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Table 1 Comparison of false discovery rate (FDR) of our quantile regression methods and linear regression methods using simulation data.

From: A model selection approach to discover age-dependent gene expression patterns using quantile regression models

  DE DV
FDR DE2 DE5 DE5 + outliers DE9 DE9 + outliers DV DV + outliers
Quantile Regression (QR) 0.021 0.040 0.049 0.082 0.151 0.017 0.023
Linear Regression (LR) 0.061 0.160 0.204 0.230 0.38 0.083 0.262
FDR QR /FDR LR 0.340 0.247 0.237 0.357 0.396 0.214 0.087
  1. The FDRs of applying our quantile regression method to seven simulated datasets are compared to the corresponding FDRs of applying linear regression based methods to identify DE and DV genes at a predefined threshold of α = 0.05 (for quantile regression) and α l = 0.05 (for linear regression). At this commonly accepted threshold, we found that our quantile regression method yields FDRs that are consistently about only one third of that the corresponding FDR when the linear regression approach is used.