Tumor Classification Using High-Order Gene Expression Profiles Based on Multilinear ICA

Abstract

Motivation. Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training gene expression profiles (GEP) ensemble, but it cannot distinguish relations between the different factors, or different modes, and it is not available to high-order GEP Data Mining. In order to generalize ICA, we introduce Multilinear-ICA and apply it to tumor classification using high order GEP. Firstly, we introduce the basis conceptions and operations of tensor and recommend Support Vector Machine (SVM) classifier and Multilinear-ICA. Secondly, the higher score genes of original high order GEP are selected by using t-statistics and tabulate tensors. Thirdly, the tensors are performed by Multilinear-ICA. Finally, the SVM is used to classify the tumor subtypes. Results. To show the validity of the proposed method, we apply it to tumor classification using high order GEP. Though we only use three datasets, the experimental results show that the method is effective and feasible. Through this survey, we hope to gain some insight into the problem of high order GEP tumor classification, in aid of further developing more effective tumor classification algorithms.

Figure 1

Third-order tensor.

Figure 2

The gene distribution frequency versus gene S-values.

Figure 3

Training tensor A _tn.

Figure 4

Training core tensor S _tn.

Figure 5

Three matrixes U ₁, U ₂, and U ₃.

Figure 6

Training tensor A _tn.

Figure 7

Training core tensor S _tn.

Figure 8

Three matrixes U ₁, U ₂, and U ₃.

Figure 9

MICA for three-order lung microarray.