all(glp.lsn.end[,c("ID","chrom","pos","lsn.type")]==lsn.data[glp.lsn.end$lsn.row,c("ID","chrom","loc.end","lsn.type")])
all.ok=all(c(ok.glp.gene.start,ok.glp.gene.end,
ok.glp.lsn.start,ok.glp.lsn.end,
ok.gene.start,ok.gene.end,
ok.lsn.start,ok.lsn.end))
if (!all.ok)
stop("Error in constructing and indexing combined lesion and gene data.")
message(paste0("Verified correct construction and indexing of combined lesion and gene data: ",date()))
return(list(lsn.data=lsn.data, # Input lesion data
gene.data=gene.data, # Input gene annotation data
gene.lsn.data=gene.lsn.data, # Data.frame ordered by gene and lesions start position. Gene start position is coded as 1 in the cty column and gene end position is coded as 4. Lesion start position is coded as 2 in the cty column and lesion end position is coded as 3.
gene.index=gene.index, # Data.frame that shows ordered row start and row end for each chromosome in the gene.lsn.data table
lsn.index=lsn.index)) # Data.frame that shows row start and row end for each lesion in the gene.lsn.data table
}
#################################################################################
# find.gene.lsn.overlaps: Use the results of prep.gene.lsn.data function to find
# lesion-gene overlaps.
find.gene.lsn.overlaps=function(gl.data) # A list of five data.frames that represent the output results of the prep.gene.lsn.data function
{
gene.data=gl.data$gene.data
lsn.data=gl.data$lsn.data
lsn.index=gl.data$lsn.index
gene.index=gl.data$gene.index
gene.lsn.data=gl.data$gene.lsn.data
m=nrow(gene.lsn.data)
message(paste0("Scanning through combined lesion and gene data to find gene-lesion overlaps: ",date()))
gene.row.mtch=NULL # initialize vector for rows of gene data matched to rows of lesion data
lsn.row.mtch=NULL # initialize vector for rows of lesion data matched to rows of gene data
current.genes=NULL # initialize vector of genes overlapping this point of the scan
current.lsns=NULL # initialize vector of lesions overlapping this point of the scan
for (i in 1:m) # loop over rows of gene.lsn.data
{
# enter a gene
if (gene.lsn.data$cty[i]==1)
{
# add this gene to the set of current genes
current.genes=c(current.genes,
gene.lsn.data$gene.row[i])
# match this gene to set of current lesions
lsn.row.mtch=c(lsn.row.mtch,current.lsns) # add current lesions to lsn.row.mtch
gene.row.mtch=c(gene.row.mtch, # add this gene for each current lesion
rep(gene.lsn.data$gene.row[i],
length(current.lsns)))
}
# exit a gene
if (gene.lsn.data$cty[i]==4)
{
# drop this gene from the set of current genes
current.genes=setdiff(current.genes,
gene.lsn.data$gene.row[i])
}
if (gene.lsn.data$cty[i]==2) # enter a lesion
{
lsn.row.mtch=c(lsn.row.mtch,
rep(gene.lsn.data$lsn.row[i],length(current.genes)))
gene.row.mtch=c(gene.row.mtch,current.genes)
current.lsns=c(current.lsns,
gene.lsn.data$lsn.row[i])
}
if (gene.lsn.data$cty[i]==3)
{
current.lsns=setdiff(current.lsns,
gene.lsn.data$lsn.row[i])
}
}
message(paste0("Completed scan of combined gene-lesion data: ",date()))
# Generate the gene-lesion hit data
gene.lsn.hits=cbind.data.frame(gene.data[gene.row.mtch,
c("gene.row","gene","chrom","loc.start","loc.end")],
lsn.data[lsn.row.mtch,
c("lsn.row","ID","chrom","loc.start","loc.end","lsn.type")])
colnames(gene.lsn.hits)=c("gene.row","gene","gene.chrom","gene.loc.start","gene.loc.end",
"lsn.row","ID","lsn.chrom","lsn.loc.start","lsn.loc.end","lsn.type")
res=list(lsn.data=lsn.data, # Input lesion data
gene.data=gene.data, # Input gene annotation data
gene.lsn.data=gene.lsn.data, # Data.frame ordered by gene and lesions start position. Gene start position is coded as 1 in the cty column and gene end position is coded as 4. Lesion start position is coded as 2 in the cty column and lesion end position is coded as 3
gene.lsn.hits=gene.lsn.hits, # Each row represent a gene overlapped by a certain lesion. Gene column shows the overlapped gene and ID column has the patient ID
gene.index=gene.index, # Data.frame that shows row start and row end for each chromosome in the gene.lsn.data table
lsn.index=lsn.index) # Data.frame that shows row start and row end for each lesion in the gene.lsn.data table
return(res)
}
##################################################################################
# count.hits: The function computes the number of hits affecting each gene by lesion
# type. It also compute the number of subjects with a hit in each annotated gene.
count.hits=function(ov.data) # result of find.gene.lsn.overlaps function
{
lsn.data=ov.data$lsn.data
lsn.index=ov.data$lsn.index
gene.lsn.hits=ov.data$gene.lsn.hits
gene.lsn.data=ov.data$gene.lsn.data
gene.data=ov.data$gene.data
gene.index=ov.data$gene.index
g=nrow(gene.data)
# Compute the number of hits matrix
lsn.types=sort(unique(lsn.index[,"lsn.type"]))
k=length(lsn.types)
nhit.mtx=matrix(0,g,k)
colnames(nhit.mtx)=lsn.types
nhit.tbl=table(gene.lsn.hits$gene.row,
gene.lsn.hits$lsn.type)
nhit.rows=as.numeric(rownames(nhit.tbl))
for (i in 1:ncol(nhit.tbl))
nhit.mtx[nhit.rows,colnames(nhit.tbl)[i]]=nhit.tbl[,i]
# Compute the matrix of the number of subjects with a hit
gene.subj.type=paste0(gene.lsn.hits$gene.row,"_",
gene.lsn.hits$ID,"_",
gene.lsn.hits$lsn.type)
dup.gene.subj.type=duplicated(gene.subj.type)
subj.gene.hits=gene.lsn.hits[!dup.gene.subj.type,]
nsubj.mtx=matrix(0,g,k)
colnames(nsubj.mtx)=lsn.types
nsubj.tbl=table(subj.gene.hits$gene.row,
subj.gene.hits$lsn.type)
nsubj.rows=as.numeric(rownames(nsubj.tbl))
for (i in 1:ncol(nsubj.tbl))
nsubj.mtx[nsubj.rows,colnames(nsubj.tbl)[i]]=nsubj.tbl[,i]
res=list(lsn.data=lsn.data, # Input lesion data
lsn.index=lsn.index, # Data.frame that shows row start and row end for each lesion in the gene.lsn.data table
gene.data=gene.data, # Input gene annotation data
gene.index=gene.index, # Data.frame that shows ordered row start and row end for each chromosome in the gene.lsn.data table
nhit.mtx=nhit.mtx, # A data matrix with number of hits in each gene by lesion type
nsubj.mtx=nsubj.mtx, # A data matrix with number of affected subjects by lesion type
gene.lsn.data=gene.lsn.hits, # Each row represent a gene overlapped by a certain lesion. Column "gene" shows the overlapped gene and ID column has the patient ID
glp.data=gene.lsn.data) # Data.frame ordered by gene and lesions start position. Gene start position is coded as 1 in the cty column and gene end position is coded as 4. Lesion start position is coded as 2 in the cty column and lesion end position is coded as 3
return(res)
}
#################################################################################
# Following four functions row.prob.subj.hit, row.bern.conv, p.order and pc06.fdr
# will be used by prob.hits function to compute p-values for the probablity of each gene
# to be affected by one or a constellation of multiple types of lesions
# row.prob.subj.hit: Compute the probability that a subject has a hit for each gene.
row.prob.subj.hit=function(P, # matrix of lesion hit probabilities, rows for genes, columns for lesion types
IDs) # vector of subject IDs for each lesion, length must equal ncol(P)
{
if (length(IDs)!=ncol(P))
stop("length(IDs) must equal ncol(P).")
g=nrow(P)
l=ncol(P)
ord=order(IDs)
IDs=IDs[ord]
P=matrix(P[,ord],g,l)
l=length(IDs)
new.ID=which(IDs[-1]!=IDs[-l])
ID.start=c(1,new.ID+1)
ID.end=c(new.ID,l)
n=length(ID.start)
m=nrow(P)
Pr=matrix(NA,m,n)
for (i in 1:n)
{
pr.mtx=matrix(P[,ID.start[i]:ID.end[i]],m,ID.end[i]-ID.start[i]+1)
Pr[,i]=1-exp(rowSums(log(1-pr.mtx)))
}
return(Pr)
}
################################################################################
# row.bern.conv: This function Compute a convolution of Bernoullis for each row of
# a Bernoulli success probability matrix.
row.bern.conv=function(P, #matrix of lesion hit probabilities, rows for genes, columns for lesion types
max.x=NULL) # Maximum number of subjects or maximum number of hits
{
m=nrow(P)
n=ncol(P)
if (is.null(max.x))
max.x=(ncol(P))
Pr=matrix(0,m,max.x+1)
Pr[,1]=1
for (i in 1:n)
{
P1=Pr*P[,i]
P0=Pr*(1-P[,i])
Pr0=P0
Pr1=cbind(0,P1)
Pr1[,max.x+1]=Pr1[,max.x+1]+Pr1[,max.x+2]
Pr=Pr0+Pr1[,-(max.x+2)]
}
rs.Pr=rowSums(Pr)
Pr=Pr/rs.Pr
return(Pr)
}
#################################################################################
# p.order: This function compute ordered p-values and prepare the data for the
# constellation test which evaluates if the gene is affected by multiple types of lesions.
p.order=function(P) # matrix of lesion hit probabilities, rows for genes, columns for lesion types
{
k=ncol(P)
p.mtx=apply(P,1,sort,na.last=T)
p.mtx=t(p.mtx)
#n.pvals=rowSums(!is.na(p.mtx))
res=p.mtx
for (i in 1:k)
{
res[,i]=pbeta(p.mtx[,i],i,k-i+1)
}
return(res)
}
###############################################################################
# pc06.fdr: Compute FDR with the Pounds & Cheng (2006) estimator of
# the proportion of tests with a true null (pi.hat)
# Reference: https://pubmed.ncbi.nlm.nih.gov/16777905/
pc06.fdr=function(p)
{
pi.hat=min(1,2*mean(p,na.rm=T))
q=pi.hat*p.adjust(p,method="fdr")
return(q)
}
#################################################################################
# prob.hits: The function evaluates the probablity of a gene to be affected by one
# or a constellation of multiple types of lesions.
prob.hits=function(hit.cnt, # Output results of the count.hits function
chr.size=NULL) # A data table showing the size of the 22 autosomes, in addition to X and Y chromosomes in base pairs. It should has two columns named "chrom" with the chromosome number and "size" for the size of the chromosome in base pairs.
{
if (is.null(chr.size))
chr.size=impute.chrom.size(hit.cnt$lsn.data,
hit.cnt$gene.data)
###################
# order and index gene.lsn.data
ord=order(hit.cnt$gene.lsn.data$lsn.type,
hit.cnt$gene.lsn.data$lsn.chrom,
hit.cnt$gene.lsn.data$gene.row,
hit.cnt$gene.lsn.data$ID)
hit.cnt$gene.lsn.data=hit.cnt$gene.lsn.data[ord,]
m=nrow(hit.cnt$gene.lsn.data)
new.sect=which((hit.cnt$gene.lsn.data$gene.chrom[-1]!=hit.cnt$gene.lsn.data$gene.chrom[-m])|
(hit.cnt$gene.lsn.data$lsn.type[-1]!=hit.cnt$gene.lsn.data$lsn.type[-m])|
(hit.cnt$gene.lsn.data$gene.row[-1]!=hit.cnt$gene.lsn.data$gene.row[-m]))
sect.start=c(1,new.sect+1)
sect.end=c(new.sect,m)
gene.lsn.index=cbind.data.frame(lsn.type=hit.cnt$gene.lsn.data$lsn.type[sect.start],
chrom=hit.cnt$gene.lsn.data$gene.chrom[sect.start],
gene.row=hit.cnt$gene.lsn.data$gene.row[sect.start],
row.start=sect.start,
row.end=sect.end,
n.lsns=sect.end-sect.start+1)
k=nrow(gene.lsn.index)
new.chr=which(gene.lsn.index$chrom[-1]!=gene.lsn.index$chrom[-k])
chr.start=c(1,new.chr+1)
chr.end=c(new.chr,k)
gene.lsn.chr.index=cbind.data.frame(lsn.type=gene.lsn.index$lsn.type[chr.start],
chrom=gene.lsn.index$chrom[chr.start],
row.start=chr.start,
row.end=chr.end,
n.rows=chr.end-chr.start+1)
nr.li=nrow(hit.cnt$lsn.index)
new.chr=which((hit.cnt$lsn.index$lsn.type[-1]!=hit.cnt$lsn.index$lsn.type[-nr.li])|
(hit.cnt$lsn.index$chrom[-1]!=hit.cnt$lsn.index$chrom[-nr.li]))
chr.start=c(1,new.chr+1)
chr.end=c(new.chr,nr.li)
lsn.chr.index=cbind.data.frame(lsn.type=hit.cnt$lsn.index$lsn.type[chr.start],
chrom=hit.cnt$lsn.index$chrom[chr.start],
row.start=chr.start,
row.end=chr.end)
b=nrow(gene.lsn.chr.index)
g=nrow(hit.cnt$nhit.mtx)
nlt=ncol(hit.cnt$nhit.mtx)
p.nsubj=p.nhit=matrix(1,g,nlt)
colnames(p.nsubj)=colnames(p.nhit)=colnames(hit.cnt$nhit.mtx)
for (i in 1:b)
{
# find rows for affected genes
gli.start.row=gene.lsn.chr.index$row.start[i]
gli.end.row=gene.lsn.chr.index$row.end[i]
gld.start.row=gene.lsn.index$row.start[gli.start.row]
gld.end.row=gene.lsn.index$row.end[gli.end.row]
gld.rows=gld.start.row:gld.end.row
gene.rows=unique(hit.cnt$gene.lsn.data$gene.row[gld.rows])
n.genes=length(gene.rows)
# find rows for lesions of this type on this chromosomes
lsn.chr.mtch=which((lsn.chr.index$lsn.type==gene.lsn.chr.index$lsn.type[i])&
(lsn.chr.index$chrom==gene.lsn.chr.index$chrom[i]))
lsn.index.start.row=lsn.chr.index$row.start[lsn.chr.mtch]
lsn.index.end.row=lsn.chr.index$row.end[lsn.chr.mtch]
lsn.start.row=hit.cnt$lsn.index$row.start[lsn.index.start.row]
lsn.end.row=hit.cnt$lsn.index$row.end[lsn.index.end.row]
lsn.rows=lsn.start.row:lsn.end.row
n.lsns=length(lsn.rows)
lsn.type=hit.cnt$lsn.data$lsn.type[lsn.start.row]
message(paste0("Computing p-values for ",
n.genes," gene(s) on chromosome ",
gene.lsn.chr.index$chrom[i],
" affected by ",
n.lsns," ",lsn.type,
" (data block ",i," of ",b,"): ",date()))
# find chromosome size
chr.mtch=which(gene.lsn.chr.index$chrom[i]==chr.size$chrom)
chrom.size=chr.size$size[chr.mtch]
# obtain gene sizes, lesion sizes, and gene hit probabilities
lsn.size=hit.cnt$lsn.data$loc.end[lsn.rows]-hit.cnt$lsn.data$loc.start[lsn.rows]+1
gene.size=hit.cnt$gene.data$loc.end[gene.rows]-hit.cnt$gene.data$loc.start[gene.rows]+1
log.pr=log(rep(lsn.size,each=n.genes)+rep(gene.size,times=n.lsns))-log(chrom.size)
pr.gene.hit=matrix(exp(log.pr),n.genes,n.lsns)
pr.gene.hit[pr.gene.hit>1]=1
lsn.subj.IDs=hit.cnt$lsn.data$ID[lsn.rows]
pr.subj=row.prob.subj.hit(pr.gene.hit,lsn.subj.IDs)
max.nsubj=max(hit.cnt$nsubj.mtx[gene.rows,lsn.type])
max.nhit=max(hit.cnt$nhit.mtx[gene.rows,lsn.type])
pr.nhit=row.bern.conv(pr.gene.hit,max.nhit)
pr.nsubj=row.bern.conv(pr.subj,max.nsubj)
for (j in 1:n.genes)
{
nsubj=hit.cnt$nsubj.mtx[gene.rows[j],lsn.type]
nhit=hit.cnt$nhit.mtx[gene.rows[j],lsn.type]
p.nsubj[gene.rows[j],lsn.type]=sum(pr.nsubj[j,(nsubj+1):(max.nsubj+1)])
p.nhit[gene.rows[j],lsn.type]=sum(pr.nhit[j,(nhit+1):(max.nhit+1)])
}
}
rownames(p.nhit)=rownames(hit.cnt$nhit.mtx)
rownames(p.nsubj)=rownames(hit.cnt$nsubj.mtx)
colnames(hit.cnt$nhit.mtx)=paste0("nhit.",colnames(hit.cnt$nhit.mtx))
colnames(hit.cnt$nsubj.mtx)=paste0("nsubj.",colnames(hit.cnt$nsubj.mtx))
colnames(p.nhit)=paste0("p.",colnames(hit.cnt$nhit.mtx))
colnames(p.nsubj)=paste0("p.",colnames(hit.cnt$nsubj.mtx))
# Compute q-values
message(paste0("Computing q-values: ",date()))
q.nhit=p.nhit
q.nsubj=p.nsubj
for (i in 1:ncol(q.nhit))
{
pi.hat=min(1,2*mean(p.nhit[,i],na.rm=T))
q.nhit[,i]=pi.hat*p.adjust(p.nhit[,i],method="fdr")
pi.hat=min(1,2*mean(p.nsubj[,i],na.rm=T))
q.nsubj[,i]=pi.hat*p.adjust(p.nsubj[,i],method="fdr")
}
colnames(q.nhit)=paste0("q.",colnames(hit.cnt$nhit.mtx))
colnames(q.nsubj)=paste0("q.",colnames(hit.cnt$nsubj.mtx))
# Now get ordered p-values
message(paste0("Computing p-values for number of lesion types affecting genes: ",date()))
p.ord.nhit=p.order(p.nhit)
colnames(p.ord.nhit)=paste0("p",1:ncol(p.nhit),".nhit")
p.ord.nsubj=p.order(p.nsubj)
colnames(p.ord.nsubj)=paste0("p",1:ncol(p.nsubj),".nsubj")
# q-values of ordered p-values
q.ord.nhit=p.ord.nhit
q.ord.nsubj=p.ord.nsubj
message(paste0("Computing q-values for number of lesion types affecting genes: ",date()))
for (i in 1:ncol(p.ord.nhit))
{
pi.hat=min(1,2*mean(p.ord.nhit[,i],na.rm=T))
q.ord.nhit[,i]=pi.hat*p.adjust(p.ord.nhit[,i],method="fdr")
pi.hat=min(1,2*mean(p.ord.nsubj[,i],na.rm=T))
q.ord.nsubj[,i]=pi.hat*p.adjust(p.ord.nsubj[,i],method="fdr")
}
colnames(q.ord.nsubj)=paste0("q",1:ncol(p.nsubj),".nsubj")
colnames(q.ord.nhit)=paste0("q",1:ncol(p.nhit),".nhit")
gd.clms=setdiff(colnames(hit.cnt$gene.data),c("glp.row.start","glp.row.end"))
lsn.clms=setdiff(colnames(hit.cnt$lsn.data),c("glp.row.start","glp.row.end"))
gd.clms=c("gene.row",setdiff(gd.clms,"gene.row"))
lsn.clms=c("lsn.row",setdiff(lsn.clms,"lsn.row"))
gene.res=cbind.data.frame(hit.cnt$gene.data[,gd.clms],
hit.cnt$nsubj.mtx,
p.nsubj,
q.nsubj,
p.ord.nsubj,
q.ord.nsubj,
hit.cnt$nhit.mtx,
p.nhit,
q.nhit,
p.ord.nhit,
q.ord.nhit)
res=list(gene.hits=gene.res, # A data table of GRIN results that includes gene annotation, number of subjects and number of hits affecting each locus. in addition, p and FDR adjusted q-values showing the probability of each locus being affected by one or a constellation of multiple types of lesions are also included in the GRIN results.
lsn.data=hit.cnt$lsn.data[,lsn.clms], # Input lesion data
gene.data=hit.cnt$gene.data[,gd.clms], # Input gene data
gene.lsn.data=hit.cnt$gene.lsn.data, # Each row represent a gene overlapped by a certain lesion. Column "gene" shows the overlapped gene and ID column has the patient ID
chr.size=chr.size, # A data table showing the size of the 22 autosomes, in addition to X and Y chromosomes in base pairs
gene.index=hit.cnt$gene.index, # Data.frame that shows row start and row end for each chromosome in the gene.lsn.data table
lsn.index=hit.cnt$lsn.index) # Data.frame that shows row start and row end for each lesion in the gene.lsn.data table
return(res)
}
##############################################################################
# grin.stats: The function run the Genomic Random Interval (GRIN) analysis to
# determine whether a certain locus has an abundance of lesions that is
# statistically significant.
grin.stats=function(lsn.data, # data.frame with columns ID (subject identifier), chrom, loc.start, loc.end, lsn.type
gene.data=NULL, # data.frame with columns gene, chrom, loc.start, loc.end
chr.size=NULL, # data.frame with columns chrom and size
genome.version=NULL) # character string with genome version. Currently, four genome assemblies are accepted including "Human_GRCh38", "Human_GRCh37", "Mouse_HGCm39", and "Mouse_HGCm38"
{
if (is.null(genome.version)&&(is.null(gene.data)||is.null(chr.size)))
{
genome.version=select.list(c("Human_GRCh38",
"Human_GRCh37",
"Mouse_HGCm39",
"Mouse_HGCm38"))
}
if (is.character(genome.version))
{
ensembl.data=get.ensembl.annotation(genome.version)
if (is.null(gene.data))
{
gene.data=ensembl.data$gene.annotation
}