A systems biology approach for pathway level analysis

TLDR

Genomics data analysis must account for complex interactions across signaling pathways, yet statistical methods commonly used to identify relevant pathways often overlook these interactions. This study demonstrates that existing pathway analysis methods can yield incorrect results by neglecting key biological factors and proposes a deeper, pathway‑specific statistical approach. Using a systems biology framework, the authors developed an impact analysis that integrates classical statistics with gene‑expression magnitude, type, position, and interaction effects, and released it as the web‑based Pathway‑Express tool. On multiple illustrative datasets, the impact analysis eliminated false positives and negatives seen with classical methods and produced biologically meaningful results.

Abstract

A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to identify the most relevant pathways in a given experiment. Here, we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. By using a systems biology approach, we developed an impact analysis that includes the classical statistics but also considers other crucial factors such as the magnitude of each gene’s expression change, their type and position in the given pathways, their interactions, etc. The impact analysis is an attempt to a deeper level of statistical analysis, informed by more pathway-specific biology than the existing techniques. On several illustrative data sets, the classical analysis produces both false positives and false negatives, while the impact analysis provides biologically meaningful results. This analysis method has been implemented as a Web-based tool, Pathway-Express, freely available as part of the Onto-Tools ( http://vortex.cs.wayne.edu ).