Unsupervised analysis of whole transcriptome data from human pluripotent stem cells cardiac differentiation.

ABSTRACT: The main objective of the present work was to highlight differences and similarities in gene expression patterns between different pluripotent stem cell cardiac differentiation protocols, using a workflow based on unsupervised machine learning algorithms to analyse the transcriptome of cells cultured as a 2D monolayer or as 3D aggregates. This unsupervised approach effectively allowed to portray the transcriptomic changes that occurred throughout the differentiation processes, with a visual representation of the entire transcriptome. The results allowed to corroborate previously reported data and also to unveil new gene expression patterns. In particular, it was possible to identify a correlation between low cardiomyocyte differentiation efficiencies and the early expression of a set of no

SUBMITTER: P Agostinho S 

PROVIDER: S-EPMC10850331 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature