Hello, I have a question about the CERES score. If a gene has CERES score -1 in cell line A, and -0.2 in cell line B, does it mean B proliferate 5 times (or similar range) faster than A in the setting of CRISPR gene of interest? Thanks.
CERES scores don’t have a simple relationship to proliferation rates. Chronos scores are based on estimates of proliferation rates, but these are always relative to the cell line’s unperturbed growth rate, so the relationship will be more complicated than you’re describing. Comparing the growth rate of two different cell lines with the same knockout isn’t usually very informative, because cell lines can grow at very different rates even without a genetic perturbation.
Thank you for the clarification.
My understanding is that the CERES score represents the growth inhibition effect by CRISPR for individual cell line, which is normalized to the unperturbed growth rate itself. With that said, is there any relationship between CERES score and growth inhibition effect. i.e. -1 indicate the growth inhibition (compared to unperturbed cell line A) for one cell line A, is more dramatic than a cell line B with score -0.2 (compared to unperturbed cell line B)? Thank you.
Yes, it is definitely the case that more negative values indicate stronger growth inhibition.
Thank you for the helpful insights.
Hi, following your comments. Am I right to assume that the smaller the CHRONOS score is, the more dependent the cells are to that particular gene?
Thanks!
CERES and Chronos scores have similar interpretations. For both, we provide two kinds of estimates:
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Gene effect: the direct estimate of the effect of gene knockout on viability. A more negative score means more depletion. Appropriate for finding dependency relationships.
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Gene dependency (annotated “PR” in portal tools): the probability that gene effect represents a true loss of viability. A score close to 1 means stronger dependency. Appropriate for calling hits in individual cell lines.
Thanks Joshua for your input. In such case, would gene dependency data be more robust to call for essential genes in the cell lines since this is the next layer of validation for the gene effect? In other words, the latter (gene dependency) would compute for how likely true it is for gene KO to deplete cell viability (gene effect). Please correct me if I am wrong. Thanks!
That’s the approach I would recommend.
