Inferring gene regulatory networks from single cell expression data

Recent advances in cell profiling technology, such as RNA-sequencing and real-time PCR (polymerase chain reaction), have provided researchers with the ability to obtain expression data of a large set of genes at single cell resolution. These revolutionary tools produce single cell data, which we could use to answer important scientific questions that are previously not possible using population-averaged measurements (Sandberg 2013). For example, by looking at single cell expression data, we could address the functional role of cell-to-cell variability arising from gene expression stochastic dynamics, in cell lineage decision-making during physiological differentiation process. However, new computational tools are also needed to take advantage of information contained in single cell data, which existing algorithms were not originally designed for. In this work, we focused on the inference of gene regulatory network (GRN) from single cell expression data.