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Table 1 Performance indices on artificial data of half-lives and expression time-courses with different numbers of time samples (n)

From: StaRTrEK:in silico estimation of RNA half-lives from genome-wide time-course experiments without transcriptional inhibition

 

\(\alpha _{opt}\)

\(q_{\text {max}}\)

\(\rho\)

pval

\(FDR<0.05\)

\(n=12\)

10

3

0.89

\(<10^{-309}\)

\(92.5\%\)

\(n=10\)

10

3

0.89

\(<10^{-309}\)

\(94.4\%\)

\(n=8\)

10

4

0.89

\(<10^{-309}\)

\(94.2\%\)

\(n=6\)

10

2

0.86

\(1.3\cdot 10^{-277}\)

\(95.2\%\)

  1. Number of gene expression profiles (\(m = 1000\)) and noise amplitude (\(C=0.2\)) are kept fixed. Legend—\(\alpha _{opt}\): optimal value of the regularization parameter; \(q_{\text {max}}\): error threshold expressed as percentile of the MSE distribution; \(\rho\): Pearson correlation coefficient; pval: p value; FDR: false discovery rate. For each instance of the noise distribution considered, we found a negligible variability of the quality indices, therefore variance is not reported in the table