Fig. 1

Improvement of BAGEL algorithm. a A brief flow diagram of CRISPR pooled library screen analysis using BAGEL pipeline. b Improvements in the model selection algorithm. The red and the blue curves indicate kernel density plots of fold changes of reference core-essential and non-essential genes, respectively. The gray curve indicates the ratio (difference in logs) of core-essential density to non-essential density at the point of fold change. Since there are few data points in the marginal area, BAGEL limited calculation area of fold change between the point that blue curve hits the density threshold (2⁻⁷ was used in BAGEL) as a lower bound and the first local minimum ratio as an upper bound (red dashed lines). In BAGEL2, we employed linear regression to interpolate marginal area outside this region (black line). c Comparison of gene essentiality (Bayes Factor) between BAGEL and BAGEL2 using RPE-1 cell line screened by TKOv3. Known tumor suppressors (NF2, KIRREL, and KEAP1) that are scored BF ~ − 20 with hundreds of other genes in BAGEL were measured as much lower Bayes Factor and distinguished clearly from others in BAGEL2. d Dynamic range of BAGEL2 results were increased from BAGEL across screens in the Avana dataset. e Jaccard index between predicted essential gene sets in Avana by 10-fold cross-validation and bootstrapping. f Pearson correlation coefficient of essentiality across 517 cell lines in Avana data between frequently amplified genes near ERBB2 on chromosome 17. After CRISPRcleanR is applied, essentiality correlation due to copy number amplification effect was successfully corrected. g Prediction performance benchmark between BAGEL, BAGEL2 applied linear interpolation and 10-fold cross-validation (BAGEL2 Raw), and BAGEL2 + CRISPRcleanR applied version (BAGEL2 CCR applied)