Associating Transcription Factors to Single-Cell Trajectories with DREAMIT

Abstract

AbstractTrajectory methods have enabled the organization of cells into contiguous cellular changes from their transcriptional profiles measured by single cell RNA sequencing. Few methods enable investigating the implied gene regulatory network dynamics from the cell transitions between and along trajectory branches. In particular, there remains an opportunity to develop methods that leverage the predicted “pseudotime” orderings of cells to reveal transcription factor (TF) dynamics. Here we present DREAMIT (DynamicRegulation ofExpressionAcrossModules inInferredTrajectories), a novel framework developed to detect patterns of TF activity along single-cell trajectory branches. It detects significant TF-target associations using a relational enrichment approach. Using a benchmark representing several different tissues, the method was found to have increased tissue-specific sensitivity and specificity over competing approaches. To illustrate the utility of the approach, we apply it to the analysis of a peripheral blood mononucleocyte dataset and discuss several examples of TF networks associated with monocytes and erythrocytes that reveal potential causal relationships among TFs. In summary, DREAMIT provides a useful tool for uncovering potential TF-to-target gene regulatory mechanisms associated with the cell-to-cell transitions predicted by trajectory inference methods.