The level of gene expression is known to vary from cell to cell and even in the same cell over time. This variability provides cells with the ability to mitigate environmental stresses and genetic perturbations, and facilitates gene expression evolution. Recently, many valuable gene expression noise data measured at the single-cell level and gene expression variation measured for cell populations have become available. In this chapter, we show how to perform integrative analysis using these data. Specifically, we introduce how to apply a machine learning technique (support vector regression) to explore the relationship between gene expression variations and stochastic noise.
Keywords: Feature selection; Gene expression variation; Intrinsic noise; Machine learning; Single-cell; Support vector regression.