103 lines
1.9 KiB
R
103 lines
1.9 KiB
R
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get_Poisson_conf_intervals = function(seq_range, quantile_vec = c(0.05,0.95), plot=T) {
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num_rows = length(seq_range)
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num_cols = length(quantile_vec)
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m = matrix(nrow=num_rows, ncol=num_cols)
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colnames(m) = quantile_vec
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rownames(m) = seq_range
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row_count = 0
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for (i in seq_range) {
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q = qpois(quantile_vec, i)
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row_count = row_count + 1
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m[row_count,] = q
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}
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results = list()
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results$mat = m
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if (plot) {
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percents = plot_conf_intervals(m)
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results$pct = percents
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}
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return(results)
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}
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get_NB_conf_intervals = function(seq_range, dispersion, quantile_vec = c(0.05,0.95), plot=T) {
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size = 1/dispersion #according to the mu-definition of size in nbinom of R, where var = mean + (1/size)mean^2
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num_rows = length(seq_range)
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num_cols = length(quantile_vec)
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m = matrix(nrow=num_rows, ncol=num_cols)
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colnames(m) = quantile_vec
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rownames(m) = seq_range
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row_count = 0
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for (i in seq_range) {
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q = qnbinom(quantile_vec, mu=i, size=size)
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row_count = row_count + 1
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m[row_count,] = q
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}
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results = list()
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results$mat = m
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if (plot) {
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percents = plot_conf_intervals(m)
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results$pct = percents
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}
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return(results)
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}
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plot_conf_intervals = function(m) {
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par(mfrow=c(1,2))
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c_names = colnames(m)
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r_vals = as.numeric(rownames(m))
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max_val = max(m)
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plot(r_vals, r_vals, xlab="known read counts", ylab="Poisson read counts dist", t='l')
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# plot the confidence levels
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line_colors = rainbow(length(c_names))
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for(i in 1:length(c_names)) {
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points(r_vals,m[,i], t='l', col=line_colors[i])
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}
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# plot the max percentage of value for 95% conf level.
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percents=c()
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for (i in 1:length(r_vals)) {
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max_delta = max(abs(m[i,]-r_vals[i]))
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percent = max_delta/r_vals[i]*100
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percents[i]=percent;
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}
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plot(r_vals, percents, ylim=c(0,100), xlab="read counts", ylab="percent of value for 95% conf interval")
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percents
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}
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