Fig. 2 | Scientific Reports

Fig. 2

From: Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma

Fig. 2

Identification of cancer subtypes by immune multi-omics consensus clustering analysis. (A) The CPI and gap statistical analysis of the immune multi-omics clusters. (B) The sample similarity of each subgroup was assessed by calculating the silhoutte score. (C) Visualization of immune multi-omics consensus clustering analysis. (D) Consensus heatmap for CSs. (E) Consensus clustering matrix based on the 10 multi-omics algorithms. (F) Kaplan–Meier analysis of CSs in the TCGA cohort.

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