Sparse representation learning derives biological features with explicit gene weights from the Allen Mouse Brain Atlas.

ABSTRACT: Unsupervised learning methods are commonly used to detect features within transcriptomic data and ultimately derive meaningful representations of biology. Contributions of individual genes to any feature however becomes convolved with each learning step, requiring follow up analysis and validation to understand what biology might be represented by a cluster on a low dimensional plot. We sought learning methods that could preserve the gene information of detected features, using the spatial transcriptomic data and anatomical labels of the Allen Mouse Brain Atlas as a test dataset with verifiable ground truth. We established metrics for accurate representation of molecular anatomy to find sparse learning approaches were uniquely capable of generating anatomical representations and gene weigh

SUBMITTER: Abbasi M 

PROVIDER: S-EPMC9987823 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature