Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data
Fig 2
Pipeline for the analysis of single cell gene expression data.
This paper develops several new tools for single cell analysis. (A) The kinetic parameters are estimated using a large look-up table. The likelihoods for each possible parameter set are found by multiplying the experimental data by the lookup table, and then the maximum likelihood is identified. (B) The SABEC algorithm is used to identify clusters of cells with uniform bursting kinetics– this iterative algorithm alternates between assigning cells to clusters and estimating bursting kinetic parameters for each cluster. This clustering algorithm is run 50 times and the results are summarized in a consensus matrix, which represents the frequency of each pair of cells being found in the same cluster. (C) Finally, the EPiK tool identifies the most likely set of parameters to have varied for a gene across two different cell populations, using a combination of the Bayesian Information Criterion (BIC), Marginal Probability (MP), and a Subsampling-based method, as described in (C). BIC calculates the likelihood for each possible set of parameters, penalised by the number of free parameters. The MP score calculates the likelihood of each parameter changing, independent of the behaviour of the other parameters. In the subsampling method, random sets of cells are selected, the kinetic parameters estimated for each population of cells; then, the distributions of estimated kinetic parameters are compared. (D) These new tools are combined to form a pipeline for analyzing single cell qPCR data. The details for each step of this pipeline are described in the Methods.