IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data
Fig 5
Constructing mouse early T-cell GRN based on automated TF selection pipeline.
(A) Overview of TF selection procedure. After selecting HVGs, IQCELL uses pySCENIC to select active regulons (TFs and their effectors), and finally IQCELL uses GDS to rank and select TFs for the final list. We have added Notch1 for its known biological importance and Rag1 and Cd3g as biological markers of DN3 stage to the list. (B) Comparison of automated vs curated TF selection show that the TF selection pipeline captures 8 of 14 genes in the curated gene list. (C) The PCA plot of the binarized scRNA-seq data color-coded with the pseudo-time values attributed to each cell. The binarization is performed by clustering the scRNA-seq expressions into expressed or not expressed levels. (D) The PCA plot of the simulated developmental trajectories are overlaid on the binarized scRNA-seq. The detected attractor is colored red and marked by star (*). The simulated data is color coded by the value of average simulation step (average distance to the attractor of simulation). (E) Expression states of the GRN model steady state attractor. Genes that are expressed (1) and not expressed (0) are represented with blue and grey squares, respectively. (F) Percentage of similarity between the attractor (vertical axis) and binarized microarray expression profiles of CLP, ETP, DN2A, DN2B, and DN3A cells (horizontal axis) [38]. The average agreement between two random states is 50%. (G) Comparison of gene perturbations between automated and curated GRNs show 17 matches of the predicted sates out of 18 perturbations.