Extended Data Fig. 7: Evaluation of the effects of MOA compounds on IFNβ responses in PBMCs.
From: Scalable, compressed phenotypic screening using pooled perturbations
a. Line plots of the number of detected positive/negative IFNβ response potentiators and interferers per cell type as a function of the p-value cut-off used in one-sided permutation tests for the significance of regression coefficients. b. Top: Ponatinib induces multiple different effects on IFNβ response modules across cell types. Bottom: Evaluation of genes comprising IFNβ JAK-STAT related response GEPs. ‘IFNβ JAK-STAT common genes’ are the intersection of selected top genes from B cell GEP3, CD4 T cell GEP3, and CD8 T cell GEP1. ‘B cell GEP3 unique genes’ are the genes unique to the selected top genes from B cell GEP3. All scores are calculated using scanpy ‘score_genes’ function and normalized to median of DMSO + IFNβ group. Two-sided Mann-Whitney U test was performed to test score differences (Methods). All the box plots indicate 25th percentile at the bottom, median in the middle and 75th percentile at the top. Whiskers are drawn to the farthest datapoints within 1.5* interquartile range from the nearest hinge. Sample sizes in each stimulation condition: DMSO n = 198 (B cell), n = 1,730 (CD4) n = 201 (CD8); DMSO + IFNβ n = 174 (B cell), n = 1,276 (CD4), n = 87 (CD8); Ponatinib+IFNβ n = 119 (B cell), n = 1,145 (CD4), n = 66 (CD8). c. Annotation of B cell cNMF GEP modules from IFNβ + control condition. d. Annotation of CD4 T cell cNMF GEP modules from IFNβ + control condition seen in Fig. 4f. GEP3 highly correlates with JAK-STAT pathway signature. e. Annotation of CD8 T cell cNMF GEP modules from IFNβ + control condition.
