Cancer Gene Diagnosis of Shipp et al. Microarray

Shuichi Shinmura ()
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Shuichi Shinmura: Seikei University

Chapter Chapter 6 in High-dimensional Microarray Data Analysis, 2019, pp 237-289 from Springer

Abstract: Abstract Shipp microarray consists of 77 patients and 7,129 genes. They analyzed the microarray by various statistical methods and uploaded a supplemental document with 67 pages. They used almost the same methods as Golub et al. except for SVM and nearest neighbor cluster. Mainly, discriminant analysis is the most appropriate method to identify oncogenes from the microarray. However, because the statistical discriminant analysis was useless at all, medical researchers had developed many methods for cancer gene analysis. Our theory shows that six microarrays are LSD (Fact3). Method2 decomposes the microarrays into many SMs (Fact4). Then, by analyzing SM, we propose cancer gene diagnosis and malignancy indexes. If Shipp et al. validate our research results, we will improve cancer gene diagnosis. Method2 already obtained SM twice in Chap. 2 . In this research, we change the number of iterations of RIP and Revised LP-OLDF in Method2 and decided the proper number of iterations. We obtain SMs by those iteration numbers in 2018. We examined the signal subspace made by all SMs and the noise space. However, Revised LP-OLDF cannot correctly find all SMs from Shipp microarray as same as Chap. 4 . Thus, we analyze only 237 SMs obtained by the RIP and examine the correlation coefficient of RipDSs. RatioSVs evaluate RIP, Revised LP-OLDF, and H-SVM. Then, we analyze two signal data and transposed data made by RIP and H-SVM. By the hierarchical cluster analysis and PCA, we can propose the possibility of cancer gene diagnoses such as malignancy indexes.

Keywords: Shipp microarray; Cancer gene diagnosis; Malignancy indexes; Defect of signal found by Revised LP-OLDF; Small Matryoshka (SM); RatioSV of PCA; RipDSs; LpDSs; HsvmDSs (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-5998-9_6

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DOI: 10.1007/978-981-13-5998-9_6

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