Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Abstract

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases.

Discovery of synergistic genetic network: A minimum spanning tree-based approach

“…21 The concept of BCT has also been used by Tsai et al to identify single and multiple class speci¯c signature genes from their expression pro¯les. 22 We have previously seen in Eq. (5) that, synergy between two genes G 1 and G 2 can be quanti¯ed as SðG 1 ; G 2 ; CÞ ¼ min½HðCjG 1 Þ; HðCjG 2 Þ À HðCjG 1 ; G 2 Þ.…”

Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta‐analysis methods

“…Microarray data can be used to look for differences in gene expression between ADC cases and normal controls, or different tumor samples. As specific signature genes, informative DE genes can be applied to construct prediction and diagnosis model . Van Gele et al .…”

Discovering monotonic stemness marker genes from time-series stem cell microarray data

“…To assess the statistical significance of the DE total index associated with the identified genes in ascending and descending profiles, a permutation test has been performed in Figure 2 . Both unadjusted p -values and adjusted q -values for multiple comparisons are computed as in our previous studies [ 7 , 8 ]. Let G be the total number of genes and S be the total number of sample points.…”