Abstract
Drug treatment induces cell type specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type. Training the method on a large collection of transcriptional drug perturbation profiles, prediction accuracy improves significantly over baseline and alternative deep learning approaches when applying the method to two cell types, with improved accuracy when generalizing the framework to additional cell types. Treatments with drugs or whole drug families not seen during training are predicted with similar accuracy, and the same framework can be used for predicting the results from other interventions, such as gene knock-downs. Finally, analysis of the trained model shows that the internal representation is able to learn regulatory relationships between genes in a fully data-driven manner.
Highlights
The transcriptional response to drug treatment is cell type specific, with some drugs eliciting similar effects across lineages and others evoking a range of responses depending on the cell type [1,2]
Motivated by the need for accurate methods for prediction of cell type specific drug responses, we developed DeepCellState, a deep learning framework, with the goal of predicting the response in a given cell type based on the response in another cell type
Training the method on the largest available database for transcriptional response to drug perturbations, LINCS, we observed that the method can predict with high accuracy the cell type specific response for treatment with drugs not seen by the method, with improved accuracy as we generalized the method from two to multiple cell types
Summary
The transcriptional response to drug treatment is cell type specific, with some drugs eliciting similar effects across lineages and others evoking a range of responses depending on the cell type [1,2]. Only important features are captured by the model Combining this property together with the addition of noise to the input allows for the construction of denoising autoencoders to build robust models from high-dimensional data. A number of recent studies have successfully applied autoencoders to biological problems, where deep autoencoders were used to denoise single cell RNA-seq data sets [8,9], analyze [10,11] and predict [12,13] gene expression, and to study the transcriptomic machinery [14]. Autoencoders have been applied to perturbation response modeling as well, focusing on single cell data [15], where for each perturbation, a large number of expression profiles are available with relatively low variance within sets of profiles from the same perturbation
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