Figure 2.
Novel genomic clusters (GCs) of AML identified by unsupervised analyses. (A) Consensus matrix generated by applying latent class analysis on 1000 subsamples representing the frequency of 2 observations being clustered in the same group. (B) Kaplan-Meier analysis showing the overall survival (OS; in months) of each GC (GC-1 to GC-4). (C) Pie charts showing the percentage of cases belonging to each GC (GC-1 to GC-4) in pAML (left) and sAML (right). (D) Bar graph showing the frequency of pAML and sAML in the GCs after normalizing the samples by bootstrapping. (E) Hyperparameter selection plot for RF modeling; cross-validation accuracy (CVA) is shown on the y-axis. CVA saturation in this plot indicates that 3 variables suffice to achieve the maximal accuracy of ∼0.97, (ie, this model correctly assigns prognosis for ∼97% of AML cases in our cohort using their corresponding genomic features).

Novel genomic clusters (GCs) of AML identified by unsupervised analyses. (A) Consensus matrix generated by applying latent class analysis on 1000 subsamples representing the frequency of 2 observations being clustered in the same group. (B) Kaplan-Meier analysis showing the overall survival (OS; in months) of each GC (GC-1 to GC-4). (C) Pie charts showing the percentage of cases belonging to each GC (GC-1 to GC-4) in pAML (left) and sAML (right). (D) Bar graph showing the frequency of pAML and sAML in the GCs after normalizing the samples by bootstrapping. (E) Hyperparameter selection plot for RF modeling; cross-validation accuracy (CVA) is shown on the y-axis. CVA saturation in this plot indicates that 3 variables suffice to achieve the maximal accuracy of ∼0.97, (ie, this model correctly assigns prognosis for ∼97% of AML cases in our cohort using their corresponding genomic features).

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