Statistical and Machine Learning Approaches for Network Analysis

1.3 Genome-wide Measurements

In this section, we present an overview of two complementary forms of data resources (Fig. 1.3), both of which have been utilized by the existing network reconstruction algorithms. The first resource is gene expression data, which is represented as matrix of gene expression levels. The second data resource is a gene set compendium. Each gene set in a compendium stands for a set of genes and the corresponding gene expression levels may or may not be available.

Figure 1.3 Two complementary forms of data accommodated by the existing network reconstruction algorithms. (a) Gene expression data generated from high-throughput platforms, for example, microarray. (b) Gene sets often resulted from explorative analysis of large-scale gene expression data, for example, cluster analysis.

1.3.1 Gene Expression Data

Gene expression data is the most common form of data used in the computational inference of biological networks. It is represented as a matrix of numerical values, where each row corresponds to a gene, each column represents an experiment and each entry in the matrix stands for gene expression level. Gene expression profiling enables the measurement of expression levels of thousands of genes simultaneously and thus allows for a systematic study of biomolecular interaction mechanisms on genome scale. In the experimental procedure for gene expression profiling ...