Optimal transport analysis of single-cell transcriptomics directs hypotheses prioritization and validation

Abstract

The explosive growth of regulatory hypotheses from single-cell datasets demands accurate prioritization of hypotheses for in vivo validation. However, current computational methods emphasize overall accuracy in regulatory network reconstruction rather than prioritizing a limited set of causal transcription factors (TFs) that can be feasibly tested. We developed Haystack, a hybrid computational-biological algorithm that combines active learning and the concept of optimal transport theory to nominate and validate high-confidence causal hypotheses. Our novel approach efficiently identifies and prioritizes transient but causally-active TFs in cell lineages. We applied Haystack to single-cell observations, guiding efficient and cost-effective in vivo validations that reveal causal mechanisms of cell differentiation in Drosophila gut and blood lineages. Notably, all the TFs shortlisted for the final, imaging-based assays were validated as drivers of differentiation. Haystacks hypothesis-prioritization approach will be crucial for validating concrete discoveries from the increasingly vast collection of low-confidence hypotheses from single-cell transcriptomics.