Fig. 1: Method overview.
a The input of MINIE consists of time-series bulk metabolomics and scRNA-seq data. Note that time-series scRNA-seq data can be interpreted as time-dependent distributions of gene expression across measured cells, sampled at specific time points. b MINIE's algorithm is divided into two steps: transcriptome–metabolome mapping inference (top) and network inference using Gaussian regression (bottom). Step 1: Bulk metabolomic and transcriptomic data (obtained by averaging the scRNA-seq data across cells) are used as inputs in this step. The diagram illustrates the sparse regression problem, which consists of determining the coefficients of Θ from the metabolite concentrations \({\boldsymbol{m}}(t)\) and bulk gene expressions \({\boldsymbol{g}}(t)\). These coefficients enable the inference of the transcriptome–metabolome mapping Γ, which is further used to estimate the metabolomic trajectories at the single-cell level. Step 2: Three MCMC samplers are employed iteratively to estimate the parameters of the underlying regulatory model, including the network topology. First, the pseudotime values are estimated from the scRNA-seq data, assigning each sampled cell a unique time point reflecting its progression. Second, the network topology is sampled (by randomly adding/deleting edges) together with other model parameters. Third, the gene trajectories are sampled from the Bayesian model, fitting the data. c The final output is a confidence matrix with the probability of existence for all regulatory interactions in the transcriptome (i.e., gene-gene and metabolite-to-gene links).
