Multi-view based integrative analysis of gene expression data for identifying biomarkers

Zi-Yi Yang,Xiao-Ying Liu,Yong Liang,Hui Zhang,Yan-Qiong Ren,Zong-Ben Xu,Jun Shu

doi:10.1038/s41598-019-49967-4

Abstract

The widespread applications in microarray technology have produced the vast quantity of publicly available gene expression datasets. However, analysis of gene expression data using biostatistics and machine learning approaches is a challenging task due to (1) high noise; (2) small sample size with high dimensionality; (3) batch effects and (4) low reproducibility of significant biomarkers. These issues reveal the complexity of gene expression data, thus significantly obstructing microarray technology in clinical applications. The integrative analysis offers an opportunity to address these issues and provides a more comprehensive understanding of the biological systems, but current methods have several limitations. This work leverages state of the art machine learning development for multiple gene expression datasets integration, classification and identification of significant biomarkers. We design a novel integrative framework, MVIAm - Multi-View based Integrative Analysis of microarray data for identifying biomarkers. It applies multiple cross-platform normalization methods to aggregate multiple datasets into a multi-view dataset and utilizes a robust learning mechanism Multi-View Self-Paced Learning (MVSPL) for gene selection in cancer classification problems. We demonstrate the capabilities of MVIAm using simulated data and studies of breast cancer and lung cancer, it can be applied flexibly and is an effective tool for facing the four challenges of gene expression data analysis. Our proposed model makes microarray integrative analysis more systematic and expands its range of applications.

Highlights

Microarray technology is one of the most recent advances being used for cancer research, which can measure the expression levels of many thousands or tens of thousands of genes simultaneously
Multi-View Self-Paced Learning (MVSPL) corresponds to the sum of Self-paced learning (SPL) model under multiple views plus a regularization term
We demonstrate the performance of the proposed MVSPL in simulation and real microarray experiments

Summary

Introduction

Microarray technology is one of the most recent advances being used for cancer research, which can measure the expression levels of many thousands or tens of thousands of genes simultaneously. These four issues reveal the complexity of gene expression data, which constrains the development of microarray technology in clinical applications To face these challenges and take advantage of multiple published gene expression datasets, the integrative analysis of gene expression data has become an effective tool by aggregating multiple datasets and increasing the statistical power in identifying a small subset of genes to effectively predict the type of the disease[12,13]. It is difficult to identify non-redundant significant genes and systematically determine (e.g. cross-validation) how many genes to include in the subset, such as GeneMeta[18] and metaArray[19] Such methods neglect correlations among genes and do not eliminate the batch effects between different datasets. The batch effects cannot be completely eliminated, meaning that each view of the data still has different types of bias

Methods

Results

Conclusion