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```
allExplained = function(genotype,cspAlleles,knownWithStutter,alleleNames,doDoubleStutter=FALSE,doOverStutter=FALSE)
{
genotypeAlleles = as.numeric(alleleNames[genotype])
genWithStutter = c(genotypeAlleles,genotypeAlleles-1)
if(doDoubleStutter) genWithStutter = c(genWithStutter,genotypeAlleles-2)
if(doOverStutter) genWithStutter = c(genWithStutter,genotypeAlleles+1)
all(cspAlleles%in%c(genWithStutter,knownWithStutter))
}
explain.all.peaks = function(cspPresence,profPresence,knownProfs,alleleNames,nUnknowns,cspAlleles,cspHeights, doDoubleStutter=FALSE,doOverStutter=FALSE,doDropin=FALSE)
{
# Catch early. Shouldn't be a problem here, but will be at some point.
if(!is.matrix(cspPresence)) stop("Expected a matrix as input.")
if(!is.matrix(profPresence)) stop("Expected a matrix as input.")
# add extra unknown contributor if dropin and no unknowns
#if(nUnknowns == 0 && doDropin == TRUE) nUnknowns = 1
if(ncol(profPresence)!=0)
{
# get known alleles
knownIndex = sapply(unlist(knownProfs),FUN=function(x) which(alleleNames==x))
knownAlleles = as.numeric(alleleNames[knownIndex])
# include stuttered known alleles
knownWithStutter = c(knownAlleles,knownAlleles-1)
if(doDoubleStutter) knownWithStutter = c(knownWithStutter, knownAlleles-2)
if(doOverStutter) knownWithStutter = c(knownWithStutter, knownAlleles+1)
} else {
knownIndex = c()
knownWithStutter = c()
}
# get csp Alleles
cspAlleles = alleleNames[row(cspPresence)[which(cspPresence)]]
# if no unknowns return knowns
if(nUnknowns==0)
{
check = !all(round(as.numeric(cspAlleles),1)%in%round(as.numeric(knownWithStutter),1))
if(doDropin) check=FALSE
if(check)
{
stop(paste0("Not enough contributors to explain CSP at locus ", colnames(knownProfs)))
} else {
return(matrix(knownIndex,ncol=1))
}
}
# get all genotype combinations for unknowns
genCombs = all.genotypes.per.locus(length(alleleNames),nUnknowns)
# if dropin add knowns and return
if(doDropin)
{
# add known profiles as last contributors
if(length(knownIndex)!=0)
{
genCombs = rbind(genCombs,matrix(rep(knownIndex,times=ncol(genCombs)),
ncol=ncol(genCombs)))
}
return(genCombs)
}
# find which combinations explain all peaks
index = apply(genCombs,MARGIN=2,FUN=function(x) allExplained(x,cspAlleles,knownWithStutter,alleleNames,doDoubleStutter,doOverStutter))
if(length(which(index))==0) stop(paste0("Not enough contributors to explain CSP at locus ", colnames(knownProfs)))
genCombs = genCombs[,index,drop=FALSE]
# add known profiles as last contributors
if(length(knownIndex)!=0)
{
genCombs = rbind(genCombs,matrix(rep(knownIndex,times=ncol(genCombs)),ncol=ncol(genCombs)))
}
return(genCombs)
}
# Defines prod.matrix from R.
prod.matrix.row <- function(x) {
# Fast column-wise product.
#
# Sadly, this is faster than apply(x, 1, prod)
y=x[,1]
for(i in 2:ncol(x))
y=y*x[, i]
return(y)
}
ethnic.database.lus <- function(ethnic, loci=NULL, afreq=NULL) {
# Reformats allele database to include only given ethnic group and loci.
#
# Filters database down to a single ethnic group and the loci of interests.
# Removes alleles which are not present in given ethnic group. Centers
# average length across filtered database.
#
# Parameters:
# ethnic: Name of ethnic group. Should correspond to what's in afreq.
# loci: Names of the loci to use. Or use all.
# afreq: Frequency table. If NULL, loads in frequency table provided with
# likeLTD package.
# Load frequency database if needed.
if(is.null(afreq)) afreq <- load.allele.database()
# figueres out loci
if(is.null(loci)) loci <- t(unique(afreq['Marker']))
# Function which recreates the input for a single locus.
# Input of a locus consists of one row per allele type.
# Also filters out alleles with 0 frequencies.
filter.locus <- function(n) {
locus <- afreq[afreq$Marker == n, ]
result <- matrix(c(locus[[ethnic]], locus[["BP"]], locus[["LUS"]]), , 3)
result[is.na(result[, 2]), 2] <- 0
rownames(result) <- locus$Allele
if(any(is.na(result[,3])))
{
result = fill.unknown.LUS(result)
}
return(result[result[, 1] > 0, ])
return(result)
}
# now apply function over all loci.
result <- sapply(loci, filter.locus)
# Then center around mean fragment length
s1 = sum( sapply(result, function(n) sum(n[, 1] * n[, 2], na.rm=TRUE)) )
s2 = sum( sapply(result, function(n) sum(n[, 1], na.rm=TRUE)) )
for(j in 1:length(result)) result[[j]][, 2] <- result[[j]][, 2] - s1/s2
return(result)
}
```

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