Inference of Genetic Networks from Steady-State and Pseudo Time-Series of Single-Cell Gene Expression Data using Modified Random Forests

A number of the genetic network inference methods have been proposed. These methods have been basically designed to analyze gene expression data of bulk cells. Recently, on the other hand, researchers have been capable of using gene expression data measured at single-cell resolution. The existing inference methods are however incapable of analyzing time-series of single-cell data because of high cell-to-cell variation in gene expression. This study therefore proposed the new inference method that has an ability to analyze steady-state and pseudo time-series of single-cell gene expression data. The pseudo time-series data are obtained through the pseudo-temporal ordering analysis. As the precise information about the measurement time is unavailable in pseudo time-series data, our method infers a genetic network using the signs of time derivatives of gene expression levels, that can be estimated from the given data. Through the numerical experiments, we finally confirmed the effectiveness of the proposed method.