While single-cell multimodal datasets allow for the measurement of individual cells to understand cellular and molecular mechanisms, generating multimodal data for many cells is costly and challenging. Cohen Kalafut and colleagues develop a machine learning model capable of imputing single-cell modalities and prioritizing multimodal features, such as gene expression, chromatin accessibility and electrophysiology.
- Noah Cohen Kalafut
- Xiang Huang
- Daifeng Wang
