Pathway and network analysis in proteomics

Abstract

Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

Keywords: Complex networks; Functional analysis; Hybrid strategy; Network modules; Pathway analysis.

Figure 1

Trends of pathway and network analysis in Proteomics from decade publications (searched in Google Scholar with terms of [“pathway analysis” AND “Proteomics”], and [“network analysis” AND “Proteomics”]).

Figure 2

Conceptual plot of different pathway analysis tools according to the utilization of functional information and/or topological information (positions are NOT absolute).

Figure 3

Illustration of multi-scale pathway analysis using colorectal cancer proteomic data as an example. The protein-protein interaction (PPI) database for the Step 8) could use STRING or HAPPI.

Figure 4

Re-ordered network adjacency matrices of a weighted BRCA-related protein interaction network with 1035 proteins and 1582 interaction, expanded in HAPPI from 223 breast cancer associated genes from OMIM. (A) The result ranked by GeneRank (similar to PageRank algorithm used by Google), (B) The result clustered by 2D hierarchical clustering in Matlab Bioinformatics Toolbox, and (C) The result reordered by Ant Colony Optimization Reordering (ACOR) algorithm. (CS: confidence score for protein interaction in the HAPPI database)

Figure 5

Prostate cancer microarray classification between primary prostate tumor (PT) samples and metastatic (MT) samples by using terrain-based visual analytics approach. The terrain model derived from a PC-specific protein interaction network containing 2637 proteins and 5772 interactions. 24 gene expression profiles (12 PT samples and 12 MT samples) are randomly selected from a microarray dataset GSE6919 in GEO. The only one PT sample classified incorrectly is marked.