64 lines
1.6 KiB
R
64 lines
1.6 KiB
R
#' @export
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mode <- function(x) {
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d <- density(x)
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d$x[which.max(d$y)]
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}
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#' @export
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tag_bad_pcr = function(samples,counts,plot = TRUE) {
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counts = decostand(counts,method = "hellinger")
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bc = aggregate(counts,
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by=list(factor(as.character(samples))),
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mean)
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bc.name = as.character(bc[,1])
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bc = bc[-1]
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rownames(bc)=bc.name
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bc = bc[as.character(samples),]
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d = sqrt(rowSums((counts - bc)^2))
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names(d) = as.character(samples)
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d.m = mode(d)
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d.sd = sqrt(sum((d[d <= d.m] - d.m)^2)/sum(d <= d.m))
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d.max = aggregate(d,
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by = list(factor(as.character(samples))),
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max)
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d.max.names = d.max[,1]
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d.max = d.max[,2]
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names(d.max) = d.max.names
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d.max = d.max[as.character(samples)]
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d.len = aggregate(d,
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by = list(factor(as.character(samples))),
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length)
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d.len.names = d.len[,1]
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d.len = d.len[,2]
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names(d.len) = d.len.names
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d.len = d.len[as.character(samples)]
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keep = ((d < d.m + (d.sd*2)) | d!=d.max) & d.len > 1
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selection = data.frame(samples = as.character(samples),
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distance= d,
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maximum = d.max,
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repeats = d.len,
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keep = keep,
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stringsAsFactors = FALSE)
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rownames(selection)=rownames(counts)
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attributes(selection)$dist.mode = d.m
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attributes(selection)$dist.sd = d.sd
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if (plot) {
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hist(d)
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abline(v=d.m,lty=2,col="green")
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abline(v=d.m + (d.sd*2),lty=2,col="red")
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}
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return(selection)
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}
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