PathAct: a novel method for pathway analysis using gene expression profiles

Kaoru Mogushi,Hiroshi Tanaka

doi:10.6026/97320630009394

Abstract

We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted.

Highlights

Gene expression profiling by microarray analysis provides a huge amount of biological information and has been widely used in biological and clinical research
252 patients had information for disease-free survival (DFS), which was calculated from the date of diagnosis until date of first relapse or date of death. These gene expression profiles were converted into a pathway activity matrix using PathAct, and we investigated pathways associated with DFS using Cox proportional hazards model
Pathway analysis plays an important role in interpreting genome-wide gene expression data

Summary

Introduction

Gene expression profiling by microarray analysis provides a huge amount of biological information and has been widely used in biological and clinical research. Since microarray technique simultaneously detects expression levels for more than ten thousand of genes, bioinformatics approaches for interpretation of such large-scale data are essential. Several interpretation tools such as gene set enrichment analysis (GSEA) [5], the Database for Annotation, Visualization and Integrated Discovery (DAVID) [6], GenMAPP [3], and GOMiner [7] have been developed and widely used in the microarray analysis. The majority of tools for pathway analysis detect pathway-level difference between two groups (e.g., cases and controls). PathAct can estimate individual pathway activity by conversion of gene expression data into quantitative values for both of each pathway and each sample. One of the most unique features is that the output data is given in matrix form, which can be used

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