Real Data Study II Sample Clauses

Real Data Study II. Latin Square Hgu133a Spike-in Experiment Data This data set consists of three replicates of 14 separate hybridizations of 42 spiked transcripts in a complex human background (HeLa cells) at concentrations ranging from 0.125pM to 512pM (Affymetrix, Santa Clara, CA). Since the spike-in genes are known, this dataset has been widely used in evaluating the performance of Microarray preprocessing algorithms (McCall, Bolstad, & Xxxxxxxx, 2010; X. Xx & Xxxxxxxx, 2004) and DE gene analysis methods (Lo & Gottardo, 2007). In our study, each time we select two out of the 14 separate hybridizations as the control and treatment groups (each group has three replicates) respectively. All 91 pairs are tested for DE gene detection. After excluding the probes that do not exist in Affymetrix GeneChip U133A, 34 probes are bona fide differentially expressed each time. We use 42 datasets from HeLa cells (cervical adenocarcinoma cell line) from the global gene expression map as the historical data. In this study, each method generates a DE probe list (ranked by the test statistics) in every pair of the control and treatment groups and we obtain the proportion of correct DE calls (match the 34 bona fide DE probes). Table 11 summarizes the average number of correctly identified DE probes among the top k ( = 5, 10, … ,40) probes across all 91 control and treatment combinations. Figure 16 shows the box plots of FDRs for the top k probes called significant. IPBT consistently detects more bona fide DE probes hence has a lower FDR in terms of the median across all the experiments. In addition, IPBT is more robust since it consistently shows the smallest interquartile ranges in the boxplot. All these results show that IPBT performs better than other methods. Table 11 Average number of correctly identified DE probes across all 91 group pairs on Spike-in Experiments data among the top k probes. Top k 5 10 15 20 25 30 35 40 student's t-test 3.1 5.9 8.6 11.2 13.8 16.3 18.7 21.0 XXX 3.3 6.2 8.9 11.6 14.3 16.7 19.5 22.3 Limma 3.3 6.2 8.8 11.6 14.1 16.8 19.5 22.3 IPBT 3.9 7.4 10.4 13.3 16.4 19.1 22.0 25.0 * The best results are in bold. Figure 16 All the detection methods are applied to all 91 pairs of hybridizations. Box plots of FDRs are shown for all 91 group pairs when calling top k probes significant.
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Real Data Study II. DNA Methylation Data Datasets DNA methylation 450K array is an array-based technology measuring more than 485,000 CpG sites. On the other hand, BS-Seq, covering the whole genome (around 28 million CpG sites), is considered a better technology for measuring DNA methylation. Here we use 50 liver cancer (LIHC) and matched normal control samples from The Cancer Genome Atlas (Cancer Genome Atlas 2012). Detailed barcodes of all these samples can be found in Section 3.5. For BS-Seq data, we use data from liver and hippocampus samples from the Roadmap Epigenomics project (Xxxxxxxxx et al. 2010) (GEO accession number GSE64577). Analyze 450K array data using 450K array historical data Similar to Section 3.3.1 on gene expression microarray data, we take two normal and two cancer datasets and treat them as being collected from the current experiment. All other normal data are used as historical data. Figure 24 (a) shows that stHM and swHM achieve a better agreement than LIMMA and t-test. FDR and ROC curves in Figure 24 (b) and (c) again illustrate that methods with historical data could benefit from historical data.

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