Simulation Study II: DE Gene Detection Performances Clause Samples

Simulation Study II: DE Gene Detection Performances. Similar to section 2.2.2, I conduct a simulation study to compare stHM and swHM with IPBT and other well-established methods for detecting DE genes: (i) Student’s t-test, (ii) ▇▇▇ (R package ‘siggenes’); (iii) Limma, (R package ‘Limma’); (iv) Z test using the true variance; and (v) IPBT (R package ‘IPBT’). Expressions for 1000 genes in k (ranging from 2 to 5) samples are simulated for both the control and treatment groups. 10% of the 1000 genes (i.e. 100 genes) are randomly selected as designated DE genes. Gene expression values in both the control and treatment groups are assumed to follow normal distributions. We derive each gene’s sample mean and variance from the 566 normal samples in the collection. For the treatment group, the mean and variance of a gene’s expression value are assumed to be the same as their counterparts in the control group except for the DE genes. The mean expression values for DE genes in the treatment group are two standard deviations higher. For historical data used by IPBT, stHM, swHM, 10 normal samples are randomly chosen out of 566 (without replacement) from the global gene expression map. We use the empirical FDR (Benjamini & ▇▇▇▇▇▇▇▇, 1995; Tusher et al., 2001) to evaluate the performance for the top 100 genes ranked by the test statistics. The simulation is repeated 500 times for each method. Figure 20(a) summarizes the distributions of the 500 FDRs for different methods by box plots. All methods using historical data clearly performs better than methods without using historical data except for the Z test with true variances (considered the gold standard). The methods using historical data and Z test have fairly close performances. We also use Receiver Operator Characteristic (ROC) curves to compare different methods. Figure 20(b) shows a typical example of ROC curve for one single simulation with sample size k = 2. The ROC curves again show that methods with historical data perform better than methods without historical data except for the Z test, and the performances of methods with historical data and Z test are similar. We also repeat the simulation with a noisier historical data. Figure 21 shows that IPBT’s performance started to deteriorate with noisier historical data while our new strategies maintain its performance advantage. Figure 20 and 21 together demonstrate that our new strategies could be almost as good as IPBT with accurate historical data and perform more robust than IPBT when the historical data becomes ...
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Simulation Study II: DE Gene Detection Performances. Simulation strategy DE gene detection result
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