Sparse low rank models for cellular perturbation experiments

Music City Center 

Large scale cellular perturbation experiments, including those enabled by CRISPR-based technologies, allow for high throughput single-cell transcriptomics experiments to measure cellular responses to biological perturbations. We identify several statistical challenges of these datasets including a high proportion of null effects and correlated effects across similar genes. To address these issues, we develop a sparse, low-rank modeling approach for improved estimation of cellular perturbation effects. Testing on simulated and real data, we compare to existing deep learning methods and linear regression to demonstrate the value of our linear matrix modeling approach. We also explore whether our linear approach can outperform nonlinear methods for predicting combinatorial effects.

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