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Table 2 Performance indices on artificial data of half-lives and expression time-courses with different noise amplitudes (C)

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

\(FDR<0.05\)

\(C=0.1\)

10

1

0.90

\(<10^{-309}\)

\(99.6\%\)

\(C=0.2\)

10

2

0.86

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

\(95.2\%\)

\(C=0.3\)

10

2

0.86

\(2.3\cdot 10^{-269}\)

\(91.1\%\)

\(C=0.4\)

10

4

0.68

\(8.9\cdot 10^{-123}\)

\(90.8\%\)

\(C=0.5\)

10

10

0.11

\(3.9\cdot 10^{-4}\)

\(90.8\%\)

  1. Numbers of gene expression profiles (\(m = 1000\)) and of time samples (\(n=6\)) 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