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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.