Fig. 1

MAUDE approach to scoring expression-based screens. a Method overview. b–d MAUDE approach to estimating the optimal mean expression level per guide. b Estimation of cell density distribution for each guide. Cell density distribution (y axis) of target gene expression (x axis) for each guide is modeled as a normal distribution, with mean μ_g. μ_g is optimized by calculating, for each bin (bins A and B, top), the fraction of cells expected to be in the bin under the null model (no overall change in expression; S(b, 0)) and the fraction of cells expected in the bin given the current value of μ_g (S(b, μ_g)). c Estimation of optimal μ_g. We find the optimal \( {\hat{\mu}}_g \) by calculating the log likelihood (y axis) of the observed bin read abundances given each value of μ_g (x axis). d Expected number of guide reads per bin. The fraction of reads (y axis) of guide g (P_b(g| μ_g)) expected in each bin b (x axis), for different values of μ_g (colors)