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@@ -204,17 +204,17 @@ optimal.off.target.counts = 120, plot = FALSE, ...) {
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fcnts_std[, i] <- fcnts_std[,i] / iv
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}
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} else {
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- fcnts_std <- apply(fcnts,2,function(x) x/fcnts_interval_medians)
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+ fcnts_std <- apply(fcnts, 2, function(x) x / fcnts_interval_medians)
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}
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- fcnts_interval_non_zero_medians <- apply(fcnts_std, 1, function(x) median(x[x>0]))
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- fcnts_std_imp <- apply(fcnts_std, 2, function(x) { x[x<=0] <- fcnts_interval_non_zero_medians[x<=0]; x})
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- p=0.001
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- li <- quantile(as.vector(fcnts_std_imp), probs= c(p, 1-p))
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+ fcnts_interval_non_zero_medians <- apply(fcnts_std, 1, function(x) median(x[x > 0]))
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+ fcnts_std_imp <- apply(fcnts_std, 2, function(x) { x[x <= 0] <- fcnts_interval_non_zero_medians[x <= 0]; x})
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+ p <- 0.001
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+ li <- quantile(as.vector(fcnts_std_imp), probs = c(p, 1 - p))
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fcnts_std_trunc <- fcnts_std_imp
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fcnts_std_trunc[fcnts_std_imp < li[1]] <- li[1]
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fcnts_std_trunc[fcnts_std_imp > li[2]] <- li[2]
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fcnts_std_final <- apply(fcnts_std_trunc, 2, function(x) log2(x / median(x)))
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- fcnts_std_final - median(apply(fcnts_std_final,2,median))
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+ fcnts_std_final <- fcnts_std_final - median(apply(fcnts_std_final, 2, median))
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s <- svd(fcnts_std_final)
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ret <- list(
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