Prediction of best features in heterogeneous Lung adenocarcinoma samples using Least Absolute Shrinking and Selection Operator

Abstract

4This study aims to create a tumor heterogeneity-based model for predicting the best features of 5 lung adenocarcinoma (LUAD) in multiple cancer subtypes using the Least Absolute Shrinking 6 and Selection Operator (LASSO). The RNA-Seq raw count data of 533 LUAD samples and 59 7 normal samples were downloaded from the TCGA data portal. Based on consensus clustering 8 method samples was divided into two subtypes, and clusters were validated using silhouette 9 width. Furthermore, we estimated subtypes for the abundance of immune and non-immune 1 6