Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Various aspects of the disclosure show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, the disclosure provides mixture models for cell-to-cell regulatory heterogeneity which result in likelihood functions to infer model parameters. Inferences that validate both computationally and experimentally different mixture models, which include regulatory states for multicellular function occupied by as few as one in 40 cells of the population, are also encompassed. When the disclosed method extends to programs of heterogeneously coexpressed transcripts, the population-level inferences are much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. The disclosed deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.
Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles