A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain.

ABSTRACT: Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables [e.g., genes or proteins (p)] for each sample. This leads to a data structure that is high dimensional (p ≫ n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p ≫

SUBMITTER: Balsor JL 

PROVIDER: S-EPMC8636820 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature