Although rank-based normalization can rescale cross-laboratory data, the performance is still poor in Figure 4.2(c). The reason may be the sequencing machine which generating Prostate-3 is older than others. Prosate-3 is generated from Illumina Genome Analyzer I
Figure 4.4: Results of LOOCV when using rank-based normalization.
(GAI), but Prostate-1 and Prostate-4 are both using Illumina Genome Analyzer II (GAII) and Prostate-2 is using Illumina Hiseq 2000 which is the newest machine. The detail of four datasets and platforms are summarized in Table 4.2. Prostate-3 is single-end data which has higher error rate than pair-end data during mapping to reference genome, and the read count per sample is also fewer than others. The read count of a sample in Prostate-3 is only 5.Prostate-3M which is far less than the others. Although the scheme of RPKM (FPKM) adjust the bias result from different total read count, the few read count may result in many genes ummapped by reads. On the other hand, Prostate-2 performs well in low number of selected genes, and the performance is stable. The read count and base count of each sample in Prostate-2 are far more than other datasets, and Illumina Hiseq 2000 which generates Prostate-2 provides lower error rate and higher perfomance than other platforms. By the advatage of platform, the information provided by Prostate-2 is more stable and it can provide feature selection higher performance. Therefore, not only the cross-laboratoty will affect performance of feature selection, but also the platform used is the factor.
Table4.2:Detailsofdatasetsandplatforms. StudyreferenceNGSgenerationreadtypeAverageAveragereadlength readcountbasecount Prostate-1[15]IlluminaGenomeAnalyzerIIpairend11M0.8G36bp Prostate-2[33]IlluminaHiseq2000pairend34M6.3G100bp Prostate-3[30]IlluminaGenomeAnalyzerIsingleend5.3M0.2M36bp Prostate-4[30]IlluminaGenomeAnalyzerIIpairend12.5M1G36bp
Chapter 5
Conclusions and future work
In our study, we apply rank-based normalization to reduce the influence by cross-laboratory. The performance has a great improvement after appying rank-based normal-ization. Furthermore, the performance of using Random Forest with applying rank-based normalization is better than using the well-known differential gene tool Cuffdiff. Although the prediction result has been improved by rank-based normalization, the balanced accu-racy is still not good enough. To further improve the performance, it may use better ma-chine which generates the sequence data. We have discussed that the sequencing mama-chine is also an important factor which affects the preformance of feature selection on cross-lab RNA-seq datasets. The better platform provide more effective and stable information for feature selection, and it performs well in the prediction results. Hence, by the develop-ment of RNA-seq technology, the data generated by the newer machine would be more suitable for cross-laboratory analysis.
RNA-sequencing technology provides expression level of genes and sequence struc-ture of RNA. In our study, we only use gene expression and we want to take an advantage of sequence structure to further analysis. Next, we want to apply the cross-laboratory feature selection in gene fusion detection. The gene fusion occurs frequently in prostate cancer Gene fusion means that two previously separated genes fuse together to a new gene. Due to the dataset are across different laboratories and different races, we are able to detect the common gene fusion event in specific race or among different races.
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