Extended Data Fig. 6: Drug screen quality control.

(a) Sixteen bubble plots display variability of the screening data across replicates. Plotted are the mean (size) and standard error of the mean (color) for each drug concentration (x-axis) and drug (y-axis) pair using day-0 normalized values. Lighter colors indicate the most variable measures. (b, left) Correlation plot showing mean GI50 scores versus GR50 scores as different data normalization techniques for sample-to-sample comparisons. Each point displays the mean log-transformed GR50 and mean log-transformed GI50 values across biological replicates (all replicates required GR50 and GI50 estimates) with extending whiskers (1 standard error) for drug-sample pairs. (b, mid left) Estimated growth rates from the screening data are shown as mean values (slower to faster growing models from left to right). Error bars indicate + /- s.e.m. Models are grouped into faster and slower growers and compared in panel d. Point indicates the estimated double rate of each biological replicate using log2 ratio of endpoint of DMSO treated organoids and day zero measurements. Number of samples is indicated on x-axis labels. (b, mid right) Density figure displays the residuals, i.e., the biggest differences from a perfect correlation. Shaded in light blue are the data with the biggest discrepancies between the two drug response metrics. The shaded area includes 32 drug-sample pairs that are analyzed in panel d. (b, right) Using the most discrepant samples from panel b, we display where GR50 or GI50 disproportionally inflate potency metrics in faster and slower growers. Here we show that GR50 and GI50 metrics do not inflate or deflate drug responses based on variable growth rates in these models (16 drug-sample pairs in faster, and 16 drug sample pairs in slower group). Stacked bars are colored by the direction of discrepancy, i.e., GR50 scores less than GI50 scores (teal) or GI50 scores were less than GR50 scores (red).