Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics
Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-37897-9 |