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. 2020 Aug 19;48(17):e102. doi: 10.1093/nar/gkaa679

Figure 3.

Figure 3.

Comparison of accuracy and gene category assignment between reference and new essentiality estimate models. The methods used are labeled on the left (GMM, Gaussian Mixture Model; BGMM, Bayesian Gaussian Mixture Model; and HMM, Hidden Markov Model). (A) left panel, Accuracy (purple) and NE accuracy (light blue) in percentage values for each method per passage. center panel, Number of genes classified as E (purple), F (blue), and NE (light blue). Error bars represent the standard deviation (n = 2). right panel, Number of genes classified E (purple) and NE (blue), with F and NE genes grouped together. Error bars represent the standard deviation (n = 2). (B) An example of an essentiality estimate using the Gaussian Mixture Model (GMM) with three components for P01, replica 1 (replica 2 in Supplementary Figure S4). The gene linear density (grey histogram) has been properly fitted to the data using three Gaussian distributions (dashed lines: E (purple), F (blue) and NE (light blue)). (C) Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The lower the AIC and BIC values, the better the balance between goodness-of-fit and model simplicity. The number of components (i.e. 3, blue shadowing) represents the elbow of the line where there is a good trade-off between fitting and the number of parameters.

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