Fig. 1

a, Simulation of cell-state transitions in response to TF perturbation. First, CellOracle constructs custom transcriptional GRNs using scRNA-seq and scATAC-seq data (left). Accessible promoter and enhancer peaks from scATAC-seq data are then combined with scRNA-seq data to generate cluster-specific GRN models (middle). CellOracle simulates the change in cell state in response to a TF perturbation, projecting the results onto the cell trajectory map (right). b, Force-directed graph of 2,730 myeloid progenitor cells from Paul et al.¹⁶. Twenty-four cell clusters (Louvain clustering) were organized into six main cell types. Mk, megakaryocytes. c, Differentiation vectors for each cell projected onto the force-directed graph. d, CellOracle simulation of cell-state transition in Spi1 KO simulation. Summarized cell-state transition vectors projected onto the force-directed graph. Vectors for each cell are shown in the inset. e, Spi1 KO simulation vector field with perturbation scores (PSs). f, Gata1 KO simulation with perturbation scores. g, Schematic of Spi1–Gata1 lineage switching. MPP, multipotent progenitor. h, Detail of Gata1 simulation for the granulocyte branch. Left, cell-state transition vectors for each cell. Right, summarized vectors. i, Systematic KO simulation result of 90 TFs in the GM and ME lineage is summarized as a scatter plot of the sum of negative perturbation scores (shown in log scale). Dashed lines represent cut-off values corresponding to false-positive rate (FPR) = 0.01. Genes are classified into four categories on the basis of their previously reported functions (Supplementary Table 2). The asterisk refers to Supplementary Fig. 11, where we expand on the predicted phenotype. All scores can be explored through our web application (https://celloracle.org).