Laboratory Data Pattern Approximation

To utilize the simulation framework in approximating actual laboratory data pattern, dkosim_lab was designed to include three initialized gene class. You may specify the percentage of essential genes by pt_neg, unknown by pt_unknown, and non-targeting controls by pt_ctrl accordingly. In our designed framework, essential genes is defined by negative genes; unknown genes might compose of genes with theoretical phenotype in either negative, positive, wild-type, or non-targeting control gene class.

Abbreviations

KO, knockout; SKO, single knockout; DKO, double knockout; %, percentage; GI, genetic interaction; std. dev., standard deviation.

Default Lab Mode

By default, dkosim_lab run simulation with following parameter:

Initialized Library Parameters

coverage: 100
n_guide_g: 3
moi: 0.3
sd_freq0: 1/2.56 (chosen by setting a 10-fold difference between 90th and 10th percentiles of SKO counts distribution)

GI Parameters

p_gi : 0.03
sd_gi : 1.5

Gene Class Parameters

% of theoretical phenotype to each gene class

pt_neg: 0.15

pt_unknown: 0.80

pt_ctrl: 0.05

Mean and std. dev. of theoretical phenotype

mu_neg: -0.03

sd_neg: 0.25

sd_unknown: 0.25

Guide Parameters

High-efficacy guides proportion and CRISPR mode

p_high : 0.75

mode: CRISPRn

Mean and std. dev. of guide-efficacy

mu_high: 0.9

sd_high: 0.1

mu_low: 0.05

sd_low: 0.07

Cell Doublings Parameters

size.bottleneck: 2

n.bottlenecks: 1

n.iterations: 30

Randomization Parameter

rseed: NULL

Miscellaneous

path: current working directory

cores_free: 1

An example running code is as follows:

dkosim_lab(sample_name="test_lab", n=20)

Customized Lab Mode

Alternatively, you may adjust values to any tunable parameters as desired, or using the parameters described in the full article to approximate actual lab data pattern in double CRISPR screens. For example, use the following code and parameters to simulate Fong-2024[1] laboratory screens:

dkosim_lab(sample_name="sim_fong2024",
           n = 246,
           coverage=1000,
           n_guide_g=3,
           moi = 0.3,
           sd_freq0 = 1/2.56,
           p_gi=0.03,
           sd_gi=1.5,
           pt_neg=64/246,
           pt_unknown=178/246,
           pt_ctrl=4/246,
           mu_neg=-0.03,
           sd_neg=0.25,
           sd_unknown=0.2,
           p_high = 0.75,
           mode="CRISPRn",
           mu_high=0.9,
           sd_high=0.1,
           mu_low=0.05,
           sd_low=0.07,
           size.bottleneck = 2,
           n.bottlenecks= 1,
           n.iterations = 30)

Check more details in the full article and example output in the pre-built DKOsimR vignettes (PDF) Section 5.

References

[1] Fong, S.H., Kuenzi, B.M., Mattson, N.M. et al. A multilineage screen identifies actionable synthetic lethal interactions in human cancers. Nat Genet 57, 154–164 (2025). https://doi.org/10.1038/s41588-024-01971-9.