Omada: robust clustering of transcriptomes through multiple testing.

ABSTRACT:

Background

Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more access

SUBMITTER: Kariotis S 

PROVIDER: S-EPMC11238428 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature