The spindle apparatus is a crucial cellular component, which is responsible for chromosome segregation during cell division. Its construction is a complex process with different checkpoints to avoid failures which may lead to cancer. Spindle organizing is assumed to contain signal cascades and feedback loops for controlling this process. It is unknown whether this process is based on nonlinear regulations of transcripts to maximize its efficiency.
Systems biologists usually construct gene networks to model a biological process based on known interactions between these genes. While linear gene interactions can be detected robustly, it is difficult to reliably detect nonlinear dependencies between genes because nonlinearity and noise must be distinguished from each other.
We propose a new generative model to detect nonlinear interactions between genes. The model extends linear Gaussian factor analysis where gene expression values belonging to a pathway are mainly driven by a single latent factor. In our model, genes are also nonlinearly driven by the hidden factor. To avoid the interpretation of noise as nonlinearity, we determine p-values that measure the probability of a linear gene being detected as nonlinear by chance.
Using this algorithm, we detect nonlinear interactions of genes involved in spindle organization based on microarray gene expression data. We found that TTK and ZWINT might have a strong nonlinear dependency to the underlying factor driving spindle organization.