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Table 3 Performance indices on artificial data of half-lives and expression time-courses with different numbers of half-lives to be estimated (m)

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

\(FDR<0.05\)

\(m=1000\)

10

2

0.86

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

\(95.2\%\)

\(m=800\)

10

3

0.82

\(3.0\cdot 10^{-180}\)

\(93.4\%\)

\(m=500\)

10

3

0.78

\(2.0\cdot 10^{-94}\)

\(91.0\%\)

\(m=200\)

10

6

0.78

\(3.5\cdot 10^{-38}\)

\(90.0\%\)

  1. Number of time samples (\(n = 6\)) 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