Microarray Data Analysis and Subgroup Identification of Medulloblastoma

Abstract Microarray technology, a recent advancement in cancer research, has the potential to address disorders such as medulloblastoma. This study focused on the careful analysis of microarray data for medulloblastoma patients, aiming to identify physiologically significant subgroups through exploratory analysis. The research involved data preparation, exploration, dimensionality reduction via principal component analysis (PCA), heatmap visualization, and subgroup identification via non-negative matrix factorization (NMF). Various R packages, including RColorBrewer, rgl, limma, biobased, and NMF, were utilized for data analysis and visualization. The study explored the application of NMF to identify optimal subgroups and assessed the quality of these subgroups. The results reveal distinct molecular subgroups in medulloblastoma, contributing to a better understanding of this complex condition and potentially guiding the development of tailored treatments.