Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

ABSTRACT: Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications. However, standard classification schemas are often not suitable for scRNA-Seq due to their unique characteristics, and new algorithms are required to effectively analyze and classify samples at the single-cell level. Furthermore, existing methods for this purpose have limitations in their usability. Those reasons m

SUBMITTER: Martorell-Marugan J 

PROVIDER: S-EPMC11735047 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature