Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression such scenarios, including variable screening, model selection, order selection response categories, and selection. perform our gene expression with 801 patients, 2426 genes, five types of cancerous tumors. As result, we recommend three finalized models: one 74 genes achieves extremely low cross-entropy loss zero predictive error rate five-fold cross-validation; two other models 31 4 respectively, recommended prognostic multi-gene signatures.