Exploring transcription modalities from bimodal, single-cell RNA sequencing data
Enikő Regényi, Mir-Farzin Mashreghi, Christof Schütte, Vikram Sunkara · 2024
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Description
Enikő Regényi and co-authors find hidden information in the shape of gene expression data that standard analysis throws away. Bimodal single-cell sequencing records each gene's RNA in two forms, spliced and unspliced, and most studies use the pair only to compute velocities. The team parametrizes the two-dimensional cloud as an ellipse and derives statistics from its shape.
Four modalities emerge, readable as changes in the rates of splicing, transcription, or degradation. On cell cycle and colorectal cancer data, the shape statistics flagged genes that differential expression analysis missed entirely, yet those genes visibly separated the phenotypes. The authors argue the modality search widens the hunting ground for biomarkers and lets RNA processing itself enter regulatory models.
Catalog record
| CREATOR | Enikő Regényi, Mir-Farzin Mashreghi, Christof Schütte, Vikram Sunkara |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |