Unveiling gene perturbation effects through gene regulatory networks inference from single-cell transcriptomic data
Fig 2
Selection of the best-performing mouse GRN.
A. Density plot of the Correlation Matrices Distance (CMD) values for the 250 inferred GRNs. The red dashed line represents the selected GRN with the lowest CMD. B. Pearson correlation matrix for the gene activity (GA) of the input dataset (scRNA-seq data with LogNorm, PST, and MB). C. Pearson Correlation matrix for IGNITE-generated gene activity dataset. D. Hierarchical clustering of gene activity of the input dataset. The clustering algorithm used is Ward’s method. Each row represents a gene, while each column corresponds to an individual cell. The color indicates inactive (−1, yellow) or active (+1, blue) gene activity. The dataset has 9547 cells. E. Hierarchical clustering of gene activity of IGNITE-generated data. Methodology for visualisation as in Fig 2D. 9547 cells were simulated. F. Principal Component Analysis (PCA) scatter plot representing the gene activity, GA, for the input dataset (scRNA-seq data with LogNorm, PST, and MB). Each point corresponds to a single cell, and the colour intensity reflects the pseudotime value of the cell. PC1 and PC2 indicate the two dimensions of the PCA space. G. PCA scatter plot representing the IGNITE-generated wild-type gene activity data, WT GA, using the same dimensional reduction approach as in Fig 2F.