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Table 1 Estimation of model parameters \( \hat{\alpha}\ and\ \hat{\beta} \) under different α and β with fixed number of cells, prct, s, and gene number = 15,000. Comparing with the noise model in Eq. 9, we have obtained fairly low RMSE in each condition

From: Detection of high variability in gene expression from single-cell RNA-seq profiling

Simulation parameters Regression results
# of cells Prct (%) s α β \( \hat{\alpha} \) \( \hat{\beta} \) RMSE RMSE (Eq. 9)
1,000 10 2 0 1 0.0003 ± 0.0002 1.0293 ± 0.0014 0.0074 ± 0.0006 0.044 ± 0.010
1.2 0.0004 ± 0.0003 1.2187 ± 0.0024 0.0047 ± 0.0006 0.066 ± 0.020
1.5 0.0007 ± 0.0004 1.5032 ± 0.0039 0.0028 ± 0.0007 0.091 ± 0.019
0.15 1 0.1557 ± 0.0005 1.0116 ± 0.0009 0.0047 ± 0.0003 0.049 ± 0.008
1.2 0.1562 ± 0.0007 1.1965 ± 0.0020 0.0038 ± 0.0004 0.030 ± 0.004
1.5 0.1569 ± 0.0007 1.4756 ± 0.0047 0.0043 ± 0.0006 0.017 ± 0.001
0.5 1 0.5146 ± 0.0013 1.0020 ± 0.0010 0.0038 ± 0.0004 0.060 ± 0.006
1.2 0.5161 ± 0.0011 1.1837 ± 0.0023 0.0039 ± 0.0003 0.047 ± 0.006
1.5 0.5187 ± 0.0016 1.4561 ± 0.0045 0.0054 ± 0.0005 0.030 ± 0.004