Back

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

Singh, R.; Li, J. S. S.; Tattikota, S. G.; Liu, Y.; Xu, J.; Hu, Y.; Perrimon, N.; Berger, B.

2022-06-30 bioinformatics
10.1101/2022.06.27.497786 bioRxiv
Show 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.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.