Multimodal Dimension Reduction and Subtype Classification of Head and Neck Squamous Cell Tumors.

ABSTRACT: Traditional analysis of genomic data from bulk sequencing experiments seek to group and compare sample cohorts into biologically meaningful groups. To accomplish this task, large scale databases of patient-derived samples, like that of TCGA, have been established, giving the ability to interrogate multiple data modalities per tumor. We have developed a computational strategy employing multimodal integration paired with spectral clustering and modern dimension reduction techniques such as PHATE to provide a more robust method for cancer sub-type classification. Using this integrated approach, we have examined 514 Head and Neck Squamous Carcinoma (HNSC) tumor samples from TCGA across gene-expression, DNA-methylation, and microbiome data modalities. We show that these approaches, primarily de

SUBMITTER: Bard JE 

PROVIDER: S-EPMC9326399 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature