CRISPR: Knockout

Corrected Fold Changes

Gene independent responses (e.g. copy number) to CRISPR-Cas9 were corrected using CRISPRcleanR.

Independent datasets generated at Sanger and Broad showed batch effects due to differences in library design and assay length (Dempster et al. Nat.Commun 2019). To allow combined analysis of these two datasets they were batch corrected to account for these technical differences (Pacini et al. Nat Commun 2021). Batch correction was calculated using ComBat using a set of overlapping cell lines screened at both institutes. Where overlapping cell lines were present the data from Sanger were preferentially selected for inclusion in the final combined datasets. The batch corrected data was then quantile normalised to adjust for differences in screen quality. Finally the first principal component is removed from the data that associates with differences in media.

Defining Fitness Genes

Loss of fitness scores are generated from corrected FCs for both individual Sanger and Broad datasets and the combined CRISPR-Cas9 dataset using BAGEL2 (Kim et al. Genome Med 2021) to call significantly depleted genes. The gene-level Bayesian factors (BFs) are calculated as the average of the sgRNAs on a targeted-gene basis. As input to BAGEL2 we used reference sets of predefined essential and non-essential genes further curated to exclude high-confidence cancer driver genes. A statistical significance threshold for gene-level BFs is determined for each cell line. Each gene is assigned a scaled BF computed by subtracting the BF at the 5% FDR threshold (obtained from classifying reference essential/non-essential genes based on BF rankings) defined for each cell line from the original BF. For consistency of visualisation, all scaled BF values are multiplied by -1 resulting in significantly depleted values having a loss of fitness score < 0. Archived Project Score v1 data was processed using an implementation of BAGEL in R as described (Behan F, et al. Nature. 2019).

Gene Fitness Metrics

• Fitness Score: based on scaled BF from BAGEL. A score < 0 indicates a statistically significant effect on cell fitness.
• Corrected fold change: Copy-number-bias corrected gene depletion fold change, computed between average representation of targeting sgRNAs post-transfection at the end of the experiment versus plasmid library.

Core Fitness Genes

Fitness genes common to the majority of cell lines tested, or common within a cancer type, may be involved in cell essential processes - we refer to these as core fitness genes. In order to identify core fitness genes, we developed a statistical method, ADaM (Adaptive Daisy Model) which adaptively determines the minimum number of dependent cell models required for a gene to be classified as a core fitness gene. ADaM was implemented at both a cancer-type specific level and a pan-cancer specific level (code publicly available at https://github.com/francescojm/ADAM).