Optimal transport analysis of single-cell transcriptomics directs hypotheses prioritization and validation
biorxiv(2022)
摘要
The explosive growth of regulatory hypotheses from single-cell datasets demands accurate prioritization of hypotheses for in vivo validation, but current computational methods fail to shortlist a high-confidence subset that can be feasibly tested. We present Haystack, an algorithm that combines active learning and optimal transport theory to identify and prioritize causally-active transcription factors in cell lineages. We apply 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.
### Competing Interest Statement
The authors have declared no competing interest.
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