Single-cell RNA sequencing (scRNA-seq) provides an instantaneous snapshot of the transcriptional state of a cell, which results from the simultaneous activity of many cellular processes. In this issue of Cell Genomics, Chen et al.1 describe the development of CellUntangler, a deep-learning-based model that allows the capture and filtering of multiple biological signals in scRNA-seq data.
Journal article
2026-03-11T00:00:00+00:00
6
Deep Learning, Single-Cell Analysis, Humans, Sequence Analysis, RNA