Executable pathway analysis using ensemble discrete-state modeling for large-scale data

摘要

21st-century biotechnology has enabled measurements of genes and proteins at large scale by RNA sequencing and proteomics technologies. In particular, RNA-sequencing has become a first step of unbiased interrogation. These studies frequently produce a long list of differentially abundant genes, which become interpretable by widely used pathway analysis methods. The pathway topologies frequently include information on how genes interact and influence each other's expression, but current methods do not utilize this information to estimate signal flow through each pathway. We have developed a model of binary (on/off) behavior that accounts for varying expression across samples as different proportions of cells expressing genes. We model signal flow by averaging repeated simulations of individual cells passing binary signals through molecular networks. We use this model to infer regulatory rules explaining gene expression. These rules of signal integration for all nodes in the network are used to identify the most important genes, and to determine if a pathway's activity is different between two groups. BONITA compares favorably to previous approaches using simulated and real data. Furthermore, application to 36 datasets from 15 different diseases demonstrates BONITA's exceptional ability to detect drug targets.