Simulation Study II: DE Gene Detection Performances Sample Clauses

Simulation Study II: DE Gene Detection Performances. Simulation strategy This simulation study considers 1,000 genes and k (ranging from 2 to 5) samples for both the treatment and control groups. We randomly select 10% of the 1,000 genes (i.e. 100 genes) as designated DE genes. Gene expression values in both the treatment and control groups are assumed to follow normal distributions. The distribution parameters are obtained from real data in the global gene expression map. First, 1,000 genes are randomly selected (without replacement) genome-wide. Then for each gene, we derive its sample mean and sample 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 equal to their counterparts in the control group except for the 100 DE genes for which the mean expression values are set to be two standard deviations higher. For historical data used by IPBT, we first randomly select 188 normal samples out of 566 (without replacement) from the global gene expression map, then obtain their gene expression values corresponding to the 1,000 genes selected earlier. We compare IPBT with four alternative methods for detecting DE genes: (i) Student’s t-test, (ii) XXX, achieved by R package "siggenes"; (iii) Limma, achieved by R package "Limma"; and (iv) Z test using the true variance (This is regarded as the best possible method). DE gene detection result To evaluate the performance, we calculate the empirical false discovery rate (FDR) (Benjamini & Xxxxxxxx, 1995; Tusher et al., 2001) (also known as false discovery proportion--the proportion of incorrect DE calls among all the ones called) from the top 100 genes ranked by the test statistics. The simulation procedure is repeated 500 times for each method. The distributions of the 500 FDRs of the methods are summarized using box plots and shown in Figure 5(a). Our method clearly outperforms all other methods except for the Z test using true variances (considered the gold standard). The performances of our method and Z test are fairly close. Remarkably, the FDR of DE genes detected by IPBT is even smaller than the FDR of DE genes detected by the Student's t-test with larger sample size (i.e. increased by one). We use Receiver Operator Characteristic (ROC) curves to further compare IPBT with the other methods. Figure 5(c) shows a typical ROC curve for one single simulation with two replicates. Detailed area under the curve (AUC) corresponding to Figure 5(c) is listed in Table 6 ("Rando...
Sample 1
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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) XXX (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 Figure 20 Simulation with accurate historical data (a) FDR (b) a typical ROC curve 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 & Xxxxxxxx, 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 Figure 21 Simulation with inaccurate historical data (a) accurate historical data (b) historical data with 20% unbiased noise (c) historical data with 30% unbiased noise (d) historical data with 50% unbiased noise 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...
Sample 1

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